Search is not available for this dataset
article
stringlengths 4.36k
149k
| summary
stringlengths 32
3.35k
| section_headings
listlengths 1
91
| keywords
listlengths 0
141
| year
stringclasses 13
values | title
stringlengths 20
281
|
|---|---|---|---|---|---|
The ability of pathogens to escape the host's immune response is crucial for the establishment of persistent infections and can influence virulence . Recombination has been observed to contribute to this process by generating novel genetic variants . Although distinctive recombination patterns have been described in many viral pathogens , little is known about the influence of biases in the recombination process itself relative to selective forces acting on newly formed recombinants . Understanding these influences is important for determining how recombination contributes to pathogen genome and proteome evolution . Most previous research on recombination-driven protein evolution has focused on relatively simple proteins , usually in the context of directed evolution experiments . Here , we study recombination in the envelope gene of HIV-1 between primary isolates belonging to subtypes that recombine naturally in the HIV/AIDS pandemic . By characterizing the early steps in the generation of recombinants , we provide novel insights into the evolutionary forces that shape recombination patterns within viral populations . Specifically , we show that the combined effects of mechanistic processes that determine the locations of recombination breakpoints across the HIV-1 envelope gene , and purifying selection acting against dysfunctional recombinants , can explain almost the entire distribution of breakpoints found within this gene in nature . These constraints account for the surprising paucity of recombination breakpoints found in infected individuals within this highly variable gene . Thus , the apparent randomness of HIV evolution via recombination may in fact be relatively more predictable than anticipated . In addition , the dominance of purifying selection in localized areas of the HIV genome defines regions where functional constraints on recombinants appear particularly strong , pointing to vulnerable aspects of HIV biology .
Pathogens , and viruses in particular , are subject to strong selective pressures during infection and often have characteristically high degrees of genetic variation [1] . Recombination is an important evolutionary mechanism that contributes to this genetic diversification . By creating novel combinations of pre-existing genetic polymorphisms in a single replication cycle , recombination enables greater movements through sequence space than can be achieved by individual point mutations . As a consequence , recombination provides access to evolutionary “shortcuts” . In addition , since recombination generally involves genes that already encode functional products , the probability of producing viable progeny is higher compared to the insertion of an equivalent number of random point mutations [2] . However , the generation of recombinant forms is not an unconstrained process . Genes and genomes generally evolve through the slow accumulation of point mutations , which often requires the progressive insertion of compensatory mutations at “linked” sites . This coevolution permits the preservation of epistatic interactions . By simultaneously introducing several substitutions , recombination has the potential to substantially perturb such coevolved intra-genome interaction networks [2] , [3] , impairing the functionality of the genes involved . Thus , the balance between the advantages of taking evolutionary shortcuts and the risk of chimeras being dysfunctional [2] determines the role played by recombination in the evolution of a given gene or organism . Several studies have focused on the impact of recombination on the evolution of proteins , particularly in relation to directed evolution experiments [4] , [5] . Two major factors have a large influence on the functionality of recombinants proteins . The first is the position of recombination breakpoint ( the region where the sequence shifts from that of one parental sequence to the other ) relative to the location of genetic polymorphisms within the gene . Recombinants involving a large number of non-synonymous substitutions will in fact have a low probability of being functional [2] . The second factor is the position of the breakpoints in relation to the boundaries of discrete protein folds . Breakpoints near the boundaries of these domains will in general have a smaller impact on protein folding , and hence protein function , than breakpoints occurring within them [3] , [6] , [7] . Recent work on Begomoviruses corroborated these findings by demonstrating that recombination events found in natural viral populations are significantly less disruptive of protein folding than randomly generated recombinants [8] . Adaptation of pathogens , either to on-going immune pressures within individual hosts or following transmission to new hosts of the same or different species , can result in infectious outbreaks that constitute major threats for public health [9]–[12] . The human immunodeficiency virus ( HIV ) is an extremely recombinogenic pathogen in which recombination has been implicated in key aspects of viral pathogenesis such as immune evasion [13] , transmissibility [14] , the evolution of antiretroviral resistance [15] , [16] and cross-species transmission [9] , [12] . Indeed , the remarkable genetic flexibility of HIV is underlined by its large genetic diversity . The HIV-1 population is subdivided into three groups , named M , N and O , with group M ( which is responsible for the vast majority of the infections worldwide ) being further subdivided into nine subtypes ( named A , B , C , D , F , G , H , J and K ) [17] . Although recombination in HIV has been shown to occur at all phylogenetic levels ( intra- and inter-subtype , as well as inter-group , reviewed in reference [18] ) , the most widely noted impact of recombination on the genetic diversification of this virus is the frequent natural occurrence of inter-subtype recombinants in parts of the world where multiple subtypes co-circulate [19]–[22] . When the same inter-subtype recombinant is transmitted between multiple individuals , and has therefore the potential to be of epidemiological significance , it is termed a Circulating Recombinant Form ( CRF ) [17] . As with the HIV-1 subtypes , CRFs form distinct clusters in phylogenetic trees and some of them contribute substantially to the pandemic . Sufficient inter-subtype recombinant sequences have been sampled to permit the detailed characterisation of variation in the locations of breakpoints both within individual genes [22] , [23] , and entire genomes [24] , [25] , [32] . This makes HIV a particularly useful model for studying the forces that shape pathogen populations within the context of global epidemics . Here we focus on recombination within the envelope gene ( env ) . This gene encodes two polypeptides ( gp120 and gp41 ) that form a heterodimer at the surface of the viral particle . Trimers of these heterodimers are the functional units that are responsible for binding to the cellular receptors and co-receptors and ultimately lead to viral entry into target cells [26] . The two protein products of env are also the targets of all the neutralising antibodies identified to date [27] . By using a tissue culture system to characterise inter-subtype recombinants generated within env in the absence of selection , and assaying the functionality of recombinant genes , we produce an empirical model of HIV recombination that accurately describes recombination patterns found in viruses sampled throughout the HIV pandemic .
We used different combinations of env sequences from primary HIV-1 isolates belonging to either different group M subtypes or group O ( see Materials and Methods for the list of parental isolates used ) to determine the distribution of breakpoints occurring within the HIV env gene in the absence of selection . We chose combinations of isolates belonging to subtypes that are co-circulating in regions of the world from which natural inter-subtype recombinant forms have emerged [28] . In order to quantify variations in recombination rates across env we used a previously described experimental system where human T cells are transduced with HIV-1 replication-defective vectors pseudotyped with the Vesicular Stomatitis Virus ( VSV ) envelope [29] . As this system mimics a single cycle of viral infection in which reverse transcription products neither influence cellular survival , nor confer a specific phenotype to the transduced cells , recombinants that were produced during reverse transcription were not subjected to any selection . After cloning of the reverse transcription products in E . coli , the system enabled identification of the recombinants based on the presence of a lacZ reporter gene ( Figure 1 ) . Given that known input sequences were used , such an approach enables the accurate and unambiguous localization of the breakpoint position to precise regions bounded by nucleotides that differ between the two parental sequences . The regions of the envelope gene that were studied were chosen so as to obtain 700 to 1 , 500 nucleotides overlapping windows , spanning the whole of env . For each of seventeen different combinations of parental sequence pairs ( Figure 2A ) , a recombination rate per nucleotide and per reverse transcription run was calculated within a 50 nucleotides sliding window ( with 10 nucleotides step size ) . These were plotted as a function of the location of the window along the gene . To evaluate whether recombination-prone regions exist within the population , data from the 17 different pairs of parental sequences were pooled and an average recombination rate was computed for the different regions , and plotted as function of the position along the env gene ( Figure 2B , top panel ) . Peaks and troughs were apparent all along the gene , with regions refractory to recombination being more common in the gp120 coding portion than in the gp41 region . The probability that breakpoints were more or less clustered across env than could be accounted for by chance ( given the null hypothesis that breakpoint positions occur randomly ) was determined by a permutation test ( Figure 2B , bottom panel ) . Six major recombination-prone or “hot” regions ( shaded light blue areas in Figure 2B ) could be defined as env regions where breakpoint clusters were bounded by statistically significant breakpoint “cold spot” ( p<0 . 05 ) . Each of the six identified breakpoint clusters contained at least one breakpoint cluster that constituted a statistically significant recombination “hot-spot” ( p<0 . 01 ) . While these recombination-prone regions covered only slightly more than half of the whole gene ( 55 . 3% ) , they included 81 . 6% of all the breakpoints ( 337/413 ) mapped . These six hot regions are areas where recombination occurs preferentially during HIV replication , irrespective of the parental strains involved . We next investigated the fate of these recombinants with respect to their establishment in the natural HIV-1 population . The fixation of a recombinant gene within a population is dependent on the interplay of multiple factors . Nevertheless , an obligatory component of evolution is undoubtedly the elimination by purifying selection of viruses that express dysfunctional proteins . To evaluate how profoundly this aspect of natural selection might influence the pattern of breakpoints generated by the mechanism of recombination , we determined the relative functionality of a subset of recombinant env genes . In addition to encoding the proteins that coat the viral membrane , env also encodes a well-known functional RNA structure , the Rev responsive element ( RRE ) . For the recombinants containing breakpoints in the RRE region the functionality of this RNA module was therefore also tested . Being involved in the regulation of the timing and the balance among the various forms of unspliced and partially or completely spliced RNAs , RRE is essential for viral replication [30] . Failure to properly regulate this process results in either a decrease or complete halt in viral production [30] . The functionality of chimaeric RREs was tested by measuring viral titres obtained upon transfection of cells with a plasmid containing the proviral sequence of the molecular clone NL4 . 3 of HIV-1 , in which we had replaced the native NL4 . 3 RRE with that of the various chimaeric RREs . To uncouple the effects on RNA-folding caused by the introduced RRE sequences from those altering the amino acid sequence of expressed proteins , we used a variant of NL4 . 3 that does not express Env ( NL4 . 3-Env− ) [31] , and a plasmid encoding the wild-type Env was co-transfected to complement the production of gp120 and gp41 proteins . In order to increase the statistical power of the analysis , additional chimaeric RREs were constructed using parental sequences other than those employed in our cell culture recombinant generation experiments ( following a PCR procedure described in Materials and Methods ) and tested for their functionality . As can be seen in Table 1 , the viral titres obtained with every chimaeric RRE sequence we tested were both similar to those obtained with non-recombinant parental RRE sequences and markedly higher than that observed when the RRE was replaced with a non-viral sequence ( see Materials and Methods ) . This result therefore clearly indicated that recombinants generated by breakpoints within the RRE generally retain the functionality of this element . To determine the functionality of individual recombinant envelopes at the protein level , full-length recombinant envelope genes containing breakpoints of interest were constructed by successive PCR , as described in Material and Methods . Each full-length recombinant gene was then cloned in the pcDNA3 . 1 expression vector , and used to transfect HEK 293T cells together with the pNL4 . 3-Env−-Luc plasmid , to generate viral particles pseudotyped with the recombinant envelope of interest . The functionality of the recombinant envelopes was then tested after transduction of HEK 293T-CD4+-CCR5+ cells at a multiplicity of infection of 0 . 1 , by measuring luciferase expression in these cells 48 hours after transduction . Since target cells cannot synthesize new viral envelope proteins , infection was limited to reverse transcription and , potentially , integration . The luciferase values observed therefore reflected the relative success of viral entry into the target cells . For this analysis recombinants derived from parental env sequences that yielded the strongest positive signals in this single cycle test were chosen ( parental sequences A-Q461 , C-CAP210 , G-1033 and O-32 , see Table 2 for their relative genetic distance ) due to the higher reliability of the luciferase signal . The parental env sequences were used as controls . As for the functional analysis of the RRE , additional recombinants involving combinations of parental sequences – other than those involved in the experiments of recombination in cell culture , but carrying breakpoints in the same regions – were also tested . These additional recombinant env sequences were generated by PCR , as described for the reconstitution of the full-length env gene . Luciferase values determined for each recombinant were plotted as a function of the corresponding breakpoint position ( Figure 3 ) . Recombinants with breakpoints falling within the six hot regions indicated in Figure 2B were preferentially characterized . It was apparent that most of the severely defective recombinants contained breakpoints in hot regions 2 and 3 of the recombination rate distribution ( Figure 3 ) . Given this data , we approximated a probability of Env functionality being disrupted by breakpoints falling within each of the six high recombination-rate regions . Since the parental sequences themselves were not uniformly functional ( Figure 3 ) , a situation that is probably common in nature , for each recombinant an estimate of loss of functionality was calculated by dividing the luciferase value obtained with that recombinant by the one of the least functional parental sequence involved in its generation . Recombinants displaying values between those of the two parental sequences were considered to retain functionality ( and assigned a functionality value of 1 ) . Of note , none of the recombinants yielded functionality values higher than that of the most functional parent from which it was generated . Values from recombinants containing breakpoints within the same region of the six hot regions were pooled , and a functionality loss value for each region was averaged ( Figure 3 ) . The most significant loss of functionality was observed in regions 2 , 3 , and 6 . Having defined a pattern of recombination in the absence of selection and the approximate probabilities of recombination events in various parts of env yielding fully functional products , we were interested in determining whether our experimental data could explain breakpoint patterns observed in circulating recombinants . The distribution along the whole HIV genome of 691 recombination breakpoints within HIV-1 group M full genome sequences from the LANL HIV Sequence Databases ( http://hiv-web . lanl . gov/ ) was inferred . The same approach used in Figure 2B to define the probability that at any region of the genome the breakpoints were more clustered than would be expected by chance was used , with a 200 nucleotides window . A previous analysis of HIV recombinants modelled the distribution of breakpoints and indicated a significant clustering of breakpoints in the 5′ and 3′ ends of the envelope gene and a lack of breakpoints between these regions [32] . Our new analysis ( Figure 4 ) confirmed the propensity for breakpoints to be located at the 5′ and 3′ ends of the env gene and the lack of breakpoints in the majority of its internal regions in recombinants from the database . In order to compare our experimentally determined breakpoint distribution to that found in recombinants from the HIV Sequence database , a higher-resolution view of the breakpoint distribution within the env gene was determined using the positions of 133 unambiguously unique recombination breakpoints detectable within 230 env sequences . Following the same procedure described above , but using a 50 nucleotides window to enable detection of breakpoint clusters at the same resolution as in our experimental system , we identified a series of recombination hot- and cold-regions within the gene ( Figure 5A , purple graph ) . In a similar way to the breakpoint distribution detected in cell culture , various hot regions could be defined ( light-purple boxes at the bottom of Figure 5A ) , which corresponded remarkably well to recombination hot regions 1 , 5 and 6 seen in cell culture ( light-blue boxes ) . Whereas the other hot regions identified in cell culture had no corresponding counterparts in the natural breakpoint distribution , there was close correspondence between the cold-spots detected in both distributions . Next we used the SCHEMA-based method [8] to investigate whether or not this breakpoint distribution exhibits evidence of purifying selection acting on recombinants with disrupted protein folding . This analysis indicated that breakpoints observable in natural viruses tend to occur in regions within env that were predicted to have a significantly lower impact on protein folding than randomly placed breakpoints ( p<1 . 0×10−4 for gp120 and p = 8 . 9×10−3 for gp41 , see Protocol S1 ) . To investigate whether accounting for variations in the functionality of recombinants might reconcile the natural and experimental breakpoint distributions , we first approximated the combined effects of mechanistic recombination rate variation ( Figure 2B ) and selection for fully functional recombinants ( Figure 3 ) on the distribution of breakpoints in cell culture . Selection “corrected” recombination rate estimates were then used to determine the distribution of 133 expected breakpoints . The resulting distribution was used to evaluate the probability of clustering of breakpoints ( green graph in Figure 5A ) . Only regions 1 , 4 , 5 and 6 remained areas of significant clustering ( light-green boxes at the bottom of Figure 5A ) , a pattern very close to that found in HIV recombinants sampled from nature , with the exception of region 4 for which there was substantially less evidence of recombination within natural recombinants than was expected based on our empirical model . Indeed , when compared to the distribution found for the 133 breakpoints encountered in the natural HIV recombinants ( Figure 5B ) , a remarkable overlap was observed , with the discrete statistically significant breakpoint clusters being consistently recaptured by our empirical model of env recombination . The substantial difference of recombination rates in region 4 was also clear .
Through the functional characterization of HIV envelope genes generated by recombination in the absence of selection , we retrace the early steps shaping patterns of inter-subtype env recombination found in the HIV-1 pandemic . We observe that the mechanism of recombination alone defines regions where recombination occurs at significantly higher rates than elsewhere along the gene . The existence of such regions is strongly suggestive of spatially conserved features in HIV genomes that either promote or restrict recombination between different isolates . The distribution of breakpoints within the gp120 encoding region ( Figure 2B ) is likely due to the distribution of conserved and variable regions , the latter restricting recombination because of the low degree of local sequence identity between the parental sequences [32] , [33] . Within genomic regions where sequence identity is high , a trigger for recombination could be the presence of secondary structures [34] . The highest recombination peak within the second region in Figure 2B ( corresponding with the C2 portion of gp120 ) coincides with a recombination hot-spot that is determined by the presence of a stable RNA hairpin structure [29] , [35] , [36] , while the fourth hot region ( Figure 2B ) corresponds to the RRE RNA structure that is highly conserved amongst all HIV isolates [37] . It is therefore possible that RNA secondary structures also contribute to the high rates of recombination observed at some of the other recombination hot regions . Noteworthy , the functionality of the RRE was retained even when crossing genetically distant isolates as for inter-group M/O recombinants ( Figure 2A ) , supporting the possibility that regions of the genome harbouring functional RNA structures , which are generally more conserved within the population , provide a mechanism for crossing distantly related retroviruses and are possibly important for recombination of RNA viruses in general . With respect to selection of recombinant genes at the protein level , experiments involving lattice proteins have shown that genes encoding proteins that tolerate mutations also tend to be recombination tolerant [2] . Since the env gene displays a degree of diversity between isolates from different HIV-1 group M subtypes ( [38] and references herein ) that is two to three times higher than the genome average , we anticipated that the manifest mutation tolerance of env might predispose it to high recombination tolerance . However , we show that this is not the case with certain regions within the gp120 encoding portions of env ( particularly region 2 described in the present work in Figure 3 ) tending not to tolerate recombination well . Viruses with small genomes ( including all RNA viruses ) tend to use overlapping genes expressed in different reading frames and to encode proteins that have multiple functions . The HIV envelope encodes for such proteins [26] , and the subtle biochemical equilibrium that regulates their functionality is very possibly limiting tolerance to recombination . The low recombination tolerance of the gp120-encoding region could only be imprecisely predicted based on computational estimates of recombination induced protein fold disruption using the SCHEMA algorithm [3] . This may have been due to either our SCHEMA analyses being based on incomplete gp41 and gp120 structures or the fact that the structures used only reflected a single conformation of these two proteins . Therefore this analysis neither takes into account the conformational changes required for Env functionality , nor the quaternary arrangement of the proteins within Env trimers . Despite these issues , the SCHEMA analysis indicated that , amongst the HIV env sequences sampled from nature , selection has been acting against recombinants with disrupted protein folding ( Table S1 ) . Unravelling the molecular reasons for the reduced functionality of certain recombinants could provide valuable insights into the nature of the molecular interaction networks required for proper Env function . The specific determinants of viral fitness ( or in vivo replicative capacity ) are complex and poorly understood at present . The fixation of a recombinant gene within a population is likely to depend on the interplay of multiple factors . Although combining cell culture functionality data with recombination rate heterogeneity is an oversimplified view of this process , the pattern of recombination predicted by our empirical model matches remarkably well the breakpoint distributions observed in nature ( Figure 5B ) . The only major deviation from this was constituted by the fourth recombination hot region observed in cell culture , which was absent from the natural breakpoint distribution ( Figures 2B and 5B ) . Determining the reasons for this discrepancy will improve our understanding of the mechanisms governing the success of recombinants in nature . Although the host immune response certainly plays a significant role in the selection of recombinant variants in vivo [13] , the similarities between the natural and experimental breakpoint distributions suggest that the forces responsible for the selection of recombinants in vivo only have limited impact on inter-subtype breakpoint patterns in env . This is most likely due to a combination of factors including mainly the complex epistatic interactions within env , the high density of fitness-determining loci within this gene , and the biochemical mechanism of recombination , which collectively constrain the fixation of genetic variability introduced by recombination . Negative fitness effects associated with recombination in env , however , should decrease with decreasing parental genetic distances [3] , [6] , [39] and therefore , in the context of intra-subtype recombination , the selective constraints on recombinants should be more relaxed than we have found them to be here . Considering recombination in env in the context of the rest of the HIV genome , it is apparent that env displays the most dramatically variable natural breakpoint distribution of all HIV genes [24] , [32] , and it constitutes the only gene within which there is an extended region with limited recombination ( Figure 4 ) . Nevertheless , although less marked , breakpoint distribution patterns reminiscent of those found in env , with alternate clusters and troughs are also identifiable in several other regions of the genome such as gag and pol [32] ( Figure 4 ) . Although little information is presently available either on differential mechanistic predispositions to recombination across these regions , or on the functionality of the resulting products , it is tempting to speculate that underlying rules such as we have defined here for env may also be operational in these other cases . In conclusion , by experimentally reproducing the generation of HIV-1 recombinants , we demonstrate that the distinctive distribution of breakpoints found in natural viruses is strongly shaped by both the mechanism of recombination , and the relative functionality of the recombinant genes . Thus , HIV evolution might not be the relentlessly unpredictable process it sometimes seems , and exploiting this evidence to pre-empt and counter the most favoured evolutionary tactics of this virus may ultimately be an efficient means by which we can devise effective vaccines and improve drugs against the virus .
HEK 293T , and CD4+CCR5+ 293T cells were grown in Dulbecco's modified Eagle's medium supplemented with 10% foetal calf serum , penicillin , and streptomycin ( from Invitrogen , CA , USA ) , and maintained at 37°C with 10% CO2 . MT4 cells were maintained in RPMI 1640 medium supplemented with 10% foetal calf serum and antibiotics at 37°C with 5% CO2 . The parental isolates used in this study were A-115 , A-120 , A-899 [33] , A-859 , A-905 ( from S . Saragosti ) and A-Q461 ( Gene Bank: AF407156 ) for subtype A isolates; B-THRO ( Gene Bank: AY835448 ) , for subtype B; D-126 , D-89 , D-122 , [33] and D-21 . 16 ( Gene Bank: U27399 ) , for subtype D; C-CAP210 ( Gene Bank: DQ435683 ) , for subtype C; G-858 , G-914 , ( from S . Saragosti ) and G-MP1033 ( from M . Peeters , Gene Bank: AM279365 ) , for subtype G; O-35 and O-32 , for group O ( from S . Saragosti ) . Single cycle recombination assays were performed using a system previously developed by our laboratory [29] . HIV-1 env fragments from group M subtypes A , C , D and G , and from group O viral DNA were amplified by PCR from infected PBMCs obtained from patients and cloned in plasmids ( called genomic plasmids ) , which differ for the genetic marker present downstream ( in the sense of reverse transcription ) of the sequence in which recombination is studied ( Figure 1 ) . All constructs were verified by sequencing . The trans-complementation plasmids , pCMV R8 . 2 [40] encoding HIV-1 Gag , Pol , and accessory proteins , and pHCMV-G [41] encoding the Vesicular Stomatitis Virus envelope protein were co-transfected into 293T cells with the two genomic plasmids to produce defective retrovirus particles which were then used to transduce MT4 cells as previously described [29] . The reverse transcription products were purified from the cytoplasmic fraction of transduced cells using the method described by Hirt [42] . The purified double stranded DNA was digested with DpnI for 2 h at 37°C ( in order to eliminate possible contaminating DNA of bacterial origin ) prior to PCR amplification as previously described [29] . The amplified product was purified after electrophoresis on agarose gel , digested with PstI and BamHI , ligated to an appropriate plasmid vector and used to transform E . coli . Plating on IPTG/X-Gal containing agar plates allowed blue/white screening of recombinant and parental colonies , respectively [29] . The frequency of recombination was determined by computing the number of blue colonies over the total number of colonies as described in reference [29] . Recombination breakpoints were identified by full-length sequencing of the env portion of the recombinant clones . The recombinant and parental sequences of each pair of isolates tested were aligned using CLUSTAL X [43] . The breakpoint location of each recombinant was determined as being the central position of the interval bounded by the two closest nucleotide sites that were characteristic of each of the parental sequences ) . Recombination rates were calculated as follows . We define each recombination window studied with each pair of parental sequences as RwXYa–b , for a recombination window involving isolates X and Y , spanning position a to position b of env ( reference sequence HXB2 ) ; a 50 nucleotides window was then considered ( XYa–bSwi , for a sliding window starting at position i of env ) , beginning from the 5′ border of the sequence studied and the number of breakpoints ( indicated as XYa–bni ) falling within the window was counted . The resulting recombination rate per nucleotide in the sliding window XYa–bSwi iswhere XYa–bN is the total number of breakpoints characterized for the RWXYa–b pair , and 50 is the size in nucleotides of the sliding window , and F the frequency of recombination observed in the whole region studied , as defined in the previous chapter . The sliding window was then displaced with a 10 nucleotides increment ( resulting in XYa–bSwi+10 , XYa–bSwi+20 , … ) across the recombination window , and XYa–bRi+10 , XYa–bRi+20 , … were computed . The various R values were reported in the graph as a function of the position of the midpoint of the window along the gene ( i . e . the position of the 25th nucleotide of each sliding window ) . For the pooled dataset reported in Figure 2B , the analysis based on the sliding window was repeated . If Swpi stands for the sliding window at position i for the pooled dataset , Rpi for the corresponding recombination rate , and q is the number of recombination window including position i , recombination rate at position i is calculated asTo statistically test for the presence of recombination hot and cold-spots in the experimentally determined recombination breakpoint distributions we used a modification of a permutation test described previously [44] . Unlike in analyses of natural recombinants , the breakpoint positions approximated in our experimental procedure were not subject to biases introduced by underlying degrees of parental sequence nucleotide variability and patchiness of parental sequence sampling . Rather than explicitly accounting for these biases when placing randomised recombination breakpoints as in the permutation test described by Heath et al . [44] , our modification of the test involved the completely randomised placement of recombination breakpoints . The test essentially involved the randomised recreation of 10 , 000 versions of our real dataset with each version containing exactly the same number of breakpoints between the same 17 parental sequence pairs observed in the real dataset . From breakpoint distributions determined for each of these 10 , 000 randomised datasets we were able to work out confidence intervals for expected breakpoint density variation given the completely random occurrence of recombination . For simulating the distribution of 133 breakpoints based on the combined effects of ( i ) the mechanistic recombination rate and ( ii ) selection for functional recombinants , local recombination rate data used to generate the graph in Figure 2B were first multiplied by the respective functionality scores given in Figure 3 for each corresponding region , yielding “functionality corrected” rates for each region . Once the expected breakpoint distribution of 133 unique recombinants determined by this method , the number of breakpoints present in a 50 nucleotides rolling window , sliding with a 10 nucleotides increment was calculated and plotted ( in Figure 5B ) as function of the position along the gene . Deviations from expected degrees of breakpoint clustering given the null hypothesis of random breakpoint locations , was tested using the same modification of the Heath et al . , [44] permutation test detailed above . Full-length sequences of recombinant env genes were reconstituted , using an overlapping PCR procedure . We separately amplified the region from the 5′ end of the acceptor gene ( using primer Topo5′ annealing to positions 5966–5990 of the reference strain HXB2 ) to the breakpoint position ( using a specific primer encompassing the region of the breakpoint ) and from the 3′ end of the donor gene ( primer Donor3′ , HXB2 positions 8785–8819 ) to the breakpoint position ( also in this case with a specific primer ) . These PCR products , overlapping by approximately 30 nucleotides around the breakpoint site , were mixed at equal ratios and used as templates to generate the full-length recombinant env gene using primer Topo5′ and Donor3′ . All PCR reactions were run with Phusion DNA polymerase ( Finnzymes , Finland ) for 30 cycles . PCR products were gel purified and ligated to pCDNA3 . 1 Topo ( Invitrogen , CA , USA ) . For RRE functionality assays , a portion of the envelope gene containing the RRE of pNL4 . 3-Env−-Luc ( nucleotides 7646 to 8046 ) was replaced with the corresponding sequence of parental or recombinant envelope genes or , as a negative control , a 400 nt sequence from the Drosophila melanogaster desoxynucleoside kinase gene ( ΔdNK ) . All constructs were verified by sequencing . HIV particles were produced by co-transfection of HEK 293T cells with an expression vector for a CCR5-tropic ( ADA ) HIV-1 envelope [45] kind gift of Dr . M . Alizon , together with a pNL4 . 3-Env−-Luc containing either a parental or recombinant RRE sequence or ΔdNK . Forty-eight hours post transfection , supernatants were filtered trough a 0 . 45 µM filter and p24 levels were determined using the HIV-1 p24 enzyme-linked immunoabsorbent assay kit ( PerkinElmer Life Sciences , MA , USA ) . Reporter HIV-1 particles were produced by co-transfection of HEK 293T cells with pNL4 . 3-Env−-Luc and either an empty expression vector or an expression vector encoding either a parental or a recombinant env . For each individual recombinant variant , prior to their use for transfection , clones were verified by sequencing of the region encoding the recombinant gene as well as the vector-encoded promoter for its expression . Supernatants , containing virus stock , were harvested 48 h post transfection , and filtered trough a 0 . 45 µM filter . Production of viral particles was tested using an enzyme linked immunoassay for HIV-p24 antigen detection ( Perkin Elmer , MA , USA ) and 20 ng of p24 were used to infect 105 293T CD4+-CCR5+ cells in 24 wells plates . Forty-eight hours later , cells were washed twice in PBS and lysed in 25 mM Tris phosphate , pH 7 . 8 , 8 mM MgCl2 , 1 mM dithiothreitol , 1% triton X-100 , and 7% glycerol for 10 min in a shaker at room temperature . The lysates were centrifuged and the supernatant was used to measure luciferase activity using a GloMax 96 Microplate Luminometer ( Promega , WI , USA ) following the instruction of the luciferase assay kit ( Promega , WI , USA ) . For samples that yielded negative results in the luciferase assay , plasmids from at least three independent bacterial clones were tested . The HIV-1 group M envelope sequence alignment was retrieved from the Los Alamos National Laboratory ( LANL ) HIV Sequence Database ( http://hiv-web . lanl . gov/ ) . The alignment was reduced to subtype reference sequences ( 3 strains for each where available ) , 53 CRF strains ( 2 strains for each where available ) and finally 197 apparently unique recombinants . Recombination was analyzed using the RDP [46] , GENECONV [47] , BOOTSCAN [48] , MAXCHI [49] , CHIMAERA [50] , SISCAN [51] , and 3SEQ [52] methods implemented in the program rdp3beta30 [53] . Default settings were used throughout except that: ( 1 ) only potential recombination events detected by four or more of the above methods , coupled with phylogenetic evidence of recombination were considered significant; ( 2 ) sequences were treated as linear; and ( 3 ) a window size of 30 variable nucleotide positions was used for the RDP method . Using the approach outlined in the rdp3 program manual ( http://darwin . uvigo . es/rdp/rdp . html ) , the approximate breakpoint positions and recombinant sequence ( s ) inferred for every potential recombination event , were manually checked and adjusted where necessary using the phylogenetic and recombination signal analysis features available in rdp3 . Breakpoint positions were classified as unknown if they were ( 1 ) detected at the 5′ and 3′ ends of the alignment but could have actually fallen outside the analysed region; or ( 2 ) within 20 variable nucleotide positions or 100 total nucleotides of another detected breakpoint within the same sequence ( in such cases it could not be discounted that the actual breakpoint might not have simply been lost due to a more recent recombination event ) . All of the remaining breakpoint positions were manually checked and adjusted when necessary using mainly the MAXCHI and 3SEQ methods ( using three sequence scans and the MAXCHI matrix method ) but also the LARD matrix method ( generated by the LARD two breakpoint scan; [54] ) , and the CHIMAERA method as tie breakers . The distribution of unambiguously detected breakpoint positions of all unique recombination events were analysed for evidence of recombination hot- and cold-spots with rdp3 as described by Heath et al . ( [44]; a window size of either 50 or 200 nucleotides and 10 000 permutations ) . A normalised version of the breakpoint distribution plot described in that study was used in which the local probability values of breakpoint numbers ( determined by a permutation test that takes into account that local degrees of sequence diversity influence the delectability of recombination events ) were plotted instead of absolute breakpoint numbers . PDB files detailing the three dimensional structures of both gp120 ( PDB ID: 2B4C , determined by X-ray diffraction , resolution of 3 . 3 Å , 338 amino acids , [55] ) , and gp41 ( PDB ID 1AIK , determined by X-ray diffraction , resolution of 2 Å , 70 amino acids , [56] ) were obtained from http://www . rcsb . org . It is important to point out that these structures are partial and that we therefore only analysed a fraction of the structural interactions involved in Env folding . We performed SCHEMA predictions of recombination induced fold disruptions using the set of natural HIV env recombinants ( described above ) essentially as described in Lefeuvre et al . ( [8]; See Protocol S1 , Supplementary Analyses , for a description of the SCHEMA method ) . This involved: ( 1 ) computing protein fold disruption , or E , scores for each natural recombinant with identifiable parents; ( 2 ) based on every pair of parental sequences identified for the observed set of recombinants , simulating every possible recombinant that could have been produced with these parental sequence pairs that involved the exchange of the same number of non-synonymous polymorphisms as were exchanged during the actual recombination events; ( 3 ) calculating E scores for each of these simulated recombinants; and ( 4 ) using a permutation test to determine whether mean E scores calculated for the natural recombinants were significantly lower than mean E-scores for the same set of recombinants randomly drawn from the simulated recombinant datasets ( Table S1 ) . If fewer than 5% of simulated datasets had an average E score lower than that of the actual dataset ( p<0 . 05 ) then this was taken to indicate that predicted fold disruptions incurred by real events were significantly less severe than if the observed distribution of breakpoints was uninfluenced by negative selection acting against recombinants with disrupted protein folding .
|
Recombination allows mixing portions of genomes of different origins , generating chimeric genes and genomes . With respect to the random generation of new mutations , it can lead to the simultaneous insertion of several substitutions , introducing more drastic changes in the genome . Furthermore , recombination is expected to yield a higher proportion of functional products since it combines variants that already exist in the population and that are therefore compatible with the survival of the organism . However , when recombination involves genetically distant strains , it can be constrained by the necessity to retain the functionality of the resulting products . In pathogens , which are subjected to strong selective pressures , recombination is particularly important , and several viruses , such as the human immunodeficiency virus ( HIV ) , readily recombine . Here , we demonstrate the existence of preferential regions for recombination in the HIV-1 envelope gene when crossing sequences representative of strains observed to recombine in vivo . Furthermore , some recombinants give a decreased proportion of functional products . When considering these factors , one can retrace the history of most natural HIV recombinants . Recombination in HIV appears not so unpredictable , therefore , and the existence of recombinants that frequently generate nonfunctional products highlights previously unappreciated limits of the genetic flexibility of HIV .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"virology/virus",
"evolution",
"and",
"symbiosis",
"molecular",
"biology/recombination",
"molecular",
"biology/molecular",
"evolution",
"virology/immunodeficiency",
"viruses",
"molecular",
"biology/bioinformatics"
] |
2009
|
Molecular Mechanisms of Recombination Restriction in the Envelope Gene of the Human Immunodeficiency Virus
|
Beta-diversity , the change in species composition between places , is a critical but poorly understood component of biological diversity . Patterns of beta-diversity provide information central to many ecological and evolutionary questions , as well as to conservation planning . Yet beta-diversity is rarely studied across large extents , and the degree of similarity of patterns among taxa at such scales remains untested . To our knowledge , this is the first broad-scale analysis of cross-taxon congruence in beta-diversity , and introduces a new method to map beta-diversity continuously across regions . Congruence between amphibian , bird , and mammal beta-diversity in the Western Hemisphere varies with both geographic location and spatial extent . We demonstrate that areas of high beta-diversity for the three taxa largely coincide , but areas of low beta-diversity exhibit little overlap . These findings suggest that similar processes lead to high levels of differentiation in amphibian , bird , and mammal assemblages , while the ecological and biogeographic factors influencing homogeneity in vertebrate assemblages vary . Knowledge of beta-diversity congruence can help formulate hypotheses about the mechanisms governing regional diversity patterns and should inform conservation , especially as threat from global climate change increases .
Beta-diversity , the change in species composition between places , represents the differentiation component of diversity , as opposed to the inventory component , which describes the species composition of a single place [1–3] . Although beta-diversity was originally defined as the differentiation of communities along environmental gradients [1] , the concept applies more widely to the phenomenon of species compositional change at any scale , regardless of mechanism [2–7] . Beta-diversity sensu lato is determined through a complex array of processes relating to the interaction of species traits ( e . g . , vagility and niche width ) and characteristics of the physical landscape ( e . g . , environmental dissimilarity , topographic complexity , and isolation ) over time [3 , 8–11] . Geographic variation in beta-diversity , from gradual changes to abrupt transitions , reflects past and present differences in environment , ecological interactions , and biogeographic history , including barriers to dispersal [4 , 7 , 9–15] . As beta-diversity quantifies the change , or turnover , in species across space , it is central to a wide array of ecological and evolutionary topics , such as the scaling of diversity [16–19] , the delineation of biotic regions or biotic transitions [20 , 21] , and the mechanisms through which regional biotas are formed [15 , 20–22] . Beta-diversity also provides information critical to conservation planning , which strives to represent all biodiversity within practical constraints such as area and cost [11 , 14 , 16 , 23 , 24] . While the total number of species , endemic species , or threatened species often contributes to the relative importance of an area [25–29] , it is the rate of species turnover between sites that dictates the optimal spatial arrangement of conservation areas [10 , 11 , 16] . Although the principles behind most approaches to systematic planning , such as complementarity , are driven by patterns of beta-diversity [23 , 30] , few methods make explicit use of turnover measures [6 , 31] . Directly incorporating beta-diversity patterns into priority setting , however , benefits conservation efforts . For example , modeling compositional dissimilarity to develop surrogates for data-poor regions can improve biodiversity representation [6 , 30 , 32] . Moreover , including turnover estimates in area selection algorithms captures variation in species assemblages , which helps to preserve ecological and evolutionary processes as well as underlying environmental heterogeneity necessary for long-term persistence [28 , 31] . Despite the importance of beta-diversity , relatively little is known about diversity's “other component , ” particularly at broad scales . This is largely because measures of beta-diversity require knowledge of species identities rather than just species counts . Recent advances in species distributional data have made beta-diversity analyses possible at large extents [17 , 20 , 21] , but these studies have been limited to single taxa . Cross-taxon congruence in beta-diversity has been tested only at small scales , with varying methods and results [14 , 22 , 32 , 33] , in contrast to the wide range of scales at which concordance in both species richness and endemism has been studied [34–38] . Here , we present what is to our knowledge the first analysis of beta-diversity congruence across large spatial scales , based on distributional data for three groups of terrestrial vertebrates in the continental Western Hemisphere . Beta-diversity of amphibians ( n = 2 , 174 ) [39] , breeding birds ( n = 3 , 882 ) [40] , and mammals ( n = 1 , 611 ) [41] was estimated as a function of the distance decay of similarity—the decrease in compositional similarity with increasing geographic distance between sites [4 , 7 , 10 , 14] . We modeled distance decay from each 100 km × 100 km grid cell , and used these models to calculate our measure of beta-diversity , βsim-d: the estimated proportional turnover in species composition at a distance of 100 km ( see Materials and Methods ) . This individual-cell-based technique accounts for the considerable geographic variation in the rate at which similarity decays , and can be used to produce a continuous layer of compositional change similar to past grid-based neighborhood analyses of broad-scale beta-diversity ( e . g . , [18 , 20 , 21] ) . Considering comparisons over a range of distances reduces possible bias in similarity levels that could arise from the differences in centroid to centroid distance and in shared perimeter length that occur between orthogonal and diagonal neighbors of a rectangular grid . The smoothing that results from the distance decay regressions also limits the influence of artifacts due to small-scale errors in range map boundary placement . Our approach to quantifying the distance decay relationship makes several improvements to methods used in previous studies [4 , 7 , 10 , 14] . For instance , we modeled distance decay using logistic regression , which has advantages over linear or log-linear ordinary least-square regressions [4 , 7 , 14] , particularly for proportional data [42] . Furthermore , following Lennon et al . [18] , we measured similarity with a metric shown to be independent of differences in species richness between grid cells in order to isolate change due to species replacement [43] ( see Materials and Methods ) . We tested congruence in βsim-d for the three taxa using two different approaches . With the first , we measured congruence in overall βsim-d patterns and examined whether congruence levels were consistent across multiple spatial extents and among different geographic locations . In the second approach , we quantified spatial overlap in the extremes of βsim-d . We report that the strength of congruence depends on the location and extent at which it is measured , and that overlap in high βsim-d is much greater than in low βsim-d . Furthermore , the pairs of taxa varied substantially in level of congruence and degree of overlap .
Amphibian , bird , and mammal βsim-d mapped at this scale ( Figure 1 ) provide a striking contrast to well-known patterns of broad-scale species richness for these vertebrate groups . Whereas high richness is generally concentrated in the tropics and decreases towards both poles [44] , βsim-d of all levels is found across a wide range of latitudes . High βsim-d stretches along the mountainous Pacific edge of the continents , while low βsim-d is found within more environmentally uniform portions of northern South America and boreal North America . Accordingly , βsim-d has a positive relationship with both elevation and number of biome boundaries ( βsim-d and elevation: Spearman rank ρ = 0 . 219–0 . 427 , p < 0 . 05 for amphibian βsim-d , p < 0 . 001 for other taxa; βsim-d and biome edge: ρ = 0 . 295–0 . 320 , p < 0 . 001 for all; Table S1; see Materials and Methods ) . Although the variables show considerable spread ( Figure S1 ) , high βsim-d grid cells of all three groups occur at significantly higher elevations and on a greater number of biome edges than expected by chance alone , while low βsim-d grid cells have significantly lower elevations and fewer biome edges than expected by chance ( Table S2; 10 , 000 random sets , p < 0 . 05 for elevation in amphibian low βsim-d grid cells , p < 0 . 001 for all others; see Materials and Methods ) . The weaker significance for elevation in amphibian low βsim-d grid cells is likely due to the wood frog ( Rana sylvatica ) being the only amphibian species to occur throughout much of the boreal region , including high-altitude areas such as the Alaska panhandle [45] . This amphibian homogeneity differs greatly from the high βsim-d of birds at northern latitudes , which captures the presence of a strong Holarctic element in the avifauna along the arctic coast [46] . Such differences in βsim-d reveal the individual biogeographic histories of the taxa and may arise from variation in dispersal ability , particularly in relation to historical factors such as glaciation and faunal interchange [45 , 47] . For instance , the elevated mammal βsim-d in South America's southern cone reflects a transition in the region's diverse mammal lineages , notably the radiation of narrowly ranging hystricognath rodents [48] , while the high amphibian βsim-d of the southern Appalachian Mountains results from the diversification of salamanders within this area's stable , moist environments [45] . Pair-wise correlations of amphibian , bird , and mammal βsim-d across the Western Hemisphere were positive and significant ( ρ = 0 . 340–0 . 553 , p < 0 . 001 for all; see Materials and Methods ) ( Table 1; Figure 2 ) . When measured at the extent of a single biogeographic realm , however , we found that pair-wise congruence was greater within the Neotropics ( ρ = 0 . 636–0 . 695 , p < 0 . 001 for all ) than at the hemisphere extent , but was comparatively weak within the Nearctic ( amphibians and mammals: ρ = 0 . 390 , p < 0 . 05; birds and mammals: ρ = 0 . 405 , p < 0 . 001 ) or even lacking ( amphibians and birds: ρ = 0 . 032 , not significant ) ( Table 1; Figure 2 ) . The disparity in congruence strength between the realms indicates that congruence measured across large regions can hide incongruities that manifest at reduced spatial extents [35 , 36] . To examine congruence at even smaller extents , we used a moving-window algorithm that calculated the correlation in βsim-d between each pair of taxa within a 350-km radius of each grid cell ( see Materials and Methods ) . Composite maps of the resulting correlation coefficients for the pairs revealed considerable geographic variation in congruence ( Figure 3 ) . Although the majority of correlations were strongly positive , others were weak or strongly negative . The latter were most apparent in the Nearctic realm for correlations with amphibians . Understanding the dependence of diversity relationships on observational scale is of pressing concern for ecology , biogeography , and conservation planning [10 , 18 , 23 , 36] . Our analyses demonstrate that both the geographic location and the spatial extent of analysis affect the level of congruence observed in βsim-d , and emphasize the need for tests across multiple scales and regions in order to make objective comparisons among ecological studies . Correlations across all grid cells do not necessarily indicate the level of cross-taxon spatial coincidence in areas of highest or lowest βsim-d—a more useful measure for conservation planning and biogeographic delineation [34 , 35 , 49] . Congruence in the extremes of diversity is frequently measured as the degree of overlap in matching percentage sets of two groups [34 , 50] . We evaluated high and low βsim-d congruence for the pairs of taxa and between all three groups as the proportion of maximum possible overlap [34] in matching percentage sets of the highest 2 . 5% and lowest 2 . 5% of each taxon's βsim-d grid cells ( see Materials and Methods ) . Spatial coincidence in high βsim-d was greatest between amphibians and birds ( 51 . 6% ) . These taxa showed lower , but similar levels of overlap in high βsim-d with mammals ( 21 . 5% and 29 . 2% , respectively ) , and coincidence between all three groups was minimal ( 15 . 1% ) . Grid cells with overlapping high βsim-d primarily occurred in the northern and southern Andes ( Figure 4 ) , consistent with the former as a center of endemism for all three taxa and with the extreme climatic gradient within the latter [44 , 45] . A substantial proportion of grid cells were found only in the high βsim-d percentage sets of one taxon . For example , 41 . 9% of amphibian high βsim-d grid cells were unique , as were 35 . 4% of bird high βsim-d grid cells and 64 . 6% of mammal high βsim-d grid cells . The distribution of these grid cells reflects the specific biogeographies of each taxon . Whereas unique grid cells were predominantly located in the northern Andes for birds and in the Central American highlands for amphibians , unique mammal grid cells were largely outside the tropics ( Figure 4 ) . There was comparatively little spatial coincidence in the lowest 2 . 5% of βsim-d . Low βsim-d of birds and mammals showed the most overlap , at only 11 . 5% . Coincidence was negligible for the other two pairs of taxa ( amphibians and mammals , 5 . 4%; amphibians and birds , 2 . 2% ) , and there was no overlap among all three groups . Accordingly , the majority of grid cells in the low βsim-d percentage sets were restricted to one taxon ( 83 . 3%–92 . 5% ) . These grid cells were located mainly in the boreal and arctic regions of the Nearctic realm for amphibians and mammals , respectively ( Figure 4 ) . Conversely , most unique bird grid cells occurred in the Neotropics within several biomes , including a substantial number in the Amazon Basin ( Figure 4 ) . The degree of overlap in matching percentage sets , however , does not provide a complete picture of spatial coincidence in the extremes of βsim-d . In fact , the majority of highest βsim-d grid cells for all three taxa actually had relatively high levels of βsim-d for the other groups ( Figure 5 ) , indicating that areas of high beta-diversity largely coincide . On average , more than two-thirds of grid cells in the highest 2 . 5% of one taxon's βsim-d grid cells were also in the highest 10% of βsim-d for the other taxa ( 70 . 0% ± 8 . 7% , range = 61 . 5%–81 . 7% ) . This was not true for low βsim-d . Low βsim-d grid cell sets exhibited greater variation in βsim-d values for the other taxa than did the high βsim-d sets . Moreover , less than one-quarter of the lowest 2 . 5% of one taxon's βsim-d grid cells were in the lowest 10% of βsim-d for the other taxa ( 21 . 9% ± 14 . 6% , range = 2 . 9%–40 . 6% ) —further evidence that areas of low βsim-d are spatially distinct ( Figure 5 ) . Congruence in beta-diversity of three groups of terrestrial vertebrates is highly dependent on the geographic location and extent of analysis , reflecting taxonomic and regional variation in the influence of large-scale historical processes and environmental factors [4 , 7 , 10 , 14 , 15] . Our results show that although correlations in amphibian , bird , and mammal βsim-d measured at small extents vary in strength throughout the Western Hemisphere , congruence is generally stronger within the Neotropical realm than within the Nearctic . This difference may be part of a broader asymmetry in biodiversity patterns between the Northern Hemisphere and the Southern Hemisphere [51 , 52] . The weak pair-wise correlations within the Nearctic realm , as well as the minimal overlap in both high and low βsim-d , could result from differing responses of amphibians , birds , and mammals to the realm's climatic and geologic history [45 , 47] . In contrast , the comparatively strong βsim-d congruence in the Neotropics is indicative of common patterns of speciation and extinction histories . This is particularly apparent within the Neotropical mountains , where the substantial overlap in high βsim-d among the three groups underscores the importance of this region in generating diversity . Variation in βsim-d congruence also has implications for conservation , because the efficacy of conservation surrogates and efforts to model overall biodiversity distribution depend on taxa having concordant patterns of compositional change [30] . Our results largely support these approaches , but it is important to recognize limitations that may arise from differing congruence levels among biogeographic realms . Regions of rapid species turnover require increased attention to the placement and size of conservation areas in order to protect biodiversity . Spatial coincidence in areas of high βsim-d is therefore encouraging , as successful conservation strategies in these places may be resource intensive . Conservation planning , of course , must occur across hierarchical scales in order to ensure adequate representation [23 , 28] . Broad-scale analyses of βsim-d highlight regions where protected areas should be closely spaced to effectively conserve biodiversity; however , the optimal configuration for conservation networks will depend on finer-scale beta-diversity patterns [53] . Mapping broad-scale βsim-d can also identify areas where species face increasing threat to persistence . For example , because βsim-d is high where species' ranges are particularly susceptible to climatic variability , such as at steep environmental gradients and centers of endemism [54–56] , or at biome transitions where range shifts are most noticeable [54 , 55] , we suggest that areas of high βsim-d are likely to be especially vulnerable to climate change . The unique biogeography of the Western Hemisphere—the great variation in the effects of Pleistocene glaciation , the complex of mountain chains along much of the western coast , and the relative isolation of the continents—has played a major role in shaping the distribution and evolution of biodiversity . More work is needed to determine if our findings will extend to other parts of the world with different geologic and environmental histories . Furthermore , the relative contribution of historical factors and current ecological interactions in determining beta-diversity patterns and congruence in beta-diversity across taxa is an important area of inquiry . Our results describe patterns of species turnover at a 100 km × 100 km resolution . As comprehensive finer-resolution data become available , further analyses will confirm whether the levels of beta-diversity and congruence we found are consistent with those measured at smaller grain sizes . Future research is also needed to ascertain the degree to which our results can be generalized to other taxa , especially more distantly related groups or those that show large variation in dispersal ability . For instance , taxa with poor dispersal and low rates of gene flow are apt to exhibit higher beta-diversity than those groups that have high dispersal and high rates of gene flow . However , we believe that some of our findings , such as the strong relationship between topography and beta-diversity congruence , will prove true for most taxa .
Analyses were based on range data for extant species of amphibians ( n = 2 , 174 ) , breeding birds ( n = 3 , 882 ) , and mammals ( n = 1 , 611 ) in the Western Hemisphere [39–41] . The range maps used for this study were obtained as digital vector files ( ArcView format ) from the Web sites indicated by [39–41] , where one can also find information on updates , detailed descriptions of the production process , and complete lists of sources . Note that these datasets are periodically updated , and the files used for these analyses may differ from the most recent versions available from [39–41] . We confined our analyses to terrestrial breeding birds , and we provide a map of bird βsim-d based on both breeding and non-breeding ranges of all terrestrial birds ( n = 3 , 890 ) for comparison . βsim-d for all birds ( Figure S2 ) was highly correlated with βsim-d for breeding birds ( Figure 1 ) ( ρ = 0 . 954 , estimated sample size [ess] = 249 . 12 , p < 0 . 001 ) . The number of species in these vertebrate groups is not static , as new species , especially of amphibians , continue to be discovered [57] . However , the areas from which species are most often described tend to be the same and will likely accentuate the patterns we present [58] . In relation to this point , systematic bias in the data may result from differences in sampling efforts , as the distributions of certain groups ( e . g . , birds ) or geographic areas ( e . g . , temperate regions ) for which sampling efforts have been intense will be more reliable than those that are undersampled ( e . g . , amphibians or tropical regions ) . As a precaution against such bias , we excluded from the analyses the 630 amphibian species with an IUCN Red List category of “data deficient” ( http://www . redlist . org/ ) because of the unreliability of their range maps . The exclusion of these species did not substantially affect our results ( correlation between amphibian βsim-d using all mapped species and amphibian βsim-d excluding “data deficient” species; ρ = 0 . 993 , ess = 158 . 6 , p < 0 . 001 ) . We recorded the presence/absence of each species in 100 km × 100 km equal-area grid cells , roughly equivalent to 1° × 1° at the equator ( Behrmann projection , WGS84 datum ) ; a species was considered present if any portion of its range ( exclusive of polygons coded as introduced , migratory , or vagrant ) occurred within the continental land area of the grid cell . However , the range maps used approximate the extent of occurrence of a species , rather than its area of occupancy , and therefore the species may not be found in every grid cell that falls within the mapped range [59 , 60] . Such biases are inherent in range data compiled across large regions , and remind us that while the patterns they show inform us about species turnover at broad scales , they are not a replacement for finer-scale distributional information . Grid cells on the perimeter of the continents vary considerably in the amount of land they contain , particularly those along the narrow Isthmus of Panama . To avoid potential effects of species–area relationships or errors from range map boundary placement , only grid cells containing ≥40% of continental land were included in the analyses ( grid cells: n = 3 , 693 for amphibians; n = 3 , 821 for birds and mammals ) . Estimates of βsim-d using this cutoff were not appreciably different from those based on a more conservative cutoff of 75% land area , but allowed for the inclusion of additional species . Grid cells were classified as either Nearctic ( n = 1 , 744 for amphibians; n = 1 , 862 for birds and mammals ) , Neotropical ( n = 1 , 878 , amphibians; n = 1 , 888 , birds and mammals ) , or transitional between the two biogeographic realms ( n = 71 for all taxa ) [61] . Transitional grid cells were not included in analyses at the realm extent . We used a moving-window algorithm to model the distance decay of similarity from each individual grid cell , and used the resulting regression parameters to calculate a value of beta-diversity , βsim-d , as the estimated proportional turnover from that grid cell at a distance of 100 km . Considering comparisons between grid cells over a range of distances helps alleviate concerns typical of gridded nearest-neighbor analyses of large-scale species distributions . For example , artifacts may arise from the small-scale errors that can occur in range boundary placement when converting polygon maps into gridded data , as well as from the discrepancy in centroid to centroid distance and shared perimeter length between orthogonal and diagonal neighbors in a rectangular grid . Similarity ( S ) between two grid cells was calculated as the complement of βsim , a dissimilarity metric that isolates change due to species replacement from differences in species richness: where a is the number of species shared , b is the number of species found only in the second grid cell , and c is the number of species found only in the first grid cell , making min ( b , c ) the number of unshared species in the more depauperate grid cell [18 , 43] . Therefore , as the complement of βsim ( i . e . , 1 − βsim ) , or the proportion of species in the more depauperate grid cell that also occur in the other grid cell . Note that S/1 − S is a transformation of the ratio of shared species to unshared species in the more depauperate grid cell , or a/min ( b , c ) . This enables us to model distance decay using a logistic regression functional form defined such that where d is the centroid to centroid distance , and I and r are fitted intercept and slope coefficients . This functional form has an advantage over linear and log-linear forms , in that is bound between zero and one , as is appropriate for a similarity index . The observed data are counts , and we use a binomial error distribution for our regression . This has several advantages over linear and log-linear regressions with a normal error distribution , resulting in a better empirical fit than other techniques [42] . First , the special cases where S = 0 ( i . e . , a = 0 ) and S = 1 ( i . e . , min ( b , c ) = 0 ) do not cause problems in the estimation process , as these are valid possibilities under the binomial distribution . Second , the binomial error distribution accounts for the greater variance in a/min ( b , c ) ( and hence S ) at low species numbers . The distance decay regression at each window was built using between-grid-cell comparisons of the focal grid cell and all grid cells within a ≤500-km centroid to centroid radius . Thus , unlike most published distance decay regressions , which compute a rate of change based on comparisons between all samples within a region , our regressions are based on comparisons only with the focal grid cell and therefore reflect the change from a particular point ( i . e . , grid cell ) . The arbitrary distance of 500 km was chosen after experimenting with several other maximum distances ( 350 , 1 , 000 , 1 , 500 , 2 , 000 , and 3 , 000 km ) because it provided a sufficient total number of between-grid comparisons ( i . e . , sample size ) , spread over a range of distances , to ensure a robust distance decay relationship , but did not result in an over-smoothed beta-diversity surface , as occurred with greater maximum distances ( as judged by visual comparisons of the maps ) . By transforming the above defined regression equation as it is possible to use any set of distance decay regression coefficients ( I and r ) to predict the proportional dissimilarity at any distance ( d ) . Thus , we used the coefficients from the distance decay regression for each grid cell to estimate our measure of beta-diversity , βsim-d , as ( 1 − ) for d = 100 km , or the estimated proportional turnover in composition from that grid cell at a distance of 100 km . The predicted degree of dissimilarity at a given distance , βsim-d , differs from the average observed dissimilarity ( βsim ) at the same distance because it accounts for the rate at which dissimilarity changes with increasing distance ( i . e . , the effect of extent ) . At the same time , βsim-d differs from the rate of distance decay in the following ways . ( i ) Estimates of βsim-d depend on both the intercept and slope parameters of the distance decay relationship . The former , as initial similarity level , reflects dissimilarity at near distances and the latter , as the rate of distance decay , captures dependency of dissimilarity on extent [62 , 63] . ( ii ) βsim-d is the estimated dissimilarity at a specified distance ( i . e . , 100 km ) —predictions at other distances ( e . g . , 50 or 300 km ) would result in different values , reflecting the effect of spatial extent on compositional change . Turnover at this distance , which is the minimum distance between adjacent grid cells , is more intuitive than that between distant grid cells for discussion and graphical representation of beta-diversity as a continuous surface , and makes it easier to compare our results to other broad-scale diversity analyses . Although the number of grid cells included in a regression model decreased with increased proximity to the coast ( including major interior water bodies ) , graphical examination of scatter plots and the resulting maps showed that coastal effects were negligible for amphibians and mammals and varied geographically for birds . The elevated bird βsim-d on some coastal sections likely has a biological rather than methodological basis [18] . It is important to remember that βsim-d quantifies change in species composition between 100 km × 100 km grid cells , and therefore does not reflect the level of heterogeneity within a grid cell . Furthermore , βsim-d is a measure of proportional species turnover and does not represent the absolute number of species gained or lost between grid cells . Lastly , while the smooth surface that results from modeling the effect of distance on similarity reduces the effect of potential errors in gridded large-scale range data , extremely abrupt transitions may be attenuated . However , the major patterns found for βsim-d were also apparent in maps of average nearest-neighbor beta-diversity ( the average dissimilarity [βsim] of a focal grid cell and its orthogonal and diagonal neighbors ) ( Figure S3 ) . Further , a comparison of Table 1 with pair-wise correlations of average βsim ( Table S3 ) shows that the congruence levels we report are not artifacts of the smoothing process . We tested whether grid cells containing high βsim-d or those with low βsim-d differed significantly in elevation or were found on a greater number of biome edges than could be expected by chance [64] . To do this , we selected sets of grid cells containing the highest 2 . 5% and the lowest 2 . 5% of βsim-d values for each taxon ( 2 . 5% = 93 grid cells for amphibians , 96 grid cells for birds and mammals ) , and calculated the mean elevation and mean number of biome edges for each set . We then compared these values to distributions of values for the mean elevation and mean number of biome edges , respectively , calculated for 10 , 000 sets of randomly selected grid cells ( grid cells per random set: n = 93 for amphibians; n = 96 for birds and mammals ) . For each comparison , we computed a one-tailed p-value by counting the number of values in the random distribution greater than or equal to the value of a high βsim-d set—or less than or equal to the value of a low βsim-d set . Elevation was measured as the mean elevation within a grid cell from a digital elevation model of approximately 1 km × 1 km resolution ( the Global 30 Arc Second Elevation Data Set , http://www1 . gsi . go . jp/geowww/globalmap-gsi/gtopo30/gtopo30 . html ) . Following van Rensburg et al . [49] , we considered a grid cell to be on a biome edge if a biome ( as delineated by Olson et al . [61] ) covering ≥5% of that grid cell also covered <5% of any of the neighboring grid cells . The number of biome edges was then calculated as the number of biomes in that grid cell meeting this definition . To evaluate the overall relationships between βsim-d and elevation and between βsim-d and number of biome boundaries within a grid cell , we calculated the correlation between βsim-d for the three taxa and each environmental variable . Correlations were calculated with Spearman rank correlation coefficients to accommodate the non-normal distributions of βsim-d . Standard significance tests are not appropriate for autocorrelated data because the assumption of independence is violated; therefore , we tested for significance using a method developed by Clifford et al . [65] that corrects the sample size of two variables based on the level of the spatial dependency in and between them [18] . We calculated the ess for each pair of variables using the PASSAGE software package [66] , and then used the corrected degrees of freedom to test the significance of each correlation . Pair-wise congruence at the hemisphere and biogeographic realm extents was measured as the correlation in βsim-d values for each pair of taxa , and significance was tested using the method described above . To examine congruence at extents smaller than a biogeographic realm , we calculated the correlation in βsim-d values within a ≤350-km-radius window ( centroid to centroid distance ) around each grid cell . We used this window size because it provided a better representation of the geographic variation in βsim-d at small extents than the other window sizes we experimented with ( radii of 150 , 250 , and 450 km ) . The same overall pattern was also apparent using larger windows but became increasingly muted as the extent widened . Moreover , larger windows had a greater discrepancy in the number of grid cells occurring within windows around coastal versus inland grid cells , while smaller windows considerably decreased the number of grid cells across which congruence was measured . The ≤350-km window was not substantially affected by either of these issues , and differences that did exist in the number of grid cells within coastal and interior windows did not appear to influence the geographical variation in congruence . Spatial overlap between matching percentage sets of the highest 2 . 5% and lowest 2 . 5% of βsim-d grid cells for each pair of taxa and for all three groups was calculated as the maximum overlap possible [34]: Nc/Nt , where Nc is the number of grid cells common to the sets and Nt is the total number of grid cells in the smallest set ( amphibians have slightly fewer grid cells than birds or mammals ) .
|
Beta-diversity—how species composition varies from place to place—is a fundamental attribute of biodiversity . However , despite its recognized importance , beta-diversity is rarely studied across large spatial scales . Here we use a new method to compare amphibian , bird , and mammal beta-diversity across large regions within the Western Hemisphere . We show that although the areas of low beta-diversity are different for the three groups , areas of high beta-diversity largely coincide . Moreover , we find that the degree to which the groups exhibit similar patterns of beta-diversity depends on the geographic location and extent at which it is measured . Beta-diversity is high where species are most susceptible to climate change , such as in areas with complex topography or high environmental variation . Identifying where areas of high beta-diversity coincide for different species groups is essential to the design of effective protected area networks .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"evolutionary",
"biology",
"ecology"
] |
2007
|
Putting Beta-Diversity on the Map: Broad-Scale Congruence and Coincidence in the Extremes
|
Poliovirus ( PV ) 2CATPase is the most studied 2C protein in the Picornaviridae family . It is involved in RNA replication , encapsidation and uncoating and many inhibitors have been found that target PV 2CATPase . Despite numerous investigations to characterize its functions , a high-resolution structure of PV 2C has not yet been determined . We report here the crystal structure of a soluble fragment of PV 2CATPase to 2 . 55Å , containing an ATPase domain , a zinc finger and a C-terminal helical domain but missing the N-terminal domain . The ATPase domain shares the common structural features with EV71 2C and other Superfamily 3 helicases . The C-terminal cysteine-rich motif folds into a CCCC type zinc finger in which four cysteine ligands and several auxiliary residues assist in zinc binding . By comparing with the known zinc finger fold groups , we found the zinc finger of 2C proteins belong to a new fold group , which we denote the “Enterovirus 2C-like” group . The C-terminus of PV 2CATPase forms an amphipathic helix that occupies a hydrophobic pocket located on an adjacent PV 2CATPase in the crystal lattice . The C-terminus mediated PV 2C-2C interaction promotes self-oligomerization , most likely hexamerization , which is fundamental to the ATPase activity of 2C . The zinc finger is the most structurally diverse feature in 2C proteins . Available structural and virological data suggest that the zinc finger of 2C might confer the specificity of interaction with other proteins . We built a hexameric ring model of PV 2CATPase and visualized the previously identified functional motifs and drug-resistant sites , thus providing a structure framework for antiviral drug development .
Poliovirus ( PV ) is the pathogen of poliomyelitis . PV infection can directly result in damage of motor neurons and cause neural lesions [1] . Since the launch of Global Polio Eradication Initiative ( http://polioeradication . org/ ) by the World Health Assembly , the number of poliomyelitis cases have been significantly reduced . The incidence of paralytic polio in 1988 was 1 , 000 children per day , and this number decreased to 400 per day in 2013[2] . Only three endemic countries remain today . Nevertheless , obstacles to global polio eradication remain . To overcome the last hurdles in the endgame phase , effective anti-PV drugs are critical in controlling transmission of vaccine-derived polioviruses ( VDPVs ) and in treating patients with chronic infection or personnel casually exposed to PV [3] . Further , to minimize poliomyelitis risk in the “post-polio” era , the National Research Council of the Unite States concluded that the development of antiviral drugs would be important , and possibly essential [4] . PV belongs to Enterovirus C species in the Enterovirus genus , Picornaviridae family [5] . It has an icosahedral non-enveloped capsid containing a single-stranded +RNA genome of ~ 7 , 500 nucleotides . PV has three serotypes , PV1 PV2 and PV3 , whose capsids exhibit distinct antigenicity [6] , but the replicative enzymes of PV are highly conserved not only among different serotypes , but also in other virus species . Therefore , the virally encoded replicative enzymes are considered as good targets for broad-spectrum antiviral drugs [7] . Among EVs replicative enzymes , 2C protein is arguably the most attractive target for direct-acting antivirals ( DAA ) development [8] . At least ten inhibitors have been found targeting 2C , including guanidine hydrochloride ( GuHCl ) [9 , 10] , HBB [11] , TBZE-029 [12] , MRL-1237 [13 , 14] , pirlindole , dibucaine , zuclopenthixol [15] , hydantoin [16] , fluoxetine [17 , 18] and brefeldin A [19] . Genotype analyses of drug-resistant virus clones had revealed a large set of residues of 2C that are likely to be involved in binding . Structural characterization of 2C proteins is therefore essential to visualize these potential drug binding sites and to assist antiviral drug development . 2C protein of enteroviruses has typically ~330 residues . It is composed of a N-terminal membrane binding domain , a central ATPase domain , a cysteine-rich domain and a C-terminal helical domain [20] . We recently determined the crystal structure of a soluble portion of 2C helicase from human enterovirus 71 ( EV71 ) [21] , the first high-resolution 2C structure in Picornaviridae . The ATPase domain of EV71 2C exhibits the structural characteristics of superfamily 3 ( SF3 ) helicases . EV71 2C has an unusual zinc finger with three cysteine ligands . The C-terminus of EV71 2C forms an amphipathic helix that mediates self-oligomerization through a specific interaction between 2C-2C . The hexameric ring model of EV71 2C revealed that the central pore is negatively charged and a putative RNA binding motif is located on the rim of the ring , which suggests the mode of RNA binding might be the winding around the hexameric ring . Poliovirus ( PV ) 2CATPase has been intensively studied for decades . Numerous genetic and biochemical studies have shown that PV 2CATPase is implicated in series of events in virus life-cycle , from uncoating [22] , cellular membrane rearrangement and assembly of membranous replication complexes [23–27] , viral RNA synthesis [28–30] to morphogenesis [31–34] . PV 2CATPase has only 329 residues but is very rich in features . It can be divided into an N-terminal domain , a central ATPase domain and a C-terminal domain . The N-terminal domain harbors a membrane binding motif [35] , an amphipathic helix [30] , an oligomerization motif [36] and an RNA binding motif [37] . The extreme N-terminal sequence of 2C is able to interact both with virally encoded proteins , 2B , 2BC , 3A , 3AB , 3C , VP3 [20 , 32 , 38 , 39] and cellular protein RTN3 [40] . Structural investigation of PV 2CATPase has been challenging because the N-terminal domain confers protein insolubility . Our structural characterization of EV71 2C was possible only when the N-terminal domain was removed . We characterized the roles of Walker A + B motifs , Motif C and arginine finger ( R-finger ) of EV71 2C in ATP hydrolysis and virus production . All of these conserved ATPase motifs are present in PV 2CATPase , and should exert similar functions [41 , 42] . It was predicted that the C-terminal cysteine rich motif of PV 2CATPase forms a CCCC type zinc finger [43] , but the second potential zinc coordination sites ( PCS ) of the putative zinc finger is not present in many other enterovirus 2C proteins including EV71 2C . The crystallographic data showed that EV71 2C indeed lacks the PCS2 and it possesses a rare zinc finger with only three cysteine ligands , but it does not seem to affect its zinc coordinating capability [21] . Whether PV 2CATPase has a distinct zinc finger requires structural investigation . PV 2CATPase shares 63 . 7% amino acid sequence identity with EV71 2C helicase , however , no conclusive evidence has shown that PV 2CATPase has helicase activity to date . To support RNA synthesis , Cho et al found that the 3D polymerase of poliovirus has the activity of unwinding long stretch of RNA duplex , and the nascent RNA strand can be displaced from the template during chain elongation reaction[44] . Thus , the unwindase activity of PV 3D satisfies the necessity of using a helicase during PV replication . Although many inhibitors have been found targeting PV 2CATPase over decades , the high-resolution structure of PV 2CATPase was still missing to date , which hindered the understanding of the mechanisms of inhibitor’s efficacy and drug-resistance and the development of anti-PV drugs . GuHCl for example inhibits ATPase activity of PV 2CATPase in vitro and in vivo [42] . Mutations that confer resistance to GuHCl were mostly mapped in segments outside the catalytically important ATPase motifs [9 , 10] , whose precise functions were unknown . We report here the crystal structure of a soluble fragment of PV 2CATPase containing the complete ATPase and the C-terminal domains to a resolution limit of 2 . 55Å . We found that the C-terminal helix mediates self-oligomerization of PV 2CATPase via a specific interaction between 2C-2C , which is essential to ATPase activity . Comparing the structures of PV 2C and EV71 2C revealed both common and distinct structural characteristics . We built a hexameric ring model of PV 2CATPase to visualize regions important to PV 2CATPase function and drug resistance .
To gain atomic insight into poliovirus 2CATPase , we carried out a crystallographic study . We first expressed a N-terminal maltose-binding protein ( MBP ) tagged full-length 2CATPase from human poliovirus 1 ( strain Mahoney , GenBank: KU866422 . 1 ) ; however , the MBP tagged protein did not yield crystals . Removal of MBP tag by proteinase cleavage only led to precipitation of PV 2CATPase . Therefore , we employed a similar strategy as used in expressing the soluble fragment of EV71 2C [21] . Firstly , we removed the N-terminal 115 residues of PV 2CATPase , ( PV-2C-ΔN ) . This fragment lacks the N-terminal membrane binding domain but retains a complete ATPase domain , a zinc binding site and a C-terminal helical domain . PV-2C-ΔN was soluble , but still failed to crystallize . Considering that surface-entropy reduction may favor crystallization [45] , we predicted a set of surface residues charges based on EV71 2C structure [21] . We then introduced alanine substitutions systematically to these residues with an aim of minimizing surface charging . We finally obtained crystals of PV-2C-ΔN bearing mutations E207A , K209A and R149A ( PV-2C-ΔN-3Mut ) . None of these mutations is located within conserved ATPase motifs , zinc binding site or the C-terminal helical domain . We grew PV-2C-ΔN-3Mut crystals in a hanging drop vapor diffusion system at 20°C . The crystals diffracted the X ray to a 2 . 55Å . It belonged to the space group of P21 and contained 6 copies of 2C per asymmetric unit ( ASU ) . We solved the crystal structure of PV-2C-ΔN-3Mut by molecular replacement using EV71 2C structure ( PDB code: 5GQ1 , B chain ) as the search model . Several regions of the initial atomic model were built manually , especially at the zinc binding site that accounted for the largest structural discrepancy . In the finally refined model , we were able to located most of residues in chains A , B , C , D and E . But chain H was largely disordered , only a fraction of residues was visible in the electron density . Chain A and B were the most intact 2C copies and they were associated with relatively low temperature factors , 67 . 82 Å2 and 72 . 44 Å2 , respectively . By contrast , the temperature factors of the other chains C , D , E and H are much higher; the values are 108 . 38 Å2 , 76 . 55 Å2 , 105 . 38 Å2 and 170 . 56 Å2 , respectively . Therefore , only chain A and B were used for further structural analysis . Because the average temperature factor of this structure is high , we generated a simulated-annealing 2Fo-Fc composite omit map to validate its quality . As illustrated in Fig 1 , the simulated-annealing composite omit map has a good fit with the final model PV-2C-ΔN-3Mut , which supported that the model was correctly built . We performed a structural comparison of the chains within ASU ( S1 Fig ) . The r . m . s . d . values among different chains ranges from 0 . 4Å to 1 . 3Å . The largest structural deviation is contributed by a small loop region 180-184aa of B chain between β3 and α2 , which is apparently resulted from the crystal packing artifacts . The C-terminal helical domain of PV 2C exhibits only slightly different orientation , but this is not comparable with the dramatical conformational changes observed at the C-terminal helix in EV71 2C structure[21] ( PDB entries: 5GQ1 and 5GRB ) . The statistics of data collection , structure refinement and structure validation are summarized in Table 1 . The overall fold of PV-2C-ΔN-3Mut is similar to EV71 2C . Structural comparison between PV-2C-ΔN-3Mut and EV71 2C ( PDB code: 5GRB , chain C ) gave a Dali Z-score of 30 . 9 with 197 aligned Cα atoms and the r . m . s . d . value was 1 . 3Å . PV-2C-ΔN-3Mut is comprised of an ATPase domain with canonical α/β Rossmann fold , a CCCC type zinc finger followed and a long helical C-terminus ( Fig 2A ) . The ATPase domain contains a five-stranded parallel β-sheet ( β1-β5 ) surrounded by three α-helices ( α1-α3 ) . We located three conserved ATPase motifs . Walker A motif is located on the loop connecting β1 and α1 , forming the phosphate binding loop ( P-loop ) . A phosphate group was found occupying the P-loop . The Walker B motif is located on the loop between β3 and α2 . The SF3 helicase specific motif C is located on the loop between β4 and α3 ( Fig 2A and 2E ) . The cysteine-rich motif of PV 2CATPase ( residue 267–289 ) , located between α4 and β6 , connects the ATPase domain and the C-terminal helical domain . The cysteine-rich motif folds into a CCCC type zinc finger ( Fig 2C ) . The zinc ion is coordinated by four cysteine ligands , C269 , C272 , C281 and C286 . Three of these cysteine residues are from the long loop between α4 and α5 , and one is from the C-terminus of α5 . The distances from each Sγ atom to the zinc varies from 2 . 2Å to 2 . 4Å , which are typical distances between sulfur and zinc as the median distance of Zn-S is 2 . 28Å [46] . In addition , two conserved residues C282 and K288 assist the zinc coordination . The carbonyl oxygen of C282 buttress the zinc ion from the bottom ( distance , 3 . 3 Å ) , whereas the side chain Nζ atom of K288 covers the zinc on top ( distance 4 . 2Å ) ( Fig 2B and 2C ) . There is an unusually long loop region between the second and third zinc ligands C272 and C281 , on which N277 and F278 dock their side chains into a hydrophobic groove formed between α1 and α6 helices . This hydrophobic interaction between the zinc finger and the ATPase domain may serve to further stabilize the folding of the zinc finger ( Fig 2C ) . Pfister and colleagues predicted four potential zinc coordination sites ( PCS1-4 ) in the cysteine-rich motif of PV 2CATPase [43] . We therefore adopted this nomenclature in our structural analysis of zinc finger structure for the convenience of comparing with the results reported previously . We found that while the conformation of C269 ( PCS1 ) , C281 ( PCS3 ) and C286 ( PCS4 ) are well conserved in both structures ( Fig 2D ) , residue C272 acts as PCS2 for zinc binding in PV 2CATPase , but its structural counterpart E272 in EV71 2C is not a zinc ligand . Instead , E272 forms a salt bridge with K288 stabilizing the overall folding of the zinc finger of EV71 2C . The loop harboring PCS2 ( residues 270–276 ) is among the least conserved regions in picornavirus 2C proteins ( Fig 2E ) , which coincides with the pronounced structural difference of this region between PV 2CATPase and EV71 2C ( Fig 2D ) . We performed crystal packing analysis of PV-2C-ΔN-3Mut structure and found that all 2C copies in the crystal polymerize via a C-terminus helix mediated 2C-2C interaction ( Fig 3A ) . The mode of PV 2C-2C interaction resembles EV71 2C-2C interaction we characterized previously [21] . Residues C323 , M324 , L327 and F328 of the C-terminus α6 helix of a PV-2C-ΔN-3Mut monomer formed a pocket-binding domain ( denoted: PBD ) that docks inside a hydrophobic pocket ( denoted: pocket ) between the zinc finger and the ATPase helicase domain of an adjacent PV-2C-ΔN-3Mut monomer ( Fig 3B ) . The pocket is formed by 13 residues , most of which are hydrophobic . A salt bridge between E325 from the PBD and R144 from the pocket further stabilizes 2C-2C interaction . To validate PV-2C-ΔN-3Mut oligomerization observed in crystalline state , we investigated self-oligomerization of PV-2C-ΔN using size-exclusion chromatography ( Fig 4 ) . The molecular mass of PV-2C-ΔN was observed to be 94kDa , close to the theoretical molecular mass of tetramers 95 . 2kDa , suggesting PV-2C-ΔN exists as tetrameric in solution . Removing PBD of PV-2C-ΔN ( PV 2C 116–319 ) reduced its molecular mass to 23kDa , nearly matching the theoretical molecular mass for monomers 23 . 8kDa . Therefore , the tetramerization of PV-2C-ΔN was dependent on the PBD mediated 2C-2C interaction . Our previous results demonstrated that mutation of either L327 or F238 could abolish self-oligomerization of EV71 2C and these two residues are highly conserved among 2C helicases [21] ( Fig 2A ) . We then measured the molecular mass of PV-2C-ΔN bearing mutation L327A and F238A respectively . The molecular mass of these mutants reduced to monomeric size , 26kDa , indicating that both residues play the crucial role in PV 2CATPase self-oligomerization ( Fig 4 ) . Our previous crystallographic study of EV71 2C identified a hinge region on C-terminus α6 helix , which could mediate rotation between 2C-2C , therefore resulting multiple 2C-2C conformations in crystal structure [21] . By contrast , we did not find the similar 2C-2C rotations in the crystal structure of PV-2C-ΔN-3Mut , the C-terminus α6 helix of PV 2CATPase was kept essentially straight in all 2C copies . We compared the PV 2C-2C conformation with multiple EV71 2C-2C conformations , which demonstrated that PV 2C-2C conformation resembles EV71 2C-2C conformation-2 [21] , a catalytically nonproductive conformation . We measured the distance from the phosphate group occupying the P-loop of a PV-2C-ΔN-3Mut monomer to the side chain of R241 ( arginine-finger ) on an adjacent PV-2C-ΔN-3Mut . The distance was 21Å , suggesting this cannot be a catalytically active conformation . In order to explore the biologically relevant conformation of PV 2CATPase , we built a hexameric ring model . We first searched structural homologues of PV-2C-ΔN-3Mut using Dali server , and found that beside EV71 2C , the best hit was the structure of a SV40 Large T Antigen ( PDB code: 2H1L ) . We generated a hexameric model of PV 2CATPase by superimposing six copies of PV-2C-ΔN-3Mut to each subunit within SV40 Large T Antigen hexamer . In the hexameric model of PV-2C-ΔN , the C-terminal PBD of a protomer is located very close to the hydrophobic pocket on the adjacent protomer . We therefore slightly bent the α6 helix so that the PBD could readily occupy the hydrophobic pocket ( Fig 5A ) . The hexameric model of PV-2C-ΔN has a ring-like shape with the diameter of 117Å and the height of 40Å ( Fig 5B ) . Its size is similar to EV71 2C hexameric model[21] . Adams and colleagues have reported that particles of the MBP tagged PV 2CATPase were composed of 5–8 protomers , and they exhibited increasing size ranging from 150 to 200Å [36] . The larger size of PV 2CATPase homo-oligomers visualized in the electron microcopy was possibly contributed by the N-terminal domain of PV 2CATPase and the presence of the MBP tag . While the unliganded foot-and-mouth disease virus 2C protein lacking the N-terminal domain self-oligomerizes in a concentration-dependent manner , this truncation containing a Motif C mutation ( N207A ) specifically forms hexamers in the presence of ATP and RNA[47] . The negative stain electron microscopy study further revealed FMDV 2C-ATP-RNA is a hexameric ring with 6-fold symmetry . The hexameric ring model of PV-2C-ΔN shows that all conserved ATPase motifs line up at the gaps between protomers , constituting the active sites ( Fig 5A ) . While Walker A , Walker B motifs and Motif C are located on one side of the active site , the R finger ( R241 ) is located on the other side . The distance from the phosphate group at the P-loop to R241 side chain has now reduced to 5 . 1Å , suggesting the improved active site conformation . The central pore of the hexameric ring has a funnel-like shape . While the opening on cytoplasm side is wider ( diameter = 27Å ) , the opening on membrane proximal side is narrower ( diameter = 14Å ) . It was previously reported that PV 2CATPase harbors two discrete segments , residues 21–45 and 312–319 , both involved in RNA binding [10] . Although the N-terminal RNA binding segment is missing in our structure , the C-terminal RNA binding segment is located on the rim of the hexameric ring , suggesting that the RNA might bind the rim of PV 2CATPase hexameric ring . To validate our structural findings , we characterized the ATPase activity of the MBP tagged full-length PV 2CATPase and a selection of mutants . We first characterized the ATPase activity of the wild-type enzyme ( Fig 6A ) . Our analysis showed that the ATPase activity of PV 2CATPase obeys the Hill equation . The nonlinear curve fitting using the Hill equation gave an R2 = 0 . 999 . The Hill coefficient n is approximal 1 . 9 , suggesting a positive cooperativity in ATP binding and hydrolysis . We calculated the enzyme kinetic parameters Vmax = 137 . 7±2 . 1 μM/min and Km = 641 . 7±25 . 1 μM . The turnover rate kcat of the enzyme is 27 . 5 min-1 . This value was calculated by dividing Vmax with the concentration of the monomeric MBP-PV 2C . The turnover rate of the MBP-tagged PV 2C is lower than that measured for the bilayer nanodiscs bound PV 2C[48] . The difference in the ATPase activity is likely due to the different strategy and affinity tag used in protein preparation . Next , we measured and compared the ATPase activity of the PV 2CATPase mutants ( Fig 6B and 6C ) . When we kept the substrate ATP concentration constant at 500μM , the wild-type PV 2CATPase exhibited an ATPase activity of 8 . 9±0 . 8 μmol/μmol/min-1 , whereas the catalytic inactive mutants K135A ( Walker A ) , D177A ( Walker B ) , N223A ( Motif C ) and R241A ( R finger ) all exhibited the background activities at least 20 folds less than the wild-type activity , similar to MOCK and MBP controls . In most of cases , mutations on either the PBD or the pocket led to severe losses or abrogation of ATPase activity . The loss of ATPase activity was possibly caused by the disruption of PBD-pocket interaction and in turn 2C self-oligomerization , therefore the active site between PV 2C-2C could not form . These results are consistent with size-exclusion chromatography experiment that the disruption of PBD-pocket interaction by mutation F328A or deletion of PBD abolished 2C tetramerization in solution ( Fig 4 ) . The ATPase activity of mutant C323A was measured as 12 . 1±1 . 2 μmol/μmol/min , similar to WT enzyme . This suggests that substituting of C323 by an alanine could not undermine the interaction between PBD-pocket . This hypothesis is supported by our size-exclusion chromatography results ( Fig 4 ) . PV-2C-ΔN bearing mutation C323A at the C-terminal PBD also eluted as tetramers , suggesting the homo-oligomerization remained unaffected ( Fig 4 ) . Among alanine substitutions of four zinc coordinating cysteine , C272A retained more than 70% of WT ATPase activities , 6 . 5±0 . 4 μmol/μmol/min , whereas C269A , C281A and C286A lost the majority of their activities; the percentage of the activities remained were 22% , 13% and 10% , respectively . Pfister et al . showed that while eliminating PCS1 ( C269 ) of PV 2CATPase caused the failure in recovering virus from plasmid , but eliminating PCS2 ( substitute C272 with serine or glutamine ) did not affected PV translation activity in vivo . Nevertheless , substitution of PCS2 could induce temperature sensitive phenotype , encapsidation defects and impairment of RNA replication at high temperature [43] . PCS2 is naturally missing in EV71 2C and many other enterovirus 2C proteins . To further investigate the significance of PCS2 , we introduced a set of mutations to an infectious clone of EV71 . Structural alignments of PV 2CATPase and EV71 2C ( Fig 2D ) revealed that residue E272 of EV71 2C is the structural counterpart of C272 of PV 2CATPase . We therefore substituted E272 with cysteine and histidine respectively , with an aim of adding PCS2 to the EV71 2C zinc finger . To cover all possibilities , we also substituted three other nearby residues S271 , N273 and N274 with cysteine respectively , so that at least one of these mutants may have a PCS2 for the zinc finger . To our surprises , the infectious clone bearing E272C or E272H did not showed the improved EV71 infectivity , instead they caused >75% losses of activities . Mutations N273C and N274C were also detrimental to virus production . Only S271C retained >75% of WT activity , but there was no improvement in EV71 production ( Fig 7A and 7B ) .
Comparing the available high-resolution structures of enterovirus 2C proteins ( PV 2C , EV-C species and EV71 2C , EV-A species ) , we identified both common and individual structural features . ( i ) The zinc finger is the most structurally distinct site in PV 2CATPase and EV71 2C . While PC 2CATPase has a CCCC type zinc finger , EV71 2C has only three cysteine ( lacking the PCS2 ) for zinc coordination . Eliminating the PCS2 of PV 2CATPase resulted in temperature-sensitive phenotypes and encapsidation defects [11 , 49] . We added the PCS2 to the zinc finger of EV71 2C in order to convert it to a CCCC type zinc finger similar to PV 2CATPase zinc finger , but it failed to improve EV71 infectivity . Zinc fingers are ubiquitous small motifs that function as binding module for nucleic acids or proteins , etc . [50] . It was demonstrated that the C-terminal cysteine-rich site of PV 2CATPase , the zinc finger in our structure , is required for morphogenesis[49] . Therefore , the significant difference in sequence and structure of the zinc finger we observed here may underlie the specificity of 2C protein and determine what process it may involve . The cysteine-rich motif is one of the least conserved regions in Picornaviridae 2C ( Fig 2E ) . In fact , the 2C of the foot-and-mouth disease virus ( FMDV ) does not even have a cysteine-rich motif between ATPase and C-terminal helical domains . So , this region of FMDV 2C might fold into a structure completely different from the zinc finger but it may still function as protein binding module with the distinct specificity . ( ii ) The hexameric ring models of PV 2CATPase and EV71 2C show that the ATPase active site formed between 2C subunits has nearly identical geometry and the catalytic residues ( Walker A & B , Motif C and R finger ) identified by structural and biochemical characterizations are invariant . ( iii ) The C-terminus amphipathic helix mediated self-oligomerization is common in enterovirus 2C . Analogous to EV71 2C , PV 2CATPase undergoes self-oligomerization both in crystalline state and in solution via PBD-pocket interaction . Although residues constituting the PBD and the pocket are not strictly conserved in PV 2CATPase and EV71 2C , the “knob-into-hole” interaction between PBD-pocket is identical . The PBD residues of PV 2CATPase include C323 , M324 , L327 and F328 , whereas the PBD of EV71 2C contains residues T323 , I324 , L327 and F328 . All PBD residues are hydrophobic , in which L327 and F328 are invariant . The strict conservation of L327 and F328 highlights their essential role in 2C activity . We showed that mutations L327 and F328 abrogated the ATPase activity and homo-oligomerization of both PV 2CATPase and EV71 2C helicases ( Figs 4 and 5 ) , and these mutations could halt EV71 infection [21] . The dimension of the hydrophobic pocket of PV 2CATPase is 17Å long , 12 Å wide and 7Å deep , which is similar to the pocket size of EV71 2C . We calculated the solvent accessible surface area ( SASA ) of the pocket of PV 2CATPase as 918 Å2 containing 13 residues , the SASA of the pocket of EV71 2C is 846 Å2 containing 14 residues . Eight zinc finger fold groups have been classified previously [50] . Comparison of the geometry of the zinc fingers of EV71 2C and PV 2CATPase with eight known groups of zinc finger demonstrates that it belongs to none of them ( S3 Fig ) . Therefore , the 2C zinc finger represents a new fold group , which we denote the “Enterovirus 2C-like” fold group . This group is composed of a N-terminal long loop followed by a short helix and a short loop . Three zinc ligands are contributed from the long loop , one ligand is contributed from the short helix . The short helix has a “PhhC” consensus sequence ( h represents hydrophobic residues ) in enteroviruses . The PhhC sequence is followed by a “GKA” motif that is invariant among enteroviruses . The conserved lysine of GKA motif plays an auxiliary role in stabilizing the zinc binding . The second zinc ligand is nonessential in zinc coordination . It can be substituted by a solvent water molecule , such in case of EV71 2C [21] . Previous studies have identified a large set of residues important to 2C functions . Genotype analyses of drug resistant virus clones have suggested many residues that were targeted by drugs . We summarized these findings and analyzed the compatibility of the published phenotypes with our structural characterization ( S1 and S2 Tables and Fig 8 ) . Based on their locations on 2C structure , we divided these residues into four categories . ( i ) Residues from Walker A motif , Walker B motif , Motif C and R finger are essential for ATPase/helicase activities . They are gathered in the gaps between subunits in the hexameric ring model of 2C protein . These residues are in general not involved in drug resistance , probably due to high genetic barrier . Only one GuHCl-resistant mutation was mapped to Walker A ( A133T ) of echovirus-9 2C [11] . ( ii ) Residues buried deeply into the hydrophobic core of 2C are important to overall folding . These include L125 , V218 , I142 A143 , M246 and I248 , most of which are buried between the parallel β-strand plane and the surrounding helices in ATPase domain . These residues were found important to encapsidation[31] , morphogenesis [49] or temperature-sensitive virus phenotype [51] . Mutations of these residues account for resistant to GuHCl , MRL-1237 and Hydantoin . ( iii ) Residues exposed to the molecular surface of 2C hexamer model may directly interact with protein binding partners , RNA or drugs . Most of these residues are mapped on the cytoplasmic side and the rim of the ring , however a few are on the membrane proximal side ( Fig 8 ) . Therefore , in vivo the membrane proximal side of the hexameric ring model is unlikely to be the accessible molecular surface of 2C . During infection , this side should be attached to N-terminal portion of 2C , whose structure is yet to be determined . We further divided the surface exposed residues into different regions ( Fig 8C ) . The majority of published drug resistant mutations were mapped into regions around the pore of the hexameric ring . These regions are potentially targeted by GuHCl , MRL-1237 , HBB , TBZE-029 , Fluoxetine , Dibucaine , Zuclopenthixol and Pirlindole . ( iv ) Residues located on N-terminal membrane binding domain are still missing in the available 2C structures . GuHCl is among the earliest identified compounds effective on PV and other EVs . We mapped all published GuHCl-resistant or dependent mutations [9 , 10] on the structure of PV-2C-ΔN , revealing two distinct sites , site-1 and site-2 ( Fig 8A and 8B ) . Mutations I142V and A143G cluster on site-1 , where they are deeply buried inside the hydrophobic core of ATPase domain . Substitute at here may affect the interaction with GuHCl or other ligands via an indirect way; the mechanism require further investigation . The other mutations , N179G on the loop between β3 and α2 , M187L on the α2 helix and S225T , I227M and A233T/S on the loop between β4 and α3 ( β4-α3-loop ) , cluster on site-2 . The β4-α3-loop is highly flexible in PV 2CATPase , and a segment of this loop residues 227–235 were missing in the electron density map . When displayed these residues on the hexameric ring model of PV-2C-ΔN , we found that six copies of site-2 from each subunit form a belt region surrounding the central pore ( Fig 8A and 8B ) , which may act as the direct binding site of GuHCl . Site-2 might act an essential interface for the binding with other viral or cellular proteins during replication . Hence , a plausible explanation of the inhibitory mechanism of GuHCl is blocking other proteins from binding to 2C by occupying site-2 . Mutations responsible for resistance to other drugs were also mapped on the β4-α3-loop . Echovirus 9 bearing 2C mutations I227L and A229V is resistant to HBB . Coxsackievirus B3 containing 2C mutation A224V , I227V and A229V is resistant to TBZE-029 , Fluoxetine and Dibucaine . Therefore , the β4-α3-loop of 2C is likely a “hot spot” for drug resistance . The investigation of PV morphogenesis suggested that the cysteine-rich motif on the C-terminal domain of 2C helicase is involved in encapsidation , possibly via an interaction with a region between Walker A and B motifs of the ATPase domain [34 , 49] . Our crystal structure of PV 2CATPase revealed that the α1 helix ( residues 135–150 ) located between the Walker A and B motifs ( Fig 2E and 2C ) directly interacts with residues N277 and F278 from the loop between PCS2 and PCS3 of the zinc finger . Wang et al showed that PV with K279A/R280A mutations showed defects in replication and encapsidation[49] . The same group later demonstrated that the PCSs of the cysteine-rich motif are also involved in encapsidation . According to the crystal structure of PV 2CATPase and the hexameric ring model , the cysteine rich motif folds into a zinc finger and it is located on six vertices of the ring ( Fig 8C ) . K279 and R280 are located immediately downstream of N277 and F278 , where they are fully accessible on the surface of the hexameric ring . Therefore , it is suggestive that the interaction between the zinc finger and the ATPase domains are important to stabilizing the folding of the zinc finger , whereas K279 and R280 may be involved in an interaction with other protein partners during encapsidation . Among four PCSs of the zinc finger , C269 ( PCS1 ) and C281 ( PCS3 ) are almost completely buried in the hydrophobic core; by contrast , C272 ( PCS2 ) and C286 ( PCS4 ) are relatively more exposed to the molecular surface . This supports the results reported by Wang and colleagues [49] , that while alanine substitution of PCS1 or PCS3 was lethal , substitutions of PCS4 and PCS2 exhibited temperature-sensitive and quasi infectious phenotypes respectively . It suggests that PCS1 and PCS3 are absolutely required in maintaining the folding of the zinc finger and probably the entire hexamer , whereas PCS2 and PCS4 might have an additional role of interacting with other proteins during encapsidation .
Vero and RD cells ( American Type Culture Collection ) were grown in Dulbecco’s modified Eagle’s medium ( DMEM , Gibco ) with 10% fetal bovine serum added . EV71 infectious clone was a gift from S . Cen ( Institute of Medicinal Biotechnology , Chinese Academy of Medical Sciences and Peking Union Medical College ) which contained the full-length cDNA of WT EV71 ( strain Fuyang , GenBank: EU703814 . 1 ) . The mouse anti-EV71 monoclonal antibody was purchased from Millipore . Donkey anti-mouse immunoglobulin G secondary antibody was purchased from LI-COR Biosciences . Because full-length PV 2CATPaseis unstable for structural and biochemical studies , we prepared the maltose-binding protein ( MBP ) tagged 2CATPase as described previously [36] . In brief , the cDNA encoding 2CATPase of human poliovirus 1 strain Mahoney ( GenBank ID: KU866422 . 1 ) was synthesized ( GENEWIZ ) , amplified using PCR and inserted into pMAL-c5X vector ( New England BioLabs ) between EcoRV and SalI sites . The resulting plasmid expressed a N-terminal tagged full-length PV 2CATPase ( MBP-PV 2C ) . The plasmid was transformed to competent E . coli cells BL21 ( DE3 ) . The bacteria were inoculated in a medium containing 100 mg/L ampicillin and grown to a density OD600 = 0 . 8 at 37°C . The cultures were cooled and induced with 0 . 3mM IPTG . The bacteria were then cultured at 18°C overnight before harvesting . The cells were pelleted by centrifugation ( 5 , 000g , 10min ) and resuspended in a cold lysis buffer ( 50mM HEPES pH 7 . 5 , 500mM NaCl , 1% Triton X-100 , 1mM DTT and 1mM EDTA ) on ice . The suspended cells were disrupted by ultrasonication on ice and clarified by centrifugation at 25 , 000g for 30min . The supernatant was loaded onto the amylose resin ( New England BioLabs ) and the resin was washed thoroughly by using a buffer containing 50mM HEPES pH 7 . 5 , 500mM NaCl , 1% Triton X-100 and 1mM DTT . The MBP-PV 2C protein was eluted with 5 column volume of elution buffer ( 50mM HEPES , pH 7 . 5 , 100mM NaCl , 1mM DTT and 20mM maltose ) . The final step of the purification was size exclusion chromatography using Superdex 200 HR10/300 GL column ( GE Healthcare ) pre-equilibrated with a buffer containing 20mM HEPES pH 7 . 5 and 100mM NaCl and 5mM DTT . The MBP tagged full-length PV 2CATPase were used for biochemical characterization , however , this protein did not yield crystals . To crystallized PV 2CATPase , we expressed a fragment of 2C lacking the N-terminal membrane binding domain . The cDNA encoding residues 116–329 of PV 2CATPase was amplified PCR and inserted into pET28-SUMO expression plasmid [21] ( modified from pET28a ) between BglII and XhoI sites . The resulting plasmid expressed a truncation of PV 2CATPase , denoted PV-2C-ΔN . The plasmid was transformed into E . coli competent cells BL21 ( DE3 ) ( Novagen ) . A single colony was picked and grew in 1L of LB media containing 50 mg/L kanamycin at 37°C to a density OD600 = 0 . 8 . The culture was then cooled to 18°C and induced by 0 . 5mM IPTG ( final concentration ) . The bacteria culture continued at 18°C overnight . The cells were harvested by centrifugation ( 5 , 000g , 10min ) and resuspended in cold lysis buffer ( 50mM Tris-HCl pH 7 . 5 and 100mM NaCl ) and disrupted by ultrasonication . The cell debris was removed by centrifugation at 25 , 000g for 30 min . The supernatant was loaded to Ni-NTA resin ( Qiagen ) . The column was washed thoroughly with 20 column volumes of wash buffer containing 50mM Tris pH 7 . 5 , 100mM NaCl , 20mM imidazole to eliminate nonspecifically bound proteins . Subsequently , Ulp1 peptidase was added to cleave SUMO tag at 4°C overnight . The flow through containing the non-tagged PV-2C-ΔN was collected and subjected to Superdex 200 HR10/300 GL column ( GE Healthcare ) pre-equilibrated with a buffer containing 20mM HEPES pH 7 . 5 and 100mM NaCl and 5mM DTT . Mutations of the full-length MBP-PV 2CATPase and PV-2C-ΔN were introduced by site-directed mutagenesis . The mutants were expressed and purified using the same protocols described above . PV-2C-ΔN-3Mut was concentrated to ~5 mg/ml before crystallization trials . The protein was crystallized by mixing 1μl of protein sample ( 5mg/ml ) with 1 . 3μl of buffer containing 0 . 2M MgCl2 , 0 . 1M MES pH 6 . 5 , 3% ( v/v ) PEG4000 , 9 . 2% ( v/v ) polypropylene glycol P 400 , and 5mM TECP were freshly added to the buffer before use . The crystals were grown in a hanging-drop vapor diffusion system at 20°C . The crystals were flash frozen in Liquid nitrogen . Glycerol ( v/v 25% ) was used as the cryoprotectant . Complete datasets were collected at BL18U1 beamline of Shanghai Synchrotron Radiation Facility ( SSRF ) . The crystal diffracted the X ray to 2 . 55Å . It belonged to a space group of P21 , contained six copies of PV 2C-ΔN in the asymmetric unit ( ASU ) . The structure was solved by molecular replacement using EV71 2C structure ( PDB code: 5GQ1 , B chain ) as the searching model . The atomic model of PV-2C-ΔN-3Mut was completed by manual building using the software Coot v0 . 8 . 2 [52] . The structure was refined using the software PHENIX v1 . 10 . 1[53] . Most residues of PV-2C-ΔN-3Mut were located in chain A , B , C , D and E , however only a fraction of residues was visible in chain H . The loop between β4 and α3 was highly flexible in all chains . The composite omit map of PV-2C-ΔN-3Mut was calculated using the software phenix . composite_omit_map from PHENIX v1 . 10 . 1[53] . The software was run in a simulated annealing mode to aggressively remove phase bias . The annealing method used was cartesian and the starting temperature was 5 , 000 K . The statistics of data collection , structure refinement and structure validation were summarized in Table 1 . ATPase assays were performed as previously described [21 , 36 , 54] . The concentration of the enzyme was kept constant at 5μM in all reactions . The volume of the reaction mixtures was 50μl , contained 20mM HEPES pH 7 . 5 , 4mM magnesium acetate , 5mM DTT , 500μM ATP and trace amount of [γ-32P] labelled ATP . The mixtures were incubated at 30°C and the reactions were initiated by adding enzyme . At the given time point , 10μL of the reaction mixture was removed and mixed with EDTA ( final concentration = 0 . 1M ) to quench the reaction . At least three time points were recorded for each reaction . The mixtures were resolved by thin-layer chromatography using PEI ( Polyethylenimine ) Cellulose Plates ( Sigma-Aldrich ) with a buffer containing 0 . 8M acetic acid and 0 . 8M Lithium chloride [21] . The PEI plates were visualized and quantified using Typhoon TrioVariable Mode Imager ( GE Healthcare ) . Vero and RD cells were grown with 5% CO2 in DMEM , supplemented with 10% Fetal Bovine serum . EV71 infectious clone contained the full-length cDNA of WT virus . The mutations were introduced by the site-directed mutagenesis . The plasmids were linearized by HindIII digestion and were in vitro transcribed to RNAs using the MEGA script T7 Kit ( Ambion ) . Subsequently , the RNAs were transfected in Vero cells with Lipofectamine 2000 ( Invitrogen ) according to manufacturer’s instructions . The Vero cells were cultured at 37°C . 72 hours post-transfection , the supernatants of cell culture were collected to infect RD cells seeded in a 24-well plate at the given temperatures . The RD cells were fixed 24 hours post-infection . Immunofluorescence assays were performed to probe the production of EV71 virus using mouse anti-EV71 monoclonal antibody and Donkey anti-mouse immunoglobulin G secondary antibody . At least eight different visual fields from each well were photographed randomly under the microscope . The positive dots were counted using the software ImageJ v1 . 2 . 0 [55] . The Superdex200 10/300 GL column ( GE Healthcare ) was equilibrated with a buffer containing 20mM HEPES pH 7 . 5 and 100mM NaCl and 5mM DTT . The column was calibrated with Gel Filtration Standard ( BIO-RAD ) containing thyroglobulin ( 670kDa ) , γ-globulin ( 158kDa ) , ovalbumin ( 45kDa ) , myoglobin ( 17kDa ) , and vitamin B12 ( 1 . 35kDa ) . The purified proteins were loaded and eluted with a flow rate of 0 . 5ml/min . The Log of the molecular mass ( kDa ) of the standards was plotted as the function of the elution volume . Four standard proteins: thyroglobulin , γ-globulin , ovalbumin and myoglobin were used for a linear-fitting to generate a standard curve . The molecular mass of PV-2C-ΔN variants was then calculated using the standard curve .
|
Since the launch of the Global Polio Eradication Initiative , the number of poliomyelitis cases has significantly reduced but obstacles to disease eradication remain . In the endgame phase , anti-poliovirus drugs will be critical in controlling transmission of vaccine-derived polioviruses and in treating patients with chronic infection . However , no effective anti-poliovirus drugs are yet available . The 2CATPase encoded by poliovirus is one of the most important drug targets , and many inhibitors of 2C have been found . We report here the crystal structure of a soluble portion of PV 2CATPase , containing an ATPase domain , a zinc finger and a C-terminal helical domain . Our findings not only revealed common and individual structural features in the picornaviral 2C protein family , but also allowed us to visualize a large collection of functional motifs and drug-resistant sites identified from the decades-long investigations of poliovirus 2CATPase . Our findings are invaluable for understanding the function of picornavirus 2C proteins and the development of antiviral drugs .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"crystal",
"structure",
"chemical",
"compounds",
"molecular",
"mass",
"pathology",
"and",
"laboratory",
"medicine",
"enzymes",
"pathogens",
"condensed",
"matter",
"physics",
"enzymology",
"microbiology",
"organic",
"compounds",
"phosphatases",
"organisms",
"viruses",
"rna",
"viruses",
"amino",
"acids",
"sequence",
"motif",
"analysis",
"crystallography",
"cysteine",
"research",
"and",
"analysis",
"methods",
"chemical",
"properties",
"sequence",
"analysis",
"physical",
"chemistry",
"solid",
"state",
"physics",
"bioinformatics",
"proteins",
"medical",
"microbiology",
"zinc",
"microbial",
"pathogens",
"chemistry",
"adenosine",
"triphosphatase",
"physics",
"sulfur",
"containing",
"amino",
"acids",
"biochemistry",
"enteroviruses",
"helicases",
"organic",
"chemistry",
"poliovirus",
"database",
"and",
"informatics",
"methods",
"viral",
"pathogens",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"chemical",
"elements"
] |
2018
|
Crystal structure of a soluble fragment of poliovirus 2CATPase
|
Individuals exposed to malaria infections for a long time develop immune responses capable of blocking Plasmodium transmission to mosquito vectors , potentially limiting parasite spreading in nature . Development of a malaria TB vaccine requires a better understanding of the mechanisms and main effectors responsible for transmission blocking ( TB ) responses . The lack of an in vitro culture system for Plasmodium vivax has been an important drawback for development of a standardized method to assess TB responses to this parasite . This study evaluated host , vector , and parasite factors that may influence Anopheles mosquito infection in order to develop an efficient and reliable assay to assess the TB immunity . A total of 94 P . vivax infected patients were enrolled as parasite donors or subjects of direct mosquito feeding in two malaria endemic regions of Colombia ( Tierralta , and Buenaventura ) . Parasite infectiousness was assessed by membrane feeding assay or direct feeding assay using laboratory reared Anopheles mosquitoes . Infection was measured by qPCR and by microscopically examining mosquito midguts at day 7 for the presence of oocysts . Best infectivity was attained in four day old mosquitoes fed at a density of 100 mosquitos/cage . Membrane feeding assays produced statistically significant better infections than direct feeding assays in parasite donors; cytokine profiles showed increased IFN-γ , TNF and IL-1 levels in non-infectious individuals . Mosquito infections and parasite maturation were more reliably assessed by PCR compared to microscopy . We evaluated mosquito , parasite and host factors that may affect the outcome of parasite transmission as measured by artificial membrane feeding assays . Results have led us to conclude that: 1 ) optimal mosquito infectivity occurs with mosquitoes four days after emergence at a cage density of 100; 2 ) mosquito infectivity is best quantified by PCR as it may be underestimated by microscopy; 3 ) host cellular immune response did not appear to significantly affect mosquito infectivity; and 4 ) no statistically significant difference was observed in transmission between mosquitoes directly feeding on humans and artificial membrane feeding assays .
Malaria is transmitted in 97 countries worldwide where a total of ~207 million cases and ~528 , 000 deaths are reported yearly . Plasmodium vivax , the second most prevalent malaria parasite species , is endemic in 58 countries where annually between 16 and 22 . 2 million cases are reported [1] . Individuals continuously exposed to malaria infections develop clinical immunity that protects them from severe and complicated disease [2] . This immunity and features in the parasite biology i . e . presence of hypnozoites leads to the presence of a significant number of asymptomatic infections in malaria endemic regions [3]; although sterile immunity is never achieved under natural conditions [4] . Individuals in these communities also develop immune responses that reduce or completely block parasite transmission to Anopheles mosquitoes in what has been called transmission blocking ( TB ) immunity [5] which may play an important role in decreasing malaria transmission in endemic areas [6–8] . Both the mechanisms involved in TB activity as well as the biology of Plasmodium transmission from human to mosquito are poorly understood [9 , 10] . Multiple host , parasite and vector factors appear to be involved in this transmission . Although malaria-causing parasites P . vivax and P . falciparum are similar in some features , there are important biological differences between them . P . vivax is characterized by inducing periodical clinical relapses due to the spontaneous activation of liver hypnozoites , which do not develop in P . falciparum infections . Additionally , P . falciparum merozoites invade erythrocytes of all ages , and this species is known to develop gametocytes in a relatively late phase of the erythrocytic cycle , whereas P . vivax invasion is restricted to reticulocytes and gametocytes appear in blood significantly earlier . It is therefore likely that earlier appearance of P . vivax gametocytes translates into greater transmissibility[11] . Factors such as total parasite density , gametocyte maturation stage , male/female gametocyte ratio and others may affect mosquito infection outcomes[12 , 13] . Furthermore , differences regarding acquisition of immunity to P . falciparum and P . vivax can be of great importance for the development of TB immunity . It has been suggested that clinical immunity against P . vivax is established earlier in life and consequently , it could require fewer malaria episodes to be developed than P . falciparum[14] . In our studies [15–18] , most asymptomatic infections associate with submicroscopic infections , which may be reflecting the low availability of reticulocytes as well as efficient clinical immunity . Additionally , mosquito factors appear critical e . g . genetic diversity , as only a limited number of anopheline species are competent vectors and not all females of a given species are equally susceptible to infection [19] . Additionally , mosquito characteristics such as longevity , pH , midgut temperature [20 , 21] and microbiota are critical for successful infection [22 , 23] . Furthermore , human host factors such as the degree of malaria immunity , diet and nutritional status , as well as drug therapies , could influence Plasmodium transmission to mosquitoes . Because of the availability of highly synchronized mature P . falciparum gametocytes in culture , i . e . NF54 strain , parasite transmission and TB effects can be readily measured in laboratory conditions adding test sera or antimalarial products to gametocyte-enriched cultured blood used to feed laboratory reared mosquitoes [24 , 25] . It has been possible to develop a highly standardized mosquito membrane feeding assay ( SMFA ) which allows artificial mosquito infections to routinely evaluate the ability of naturally elicited antimalarial antibodies to block mosquito infection . Mosquito feeding can be performed either by direct biting on infected malaria patients or by ex vivo exposure to infected blood delivered through glass devices covered with an artificial membrane . While the first method requires the availability of infected patients carrying mature gametocytes in blood circulation and the fulfillment of ethical restrictions , the second is based on the use of cultured parasites such as Plasmodium falciparum , or in the case of Plasmodium vivax , due to the lack of in vitro cultures , the use of infected blood directly obtained from patients is required . TB can be assessed based on different parameters such as: recution in the number of infected mosquitoes , reductions in the oocysts counts per mosquito , or by the reduction on sporozoite production . Mosquito TB assays [SMFA and membrane feeding assay ( MFA ) ] permit epidemiological studies to determine the prevalence of TB activity in endemic communities [24 , 25] or to evaluate TB activity of antibodies elicited artificially by vaccination with specific parasite antigens or mosquito components being tested as TB vaccine candidates [26 , 27] . Epidemiological studies conducted in Africa , indicate the presence of TB immunity . Likewise studies carried out in Asia [28] and Latin America [29 , 30] , indicate a high prevalence of P . vivax TB activity in endemic communities . On the other hand , several P . falciparum surface antigens have been proposed and tested as TB vaccine candidates . The parasite antigen PfS25 is expressed in oocysts/ookinete that induces complete TB activity and is currently under clinical development [31 , 32] . The gametocyte antigen involved in the fertilization process Pfs48/45 , is also in clinical development [32] . In the case of P . vivax , TB vaccine development has proved difficult due to technical constraints imposed by the lack of parasite culture methods . For this species the most advanced TB vaccine candidate is Pvs25 which has shown induction of high TB activity in preclinical studies and has a clinical product tested in phase 1 trial [27 , 33] . However , several P . vivax vaccine candidates including Pvs48/45 , Pvs230 and Pvs47 are under development [26 , 32] An additional constraint to study P . vivax TB is the scarcity of laboratory colonies of P . vivax susceptible Anopheles mosquitoes , which limits the studies on P . vivax TB immunity [30] . Anopheles albimanus is widely distributed in the American continent , ranging from southern regions of Mexico to northern Peru . This mosquito species has been found naturally infected by both P . vivax and P . falciparum and is considered a primary malaria vector in Latin America . In spite of being less susceptible[34] , this mosquito species is easy to colonize , and it has been used for several years under laboratory conditions in different centers for malaria sporozoite production and TB studies[30 , 35–37] . This is a preliminary study focused on the optimization of the currently available P . vivax MFA in An . albimanus [30] and on comparing controlled MFA and DFA assays in order to further establish optimal conditions for reliably determining the prevalence and intensity of TB activity present in communities naturally exposed to P . vivax malaria . Furthermore , an optimized P . vivax MFA could be employed to assess the responses elicited by vaccination with P . vivax TB vaccine candidates .
The protocol was approved by the Ethics committee of Centro Internacional de Vacunas ( CECIV ) . Samples from volunteers were collected anonymously and not linked to the identity of the donor . Written informed consent ( IC ) was obtained from each volunteer at enrollment . All volunteers were adults . Blood samples harboring P . vivax parasites were obtained from symptomatic ( fever ( axillary temperature >37 . 5°C ) , malaise , chills , and/or headache ) and microscopically confirmed P . vivax malaria patients who presented to malaria outpatient clinics in Tierralta , Cordoba and Buenaventura , Colombia between January and August , 2014 . A total of 94 parasite donors , men and women aged 18–65 , were recruited from outpatient centers and signed an IRB approved informed consent ( IC ) before enrollment ( Fig 1 ) . A total of 10 mL of blood was drawn from each volunteer for molecular confirmation of Plasmodium species , quantification of parasite and gametocyte density and maturation state , and subsequent MFA . Sera were used for cytokine level measurement . Samples were fractionated and handled as described below . A subgroup of 24 volunteers was asked for their willingness to allow mosquitoes to directly feed on the forearm ( DFA ) . Before enrolment , malaria diagnosis was performed by microscopy and later on confirmed by qPCR . Patients were considered malaria positive and included in the study if confirmed to be positive exclusively for P . vivax infection by microscopy and reported not having initiated any anti-malarial treatment before diagnosis . For microscopic blood examination , two drops of blood ( ~100μL ) were obtained by finger prick , deposited onto glass slides , and stained using Giemsa stain method [38 , 39] . Presence of sexual and asexual parasites was independently confirmed by two experienced microscopists , using 200 leukocytes as reference for quantification . Parasitemia and gametocytemia were reported per microliter assuming an average of 8000 cells/μL [40] . Parasitemia was confirmed and quantified by the qPCR method using Taqman probes based on Plasmodium sp . 18S as previously described [41] . Positive control DNA , and a calibration curve of known parasitemia for P . falciparum and P . vivax were included in each run including the extraction of a negative control . Samples were considered negative if an increase in the fluorescence signal was observed after a minimum of 40 cycles . Volunteers who accepted direct mosquito feeding were subjected to the DFA as previously described [42] with minor modifications . Briefly , batches of 30 adult ( 3–4 days after emergence ) female An . albimanus mosquitoes were placed in feeding boxes , starved overnight , and then fed directly on P . vivax positive volunteers for 10 minutes . Fed mosquitoes were transported in cages to secure infection rooms and kept under strict security and laboratory conditions ( constant 80% , humidity and 26°C temperature ) . Mosquito infections were evaluated seven days after DFA by microscopic examination of midguts ( DNA was extracted from 30 midguts of individual mosquitoes to determine the number of parasites present in each sample by qPCR ) and oocyst counts , as well as by RT- qPCR seven days after DFA , additionally sporozoite loads in salivary glands were assessed 14 days after DFA . MFA was performed in two mosquito colonies located in Buenaventura , and Tierralta two sites with local malaria transmission . Infected blood samples were sent at 37°C from endemic areas to the nearest mosquito colony . Blood samples were centrifuged at 3000 rpm to separate iRBCs from plasma , were washed twice with incomplete RPMI and subsequently reconstituted with a pool of AB+ sera obtained from healthy donors at the Red Cross Blood Bank ( Cali , Colombia ) . A total of ~100 adult female An . albimanus mosquitoes ( 3–4 days after emergence ) were subjected to overnight fasting and the next day were placed into feeding and cages ( 10x10x5 cm ) provided with an glass feeder ( Ø 3 cm ) with and artificial membrane . Mosquitoes were allowed to feed for 20 minutes at 37°C after which , unfed mosquitoes were removed from the cages and fed mosquitoes were transported to secure infection rooms as described above in the DFA section . For each MFA , the amount of blood required was calculated considering that each mosquito consumes ~3 . 5 μL of blood per feed . Mosquito infection was evaluated by light microscopy to assess the number of oocysts in mosquitoes midguts , after staining with 2% mercurochrome . Infection was evaluated on day seven and sporozoites in salivary glands on day 14 after MFA . Microscopic counts were compared with those of qPCR individually . First , each mosquito midgut was microscopically evaluated , then the midgut was quantitatively transferred to a micro-centrifuge tube for individual DNA extraction . DNA was extracted using the PureLink Genomic DNA kit ( Invitrogen , CA ) using the tissue protocol with 12 hours of proteinase K digestion . PCR was performed using Taqman probes as was described [12 , 43–45] . In order to assess the influence of mosquito age on infectivity , MFAs were performed using three different mosquito age groups: 2 , 4 and 8 days after emergence . Likewise , the influence of mosquito density was tested by using mosquito groups of 50 , 100 , 200 and 300 per cage . Additionally , the influence of the delay to perform the MFA after blood draw was tested at different time intervals: 0 , 4 , 8 and 24 hours after donors’ bleeding . In this case ~130 μL of blood were collected for both exflagellation and gametocyte maturation analysis and MFA were performed using four day-old mosquitos at a 100 mosquito/cage density . A total of 24 independent assays varying the incubation time were performed with different P . vivax isolates . For this part of the study the sample collection and feeding where performed at insectaries we have developed in endemic areas , in this case in Tierralta ( Cordoba ) at the same building of the malaria clinic . The time 0 indicates and assays performed within the first 30–45 min after blood collection . To study the influence of parasite sexual stages maturation on mosquito infectivity , the mRNA expression profiles of 18S , Pvs 25 ( stage V ) , Pvs16 ( stage I-IV ) , PvNeK ( microgametocytes ) and PvMap ( macrogametocytes ) were assessed by RT-qPCR ( n = 42 ) . Whole blood samples were stored in RNA stabilizing solution ( Tempus ) at -20°C until analysis . The RNA was purified using affinity columns ( Qiagen , Hilden , Germany ) and cDNA was transcribed using Superscript III ( Invitrogen , Carlsbad , CA ) according to manufacturer’s instructions [46] . Transcripts were evaluated using SYBR green as previously described[47] . Sera collected from parasite donors were studied to determine the plasma levels of IL-2 , IL-4 , IL-6 , IL-10 , TNF-α and IFN-γ using the Cytometric Bead Array ( CBA , catalog No . 551809 BD Biosciences Pharmingen , USA ) ( n = 24 ) according to manufacturer’s instructions . The infected blood samples were drawn in EDTA tubes and the plasma was aliquoted for analysis . Ten samples from non-infected volunteers were used as baseline cytokine levels . The samples were evaluated in duplicate and in a FACS Canto-II flow cytometer . Standard curves for each cytokine were generated and the concentration calculated using the BD FCAP Array Software v 1 . 0 . 1 ( BD Biosciences ) .
A total of 94 P . vivax infected blood samples were collected ( 24 from Buenaventura and 70 from Tierralta ) and distributed as follows: 70 samples to determine vector factors and 24 samples to determine parasite factors and comparing MFA vs DFA as part of the host factors study . Most of the participants ( 57 . 3% ) were female and the population was predominantly young adults , 65 . 4% between 18 and 35 years of age ( S1 Table ) .
In this study we found that human , parasite and mosquito factors ( i . e . cytokine levels , gametocyte markers , time between the blood draw ) play a fundamental role in P . vivax infectivity , thereby affecting the MFA outcome . In contrast to other vaccine candidates where the vaccine efficacy is tested in vivo , TB vaccine efficacy relies on the MFA to assess the ability of immune responses to reduce or block parasite transmission to the mosquito . Whereas with P . falciparum MFA can be easily performed with cultures of well-characterized parasite strains/clones , for P . vivax these assays are more labor intensive as they rely on the availability of parasite infected blood from acutely infected patients , introducing significant variability . In this study a total of 94 P . vivax field isolates were used to optimize an assay to evaluate TB immunity . High levels of the regulatory cytokines IL-10 , IFN-γ and TNF were correlated with low parasite infectivity . This T-cell immune response explains the infectivity outcome in the study groups . According to previous studies , fever/chills have a deleterious effect on the parasite , most likely associated with cytokine release . The gametocyte density , measured as Pvs25 gene expression , was highly related with the parasite infectivity . In spite all the samples were positives for Pvs25 , the samples with high copy numbers were the most infectious , furthermore , statistically significant differences were observed with PvsNek-4 , indicating that the gametocyte density and maturation status are correlated with parasite infectivity . We found a negative correlation between the levels of IFN-γ , IL-10 and TNF and the Pvs25 expression , indicating that the immune response may have a bigger impact on the viability of gametocytes . The oocyst determination by using qPCR showed a high proportion of 18S DNA copies detected for each oocyst showing the multinucleated nature of the oocysts . In addition , the oocyst size , ranging between 5–50 μm [51] , could be able to carry high numbers of haploid sporozoites before the rupture . Interestingly the use of PCR presents the infection load as potential sporozoites released , which could be a more accurate measure of the transmission potential . The maximum infectivity level was achieved 4 hours after blood-draw , coinciding with increased expression of Pvs25 . Of the total samples that infected mosquitoes , only 31% showed a similar infection rate and total oocyst count at 4h and 8h . This could be due to gametocyte maturation boosted by stress and nutrients decrease at 8h after blood drawn . These results indicate that parasite infectivity is time-limited and supports the use of TB assays ( TBAs ) performed no later than 4 hours after blood draw in order to obtain reliable results . This requires the laboratory mosquito colony be within 4 hours travel-time from the endemic areas . Infections of Anopheles mosquitoes by DFA are considered the gold standard for P . vivax infectivity due to the lack of in vitro culture . However , most of the reported studies have been performed using only MFA . In this study we assessed parasite infectivity using both to investigate the influence of human factors . We found no significant differences between DFA and MFA mosquito infection rate . However , oocyst intensity was significantly higher for MFA , indicating a possible role of cytokines in parasite maturation at the mosquito level . Even though the high correlation between DFA and MFA would suggest that either of the two techniques can be used to measure TB , they also have applications for which one would be better than the other . In general terms DFA would better indicate TB in the host i . e . the parasite transmissibility in the presence of antibodies , cytokines and human host cells . On the other hand , MFA would be highly complementary in allowing a separate assessment of specific TB responses , and parasite factors associated with transmission i . e . parasite maturation , role of macro and microgametocyte density and proportion , parasite diversity and others[12] . We optimized the variables to develop an assay to assess TB immunity against P . vivax . This assay could be used for implementation of TBA in appropriate regions of Latin America and to assess the potential of current TB vaccine candidates .
|
Here we assessed the host , vector , and parasite factors for membrane feeding assay using Plasmodium vivax and Anopheles albimanus mosquitoes and samples from endemic regions of Colombia . This membrane feeding assay method allowed more efficient assessment of the presence of TB activity in sera of individuals from malaria endemic regions as well as in sera of immunized animals and humans . This method can contribute to better assess the TB immune responses elicited by natural infection as well as further evaluation of protective efficacy of malaria TB vaccines .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"parasite",
"groups",
"oocysts",
"plasmodium",
"gametocytes",
"tropical",
"diseases",
"parasitic",
"diseases",
"animals",
"parasitic",
"protozoans",
"parasitology",
"germ",
"cells",
"bacterial",
"diseases",
"apicomplexa",
"protozoans",
"insect",
"vectors",
"infectious",
"diseases",
"malarial",
"parasites",
"animal",
"cells",
"tuberculosis",
"epidemiology",
"disease",
"vectors",
"insects",
"arthropoda",
"mosquitoes",
"cell",
"biology",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"malaria",
"organisms"
] |
2016
|
Optimization of a Membrane Feeding Assay for Plasmodium vivax Infection in Anopheles albimanus
|
For sensory signals to control an animal's behavior , they must first be transformed into a format appropriate for use by its motor systems . This fundamental problem is faced by all animals , including humans . Beyond simple reflexes , little is known about how such sensorimotor transformations take place . Here we describe how the outputs of a well-characterized population of fly visual interneurons , lobula plate tangential cells ( LPTCs ) , are used by the animal's gaze-stabilizing neck motor system . The LPTCs respond to visual input arising from both self-rotations and translations of the fly . The neck motor system however is involved in gaze stabilization and thus mainly controls compensatory head rotations . We investigated how the neck motor system is able to selectively extract rotation information from the mixed responses of the LPTCs . We recorded extracellularly from fly neck motor neurons ( NMNs ) and mapped the directional preferences across their extended visual receptive fields . Our results suggest that—like the tangential cells—NMNs are tuned to panoramic retinal image shifts , or optic flow fields , which occur when the fly rotates about particular body axes . In many cases , tangential cells and motor neurons appear to be tuned to similar axes of rotation , resulting in a correlation between the coordinate systems the two neural populations employ . However , in contrast to the primarily monocular receptive fields of the tangential cells , most NMNs are sensitive to visual motion presented to either eye . This results in the NMNs being more selective for rotation than the LPTCs . Thus , the neck motor system increases its rotation selectivity by a comparatively simple mechanism: the integration of binocular visual motion information .
The fly provides us with the opportunity to apply an integrated systems approach . Quantitative analysis of distinct behaviors may be combined with electrophysiology , neuroanatomy , neurogenetics , and computational modeling to study the neural basis of behavior [6–9] . Minimal dissection is required to access the fly visual system , allowing electrophysiology to be conducted with the neural circuitry intact . Lesion and neurogenetic experiments conclusively show that a population of individually identified interneurons in the fly's third visual neuropil , the lobula plate , are key to optomotor behavior and visual gaze stabilization [10–13] . These lobula plate tangential cells ( LPTCs ) selectively integrate local motion signals provided by retinotopically arranged arrays of directional-selective small field elements . Two LPTC subgroups , ten VS cells ( vertical system [14] ) and three HS cells ( horizontal system [15] ) , in either side of the brain are major output elements of the lobula plate [16] . The receptive field maps of each of the VS and HS LPTCs show marked similarities to the panoramic retinal image shifts or “optic flow fields” generated during rotations of the fly about different body axes ( Figure 1 ) [17 , 18] . This has been taken to suggest that the LPTCs encode information about the fly's self-rotation [17 , 18] . More-recent experiments have shown that LPTC responses to naturalistic stimuli contain information about both translation and rotation of the fly [19 , 20] . For gaze stabilization only rotation information is important , whereas translation information may be used for tasks such as distance estimation . LPTCs convey visual information to neck motor neurons ( NMNs ) using direct connections and also indirectly via descending neurons [21] . NMNs combine the LPTC inputs with those from other sense organs to control head rotations that keep the retinal image level when the body rotates [22] . Because NMNs are concerned with correcting for body rotations , not translations , they must separate out the rotation-induced components of the LPTC responses from those induced by translational self-motion . The neural mechanisms underlying this task are currently not very well understood . In a pioneering study , Milde et al . [23] showed that , like the LPTCs , some NMNs respond to visual motion in a directional-selective manner . In addition , whole-nerve stimulations suggest that the directional preferences of NMNs within a given nerve are appropriate for controlling the muscles they innervate [24 , 25] . Previous studies , however , did not address the question of how the self-motion parameters encoded across the population of LPTCs are used by the neck motor system . We mapped the receptive fields of NMNs in the blowfly ( Calliphora vicina ) to characterize the transformation occurring between the LPTC and NMN populations .
Here we show example NMN receptive field maps that illustrate the range of different map types obtained from each nerve ( Figures 3–5 ) - population data are shown later ( Figures 6 , 7 , S1 and S2 ) . The receptive field maps consist of local vectors plotted against azimuth and elevation . The azimuth and elevation indicate the horizontal and vertical angular position of a motion stimulus within the fly's visual field . The fly was oriented so that it was facing zero degrees azimuth and elevation . The orientation and length of each local vector in the receptive field maps indicate the NMN's local preferred direction of visual motion and relative motion sensitivity , respectively . All receptive field maps , except where noted , are plotted with respect to recordings from the left part of the nervous system . Maps derived from NMN recordings in the right part of the nervous system are mirror-transformed . To distinguish between the different examples we present , we have labeled them , e . g . , FN NMN A , FN NMN B , etc . This naming convention is for convenience only and should not be taken to imply that the data come from any particular identified neuron . The FN is the biggest of the four neck motor nerves . NMNs running through this nerve innervate a variety of different neck muscles that , based on their anatomy [25] , could potentially be involved in head inclination ( nose-up pitch ) , declination ( nose-down pitch ) , unilateral adduction ( yaw ) , and unilateral depression ( roll ) . FN-innervated muscles may potentially also be involved in head extension/retraction—the only possible translational degree of freedom of the head [25] . Milde et al . [23] found that FN neurons were tuned to a variety of directions in the frontal visual field and to downward visual motion laterally—from this they concluded that FN units were sensitive to roll . Using our receptive field mapping approach , we were able to determine which self-motions the FN units were most sensitive to with a higher degree of accuracy . We found FN units with receptive fields that appear to be tuned for pitch , roll and pitch-roll intermediates . Our first FN NMN example , FN NMN A ( Figure 3A ) , had a bilaterally symmetric receptive field with strong motion sensitivity in areas above the eye equator . In the frontal visual field , this NMN was most sensitive to vertical upward motion . Its directional preferences gradually became horizontal within the lateral visual field and eventually turned into a preference for almost vertical downward motion in the caudolateral visual field . The directional preferences were oriented along concentric circles around an area of low sensitivity at roughly ±90° azimuth . Such sensitivity minima , or singularities , within the receptive fields indicate the orientation of the neuron's “preferred rotation axis” , i . e . the axis about which the animal has to turn in order to most strongly activate the neuron . The rotation axis that this NMN preferred nearly coincides with the animal's transverse body axis , suggesting that FN NMN A would respond most strongly when the fly performs a nose-down pitch rotation . FN NMN B ( Figure 3B ) responded most strongly to oblique upward motion in the frontal visual field above the eye equator and was slightly less sensitive to motion in the right visual hemisphere . The global appearance of this NMN receptive field was similar to that of the VS8 LPTC [17]: its preferred directions being oriented around a singularity at an azimuth of roughly −70° . Thus , the preferred rotation axis of FN NMN B lay between the longitudinal and transverse body axes , indicating that this NMN would probably respond best to nose-down pitch in combination with a slightly smaller roll component . Simultaneous recordings were often made from multiple FN NMNs with receptive fields similar to FN NMN B ( Figure 3B ) . FN NMN C ( Figure 3C ) had a binocular receptive field that covered the entire area tested in our experiments . The receptive field resembled an optic flow field generated during roll-rotation in combination with a subtle nose-down pitch . Accordingly , the NMN's preferred rotation axis was close to the fly's longitudinal body axis but slightly tipped downward frontally . The VCN hosts three NMNs innervating different oblique horizontal ( OH ) muscles . These muscles were suggested to be involved in the control of yaw rotations of the head [25] . VCN NMNs ( Figure 4A ) were recorded from the OH muscle group . They responded to motion across an area that covered the ipsilateral visual hemisphere . In contrast to most other NMNs , the VCN NMNs did not appear to receive contralateral input beyond the region of the eye's binocular overlap [26] . Yaw rotation of the fly results in horizontal motion in the same direction across both eyes , whereas forward translation motion of the fly results in horizontal motion in opposite directions either side of the focus of image expansion . Therefore , to distinguish between yaw rotation and translation , a neuron needs a binocular receptive field . In contrast to most of the other NMN receptive fields , the VCN NMN receptive field was mostly monocular . This type of NMN may therefore respond to both yaw rotation and translation of the fly . Besides potentially controlling yaw head movements , such NMNs may also be involved in the control of head retraction . In principle the oblique horizontal muscles which VCN NMNs innervate [25] are suited to serve this function when simultaneously activated on either side of the neck motor system . The ADN contains only two motor neurons , both of which supply the transversal horizontal ( TH ) muscles involved in rotations of the head around the vertical axis , i . e . , yaw [25] . ADN NMN A ( Figure 4B ) was recorded from the TH muscle group . It had the smallest receptive field of all the NMNs presented in this account . The extent of the receptive field nearly reached 90° along the azimuth , and the neurons' sensitivity dropped sharply toward the dorsal and ventral visual field . Other ADN receptive fields , such as ADN NMN B ( Figure 5A , also recorded from the TH muscle group ) , covered the entire area of the visual field examined . In a manner reminiscent of the HSE LPTC receptive field ( Figure 5B ) , such ADN NMNs showed the highest sensitivity to horizontal motion along the entire eye equator ( Figure 5A ) . Their motion sensitivity decreased toward both the dorsal and the ventral parts of the visual field . The singularity within this receptive field , though difficult to see in the cylindrical projection , lay at about +45° azimuth and +75° elevation , suggesting that this type of NMN would respond strongly during yaw rotation of the animal to the right . The CN contains several neck motor neurons that , among others , innervate muscles involved in head declination [25] . Some of the units recorded from the CN were only sensitive to vertical downward motion in a very small portion of the frontal visual field ( unpublished data ) . Because of their small receptive fields , the tuning of these units to self-motion could not be determined . We therefore excluded these units from the current analysis . Wide-field CN NMNs were particularly sensitive to vertical downward motion in the frontal-to-frontolateral aspect of the contralateral visual field with a singularity at an azimuth of about −45° ( Figure 5C ) . The finding that the CN NMNs respond most strongly in the contralateral field is in agreement with Milde et al . [23] . The CN NMNs were noticeably similar to the contralateral VS3 LPTC ( Figure 5D ) [17 , 27] . The distributions of the preferred directions and motion sensitivities within the CN NMN resembled optic-flow fields generated during nose-up banked turn to the left . The preferred rotation axis of a neuron is a convenient parameter to compare how the LPTC and NMN populations encode and control self-rotations . The preferred rotation axis is the body axis about which the fly would have to rotate to maximally excite a given neuron [28] . We quantitatively estimated the preferred rotation axes of all 47 NMNs studied and a group of 28 LPTCs for which the binocular receptive field maps had previously been obtained [27 , 29] . This was done by finding for each LPTC and NMN the rotation axis the animal would have to turn around to generate an optic flow field best matching the cell's receptive field map ( see the Materials and Methods section ) . A second set of axes was obtained in the same manner from the mirror transformed NMN and LPTC receptive fields to account for the LPTCs and NMNs on the other side of the nervous system . This duplication of the dataset was based upon the well-supported assumption that bilateral symmetry exists in the LPTC and NMN populations [16 , 25] . Figure 6 shows the preferred rotation axes of the LPTCs and NMNs in blue and red , respectively . Each arrow in Figure 6B corresponds to the axis about which a fly would have to rotate clockwise to maximally stimulate a given neuron . For comparison , Figure 6A shows a fly oriented in the same coordinate system . To show the preferred rotation axes on all sides of the sphere , we also plotted the axes against azimuth and elevation in a 2D cylindrical projection ( Figure 6C ) . The axes in the dorsal and ventral pole region of the cylindrical projection appear to be more scattered than they are in the spherical presentation ( Figure 6B ) . This distortion is due to the reduction of dimensions when transforming the data from the 3D spherical representation to a 2D cylindrical projection . To quantify the relationship between the LPTC and NMN preferred rotation axes , we binned the two axis distributions into equi-angular bins ( 15° in azimuth and elevation at the equator ) and then performed a normalized spherical cross-correlation [30] between the two ( Figure 6D ) . The color values in Figure 6D indicate the correlation coefficient , r , between the binned NMN and LPTC preferred axes at different relative rotations between the two sets of axes . All possible combinations of the three rotational degrees of freedom ( α , β , and γ ) were tested with a resolution of 15° . There was a clear peak in the cross-correlation function at zero rotation ( Figure 6D ) , indicating that there was no systematic rotation between the NMN and LPTC preferred axes . At zero rotation , the correlation coefficient between the binned NMN and LPTC axes was r = 0 . 57 . Thus , almost a third ( coefficient of determination r2 = 32% ) of the variation in the measured NMN preferred rotation axes was related to the preferred axes of the LPTC population . The most obvious difference between the receptive fields of many NMNs and LPTCs is that , while the VS LPTCs tended to have monocular receptive fields [17 , 27] , most NMNs responded to motion in both visual hemispheres . We quantified the degree of binocularity for each receptive field ( Figure 7 ) by taking the ratio between the mean motion sensitivity in the left and right hemispheres ( see the Materials and Methods section ) . The resulting binocularity ratio was 0 . 6 on average for the NMNs compared to 0 . 32 for the LPTCs . This increase in binocularity reflects a statistically significant difference between the two populations ( p < 10−8 , Student's two-sample t-test ) . Theoretical analysis has shown that sampling both visual hemispheres enables a system to distinguish more accurately between the rotation- and translation-induced components of optic flow [31] . We investigated whether the increased binocularity of the NMNs was also accompanied by a higher rotation-selectivity than found for the more monocular LPTCs . To do so , we quantitatively estimated for each neuron its rotation selectivity ratio , which indicates the preference for rotation over translation based on the neuron's receptive field organization ( see Materials and Methods ) . The rotation selectivity ratio ranges from 0 to 1 , where a ratio of 0 indicates the neuron was selective for only translation , 1 indicates selectivity for pure rotation , and 0 . 5 results from an equal selectivity to rotation and translation . For both the NMNs and the LPTCs , the more-binocular receptive fields tended to have a higher selectivity for rotation ( Figure 7 ) . On average , the NMNs were more rotation-selective than the LPTCs . The LPTCs had a median rotation selectivity of 0 . 55 compared to a value of 0 . 65 for the NMNs . We found this 18% increase in rotation selectivity to be statistically significant ( p < 0 . 01 , Mann-Whitney U test ) . When the NMN receptive fields were artificially made to be monocular by setting the motion sensitivity on their weakest side to zero , the distribution of rotation selectivities was no longer statistically different from those of the LPTCs ( p > 0 . 7 , Mann-Whitney U test , median “monocularized” NMN rotation selectivity = 0 . 58 ) . This suggests that the increased binocularity seen in the NMNs leads them to be more selective for rotation than the LPTCs .
The NMN and LPTC receptive fields differ in how sensitive to motion they are at different positions in the visual field . In particular , most NMNs display a strong degree of binocularity , whereas their input elements , the LPTCs are either mostly monocular or weakly binocular [17 , 27 , 29] . This is especially true of those NMNs that receive LPTC inputs indirectly via descending neurons ( FN and ADN NMNs [21] ) , possibly reflecting a high degree of binocular integration at the level of descending neurons . Such binocular integration has recently been found in at least one descending neuron that provides input to NMNs [32 , 33] . In particular , the visual response properties of the descending neuron DNVOS2 [33] are similar to some of the FN receptive fields we obtained ( Figure 3C ) . Binocular information is required to distinguish between the optic flow resulting from certain rotations and translations . Thus , by appropriately combining LPTC inputs to generate binocular receptive fields , NMNs can “read out” rotation information from the LPTC population while reducing the influence of translation-induced signals ( Figure 7 ) . This comparatively simple mechanism may be facilitated by some LPTCs already having receptive fields and therefore preferred axes of rotation similar to those the NMNs require to control head rotations ( Figures 5 and 6 ) . Such a straightforward mechanism would explain why the circuitry connecting LPTCs to NMNs is comparatively simple and direct [25] , contributing to fast gaze stabilization responses [22] . Our data provide an extensive survey of the NMNs that are sensitive to wide-field motion . However , it is possible that some visually responsive NMNs may have been missed in this study . For example , no NMN receptive fields similar to those of the VS5–6 LPTCs were found . The reason for this could be that no such NMNs exist , that we did not record from them due to a sampling bias , or that the visual responses of such neurons are gated by another sensory modality [34] . It is also possible that we may have recorded from NMNs with receptive fields similar to the LPTCs more often , while missing NMNs with very different receptive fields . If true , this would have artificially strengthened the correlation we find between the preferred axes of the LPTC and NMN populations ( Figure 6D ) . We have , however , sampled units from all neck nerves ( Figures 3–5 ) . Furthermore , in some nerves we found multiple types of receptive fields , strongly suggesting that we have recorded from multiple types of neurons . In some cases , we have obtained simultaneous recordings from multiple units with similar receptive fields , indicating that we have been sampling separate units even among those with similar response properties . Thus , we are confident that the data are a thorough representation of the subpopulation of NMNs that are sensitive to wide-field motion . In Figure 6 , we describe the NMN and LPTC populations in terms of their preferred axes of rotation ( see the Materials and Methods section ) . This is not a complete description of the cells' responses to self-motion , because the LPTCs [19 , 20] , and possibly some NMNs ( Figures 4A and 7 ) , can also respond to translation-induced optic flow . However , as the neck motor system mainly compensates for rotations , it is reasonable for the purposes of this study to focus on the rotational component of the NMN and LPTC receptive fields . Furthermore , the preferred axis of rotation does not appear to be affected by translation . Karmeier et al . [28 , 35] have shown that the preferred rotation axis estimated from VS LPTC receptive fields is in agreement with that obtained through wide-field stimulation of the same neurons , even when rotation and translation are superimposed . In describing the coordinate system made up of the NMN preferred rotation axes ( Figure 6 ) , we now have a more complete picture of the visuomotor transformation that takes place in the gaze-stabilization system of the fly . However , the NMN coordinate system we describe is based on the visual receptive fields of the NMNs , not the pulling planes of the muscles they innervate . And it is the pulling planes of the muscles that represent the final stage of the visuomotor transformation . Gilbert et al . [24] , on the other hand , monitored head movements resulting from whole-nerve stimulation . Their results suggest a direct relationship between NMN preferred axes of rotation and muscle pulling planes , though this is not necessarily the case in other systems [5 , 36] . In a previous pioneering study , Milde et al . [23] investigated the visual response properties of NMNs in the context of the anatomical organization of the neck motor system [25] . While our results on the local motion preferences of the neck motor neurons are generally compatible with those of Milde et al . [23] , there are two decisive differences: ( i ) Milde et al . did not obtain the complex receptive field organization of the NMNs , limiting themselves to a description of each neuron as tuned to horizontal or vertical motion , and ( ii ) for many of the NMNs , they did not report the contralateral input that we find . Here we provide a quantitative description of how the receptive fields of NMNs are adapted to specifically control certain head rotations , which significantly adds to the previous level of understanding [23] . The detailed knowledge about the receptive field organization of the NMNs , including their estimated preferred rotation axes , enables us to make specific predictions about the functional organization of the neck motor system which can be tested in future experiments . Based on the NMNs' and LPTCs' receptive field organization , future theoretical studies may be able to predict the connections between these two neural populations . Furthermore , our estimate of the motor neurons' preferred rotation axes should provide some additional guidance when investigating the actual pulling planes of the neck muscles . Directional motion information is acquired locally in retinal coordinates defined by the orientation of the hexagonal rows of the ommatidial eye lattice [8 , 37] . At this stage , the information is ambiguous with respect to self-motion , because different self-motions can result in the same direction of local motion [17] . VS LPTCs resolve this ambiguity by integrating local motion information from across one entire eye so that each neuron is broadly tuned to a specific axis of rotation [4 , 17 , 18] . Many of the resulting LPTC receptive fields are already similar to those required by the NMNs . Thus , the visual system LPTCs play a key role in the visuomotor transformation in that they convert local sensory information into signals related to self-motion , in this case rotation , which can immediately be used by various motor systems . However , the outputs of LPTCs are still partially ambiguous with respect to self-motion . LPTCs mostly respond to monocular inputs , but binocular motion information is needed to unambiguously distinguish certain rotation and translation components [31 , 38] . The NMNs perform this final step by integrating LPTC inputs from either side of the brain . We conclude that the fly gaze-stabilization system is an excellent example of task-specific processing of visual motion information that results in a simplified sensorimotor transformation .
We mounted female 1–3-d-old blowflies ( C . vicina ) from the Department of Zoology , University of Cambridge , either dorsal or ventral side up on custom-made holders . The wing bases were waxed and the legs and wings removed . The resulting wounds were sealed with beeswax to reduce fluid loss . We aligned the orientation of the fly's eye with the center of the visual stimulus apparatus according to the deep pseudopupil [39] , and then fixed its head in position with beeswax . The ocelli were obscured with nontoxic acrylic black paint . In those experiments where the fly was mounted ventral side up , we cut a small window in the neck or thorax cuticle exposing the neck nerve to be studied . Two hook electrodes constructed from 0 . 025-mm-diameter silver wire were placed under the neck nerve of interest . Haemolymph was temporarily removed from the recording site and replaced with a mixture of petroleum jelly and paraffin oil . The tissue was kept moist with fly saline [15] . In those experiments where the fly was mounted dorsal side up , we used the same methods except that we placed the hook electrodes under neck muscles instead of a nerve , so we could record from NMN axon terminal arborizations . Figure 2C shows an example of the characteristic signal structure we obtained in such recordings: a fast NMN action potential was reliably followed in a 1:1 fashion by a slower biphasic potential , most likely the induced neck muscle potential . In all , we recorded from 47 NMNs in 38 flies that responded to visual motion over an area wider than 90° along the azimuth . Signals from the hook electrodes were amplified 3000× by a Brownlee Precision amplifier Model 440 operating in differential AC mode . A PC-controlled National Instruments PCI-6025E data acquisition board sampled the amplifier output at 10 kHz . Software for stimulus control , data acquisition , spike sorting by template matching , and data analysis was programmed in Matlab ( Mathworks ) . Visual stimuli were presented on a green cathode ray tube ( CRT , P31 phosphor ) driven by an Innisfree Picasso image synthesizer at a refresh rate of 182 Hz . We placed the CRT 7 . 4 cm or 18 . 5 cm from the fly so that the circular screen aperture subtended a visual angle of 62 . 6° or 27 . 3° . Depending on the visual responsiveness of the unit being studied , one of two different types of visual stimuli was used to obtain directional tuning curves at 66 different positions within the NMNs' receptive fields . For NMNs that were highly sensitive to local visual motion , we used a procedure introduced by [40] . In brief , a black dot ( 7 . 6° diameter ) was moved on a circular path ( 10 . 4° diameter ) across a green background ( 96% contrast ) completing 6 cycles at a speed of 2 cycles/s . By traveling on a circular path , this stimulus covered all possible directions of visual motion . Correlating a unit's change in spike rate with the direction of dot movement revealed the local directional tuning curve . See [40] for further details . If the recorded unit did not produce a robust response to the dot stimulus , we used square wave visual gratings instead . The gratings had a 96% contrast , spatial wavelength of 10° and were moved perpendicular to their orientation with a temporal frequency of 5 Hz across the full extent of the 62 . 6° diameter screen . The grating was moved in 16 different directions with a spacing of 22 . 5° , presented in a pseudo-random order . Before each motion stimulus , a blank screen of the same mean luminance as the grating ( 18 cd/m2 ) was shown for 5 s and the neuron's baseline spike-rate recorded . In these experiments , we defined the response to a grating as the mean spike rate during a 1-s stimulus presentation minus the baseline spike rate . As an example , Figure 2A and 2B show the response of a CN NMN to grating motion in opposite directions . Plotting the responses to visual motion against the 16 different directions of motion revealed the neuron's local directional tuning curve . From the tuning curve , we could estimate the local preferred direction and motion sensitivity by finding the phase and amplitude of the fundamental harmonic in a fast Fourier transformation of the tuning curve ( Figure 2D ) . The CRT was mounted upon a semicircular frame that allowed it to be moved to different positions within the fly's visual field . The CRT position could range from −120° to +120° in azimuth and −70° to +75° in elevation . We obtained directional tuning curves in a pseudo-random order at different positions within the fly's visual field . For elevations 15° and −15° relative to the horizontal plane of the stimulus apparatus , we presented visual stimuli with 15° spacing along the azimuth . For elevations of 45/ −45° and 75/ −70° , we used an azimuthal spacing of 30° and 45° , respectively . Once we had determined the local preferred direction and local motion sensitivity for a unit at multiple locations within the visual field , we plotted them in vector field maps of the visual field where each position is defined by its azimuth and elevation . The orientation and length of each vector indicate the neuron's local preferred direction and motion sensitivity at each point in the visual field . Figure 2E and 2F show the receptive field map for an individual CN neck motor neuron as obtained with the dot method and the grating method , respectively . The boxed arrow in Figure 2F is derived from the tuning curve in Figure 2D . While the global distribution of directional motion preferences looks similar in the maps obtained with the dot method and the grating method , there are two differences at the local scale: first , in the grating map , the position-dependent changes of local preferred directions appears to be more smooth , and second , the area of higher local motion sensitivity seems to be extended ( Figure 2E and 2F ) . Both of these differences are to be expected , since the grating stimulus covers a larger visual angle and therefore the upstream cells will be integrating signals over more directional selective input elements , which results in a general smoothing of the receptive field map . Despite these differences in detail , both methods produced similar results with respect to the neuron's estimated preferred rotation axis ( see below for details on the estimation procedure ) . In all receptive field maps presented , black arrows show experimentally determined results whereas gray arrows result from interpolation . The spline interpolation method used is described by [41]; it makes no assumptions other than that the transitions between data points are smooth . The lowest position at which the CRT could be held was −70° in elevation , whereas the highest position was 75° . Thus , a vertical asymmetry exists between maps taken from flies mounted dorsal and ventral side up . To allow the comparison of receptive fields obtained in different experiments , this asymmetry was overcome by performing a 5° extrapolation [41] on the data taken at an elevation of −70° . Consequently , instead of plotting the data obtained at −70° elevation , we plot extrapolated data at −75° elevation . Receptive field maps taken from flies mounted ventral side up were flipped vertically to allow comparison with maps taken from flies mounted dorsal side up . The receptive fields we describe range from monocular , i . e . , only responding to motion in one visual hemisphere , to fully binocular , i . e . , responding to visual motion across both hemispheres . To describe the degree of binocularity of each neuron , we computed a “binocularity ratio” for each neuron's receptive field . For each neuron , we computed the mean motion sensitivity in the left and right visual hemispheres . The binocularity ratio is simply the smaller of the two mean motion sensitivities divided by the larger . A value of 1 means that the neuron was equally sensitive to motion in both visual hemispheres , a value of 0 means that the neuron only responded to motion in one hemisphere . All the binocularity ratio data passed the Lilliefors test for normality , so parametric statistics are used to describe these data . The NMN and LPTC binocularity ratio data sets did not pass an F-test for equal variances , so we used Student's 2-sample t-test with a correction for unequal variances to compare the two data sets . To quantitatively estimate each neuron's selectivity for rotational versus translational motion , we applied the iterative least-square algorithm developed by Koenderink and van Doorn ( KvD [38] ) to each neuron's receptive field map . This procedure parameterizes each neuron's receptive field by its preferred self-rotation and translation components vectors R and T; similar approaches have been used previously for the LPTCs [29] . The magnitudes of the two components of the parameterization , |R| and |T| , give the relative contribution of the rotation and translation parameters when calculating an optic flow field that best matches a neuron's receptive field . Thus , |R| and |T| can be thought of as representing how much of the receptive field can be explained by rotation and translation respectively . We used the magnitude of the KvD rotation and translation components to calculate the proportion of the receptive field that was explained by the rotation component: A rotation selectivity ratio of 0 means that we estimate the neuron only responds to translation-induced optic flow , 0 . 5 means we estimate the neuron responds to translation and rotation equally , 1 means the neuron only responds to rotation . The rotation selectivity ratio data sets did not pass the Lilliefors test for normality , so we use nonparametric statistics to describe these data . Binocular receptive field data were used for all calculations . For all metrics , we only used data from receptive field positions where data were available for both the LPTCs and NMNs . The receptive field maps allow us to compare the visual responses of individual cells . To compare the entire coordinate system used by the VS and HS cells for encoding self-rotation to the coordinate system used by NMNs for controlling head rotation , we estimated each cell's preferred axis of rotation . This was done by comparing a cell's receptive field to optic flow fields generated by rotations about different axes . The rotational flow fields were computed using the formalism described by Koenderink and van Doorn [38] . The local motion vectors making up a given optic flow field ( P ) were projected onto the local preferred directions of the cell's receptive field ( U ) . The resulting values were weighted by the cosine of their elevation θj to compensate for the over-sampling of high and low elevations and then summed across all locations j , where N is the total number of local preferred direction measurements . The resulting value s is a measure of the similarity between the cell's receptive field and the rotational optic flow field [29]: We repeated this procedure for axes of rotational optic flow across the entire sphere with a spacing of 1° between axes tested . We defined the cell's “preferred axis of rotation” as the axis of self-rotation that resulted in optic flow most similar to the cell's receptive field , and therefore the largest value of s . This definition is based on the assumption that the more similar an optic flow field is to a unit's receptive field map , the stronger the unit's response to the optic flow field will be [28 , 35] . Using this method , we obtained a preferred axis for all LPTCs and NMNs . The preferred axis is defined by just two angles , its azimuth and elevation , allowing easy comparison of a large number of cells simultaneously .
|
Many behavioral tasks rely on sensory information . This information , however , needs to be transformed into a format that is compatible with the requirements of motor systems . In this study we characterize the neural basis of such a sensorimotor transformation in a model system . Flies , like humans , stabilize their gaze to keep their eyes level , even when the body is rotating . An identified population of fly brain neurons called lobula plate tangential cells ( LPTCs ) contributes to this task . These cells analyze the wide-field retinal image shifts generated when the fly is moving relative to its environment . We have characterized the visual receptive fields of motor neurons that use the information encoded by LPTCs to control gaze-stabilizing head movements . Our results suggest that the motor neurons use their LPTC inputs in a comparatively simple and direct way: they combine inputs from both sides of the brain to increase the motor neurons' selectivity for rotations . Such a mechanism enables a specific , fast , and surprisingly simple sensorimotor transformation in which visual information contributes to gaze stabilization .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"neuroscience"
] |
2008
|
Visuomotor Transformation in the Fly Gaze Stabilization System
|
Combining structural proteomics experimental data with computational methods is a powerful tool for protein structure prediction . Here , we apply a recently-developed approach for de novo protein structure determination based on the incorporation of short-distance crosslinking data as constraints in discrete molecular dynamics simulations ( CL-DMD ) for the determination of conformational ensemble of the intrinsically disordered protein α-synuclein in the solution . The predicted structures were in agreement with hydrogen-deuterium exchange , circular dichroism , surface modification , and long-distance crosslinking data . We found that α-synuclein is present in solution as an ensemble of rather compact globular conformations with distinct topology and inter-residue contacts , which is well-represented by movements of the large loops and formation of few transient secondary structure elements . Non-amyloid component and C-terminal regions were consistently found to contain β-structure elements and hairpins .
α-Synuclein is involved in the pathogenesis of misfolding-related neurodegenerative diseases , in particular Parkinson’s disease [1 , 2] . A misfolding event leads to the formation of oligomers which are believed to result in cell toxicity and which eventually lead to the death of neuronal cells [3] . α-Synuclein is thought to interact with lipid vesicles in vivo [4] and the toxicity is thought to be mediated via membrane disruption by misfolded oligomers [5] . Moreover , a prion-like spread of the pathology via the conversion of native α-synuclein molecules by toxic oligomers has been suggested [6] . Native α-synuclein is considered to be an intrinsically disordered protein , although there is evidence that some globular structure exists in solution , which may serve as a basis for understanding the mis-folding and oligomerization pathways . A number of biophysical methods , such as NMR , EPR , FRET , and SAXS—in combination with computational methods—have been applied to the study of intrinsically disordered proteins , including the structure of α-synuclein in solution [7–10] . In all of these cases , even a limited amount of experimental structural data was helpful in the characterization of the conformational ensemble of α-synuclein in solution . Recently , we developed a method for determination of protein structures , termed short-distance crosslinking constraint-guided discrete molecular dynamics simulations ( CL-DMD ) , where the folding process is influenced by short-distance experimental constraints which are incorporated into the DMD force field [11] . Adding constraints to DMD simulations results in a reduction of the possible conformational space and allows the software to achieve protein folding on a practical time scale . We have tested this approach on well-structured proteins including myoglobin and FKBP and have observed clear separation of low-energy clusters and a narrow distribution of structures within the clusters . The conformational flexibility of intrinsically disordered proteins , such as α-synuclein , brings additional challenges to the computational process [12] . In cases like this , proteins exist as a collection of inter-converting conformational states , and crosslinking data represents multiple conformations of a protein rather than a single structure . In addition , recent research indicates that traditional force fields with their parametrization are not ideal for providing an accurate description of disordered proteins , and tend to produce more compact structures [13] . Recently research has been focused on improving traditional state-of-the-art force fields and their ability to predict structures of disordered proteins without losing their accuracy for structured proteins [14] . In this work we use a Medusa force field [15–17] that is utilized in DMD simulations is discretized to mimic continuous potentials . DMD uses a united atom representation for the protein where all heavy atoms and polar hydrogens are explicitly accounted . The solvation energy is described in terms of the discretized Lazaridis-Karplus implicit solvation model [18] and inter-atomic interactions , such van der Waals and electrostatics , are approximated by a series of multistep square-well potentials . Other additional potentials , such as pair-wise distance constraints [19 , 20] and solvent accessibility information [21 , 22] can also be readily integrated . During CL-DMD simulations there are no continuous forces that would drive the atoms to satisfy all constraints , rather generating conformational ensembles , which satisfy an optimal number of the constraints are generated . This , to some degree , naturally resolves conflicting experimental constraints . Thus , CL-DMD simulations are a viable computational platform for the structural analysis of intrinsically disordered proteins [23] in general , and α-synuclein in particular . Here , we used the CL-DMD approach [11] to determine conformational ensembles of the α-synuclein protein in solution . During this process , α-synuclein was crosslinked with a panel of short-range crosslinkers , crosslinked proteins were enzymatically digested , crosslinked residues were determined by LC-MS/MS analysis , and the resulting data on inter-residues distances were introduced into DMD force field as external constraints . To experimentally validate the predicted structures , we analyzed α-synuclein using surface modification ( SM ) , circular dichroism ( CD ) , hydrogen-deuterium exchange ( HDX ) , and long-distance crosslinking ( LD-CL ) .
α-Synuclein was crosslinked with a panel of short-range reagents azido-benzoic acid succinimide ( ABAS-12C6/13C6 ) , succinimidyl 4 , 4'-azipentanoate ( SDA ) , [24] triazidotriazirine ( TATA-12C3/13C3 ) , [25] and 1-ethyl-3- ( 3-dimethylaminopropyl ) carbodiimide ( EDC ) [26] . ABAS and SDA are hetero-bifunctional amino group-reactive and photo-reactive reagents , TATA is a homo-bifunctional photo-reactive reagent , and EDC is a zero-length carboxyl-to-amino group crosslinker . Crosslinked proteins were digested with proteinase K or trypsin proteolytic enzymes , and the digest was analyzed by LC-MS/MS to identify crosslinked peptides ( S1 Table ) . We used an equimolar mixture of 14N- and 15N-metabolically labeled α-synuclein to exclude potential inter-protein crosslinks from the analysis and to facilitate the assignment of crosslinked residues based on the number of nitrogen atoms in the crosslinked peptides and the MS/MS fragments [27] . The distances between crosslinked residues are based on the length of the crosslinker reagents , and were introduced as constraints into the DMD potentials ( see section below and [11] for additional details ) . A total of 30 crosslinking constraints were used in these DMD simulations ( S1 Table ) . In addition , α-synuclein was characterized by top-down ECD- and UVPD-FTMS HDX and CD to determine the secondary-structure content ( Fig 1 and S1 Fig ) . Quantitative differential surface modification experiments were performed with and without 8 M urea to determine the characteristics of the residues as exposed or buried ( S2 Table ) . LD-CL was used to estimate the overall protein topology ( S3 Table ) . α-Synuclein was expressed using a pET21a vector provided by Dr . Carol Ladner of the University of Alberta . The protein was expressed in E . coli BL21 ( DE3 ) bacteria and was purified as in [25] . Briefly , the protein was overexpressed with 1 mM IPTG in 1L LB cultures of BL21DE3 E . coli for 4 hours at 30°C . Cells were lysed with a French press and the lysate was heated at 70°C for 10 minutes and then centrifuged at 14000 g for 30 minutes . The soluble fraction was precipitated for 1 hour in 2 . 1 M ( NH4 ) 2SO4 . α-Synuclein was then purified by fast protein liquid chromatography on a Mono Q 4 . 6/100 SAX column ( GE Life Science ) , using a gradient from 50–500 mM NaCl , in 50 mM Tris at pH 8 . 0 . Elution fractions containing α-synuclein were further purified by size exclusion on a Superdex 200 30/100 GL column ( GE Life Science ) . For the expression of metabolically labeled 15N α-synuclein , 1L of M9 Minimal media was prepared with 1 g/L 15NH4Cl ( Cambridge Isotopes ) as the sole source of nitrogen . BL21 ( DE3 ) cells were grown overnight in 50mL of this media , then seeded into 1 L , grown to an A600 of approximately 0 . 8 , and induced using 1 mM IPTG . After expression overnight at 30°C , 15N α-synuclein was purified as described above . Unlabeled and 15N metabolically-labeled α-synuclein were mixed in a 1:1 ratio at a concentration of 20 μM in 50 mM Na2HPO4 and incubated overnight at room temperature prior to crosslinking . α-synuclein aliquots of 38 μL were then crosslinked using either 1 mM of the ABAS-12C6/13C6 crosslinker ( Creative Molecules ) or 30 mM of the EDC crosslinker . ABAS crosslinking reaction mixtures were incubated for 10 minutes in the dark to allow the NHS-ester reaction to take place , followed by 10 minutes of UV irradiation under a 25 W UV lamp ( Model UVGL-58 Mineralight lamp , UVG ) with a 254 nm wavelength filter . ABAS reaction mixtures were quenched with 10 mM ammonia bicarbonate . EDC reaction mixtures were incubated for 20 minutes . A portion of each crosslinking reaction mixture was checked by SDS-PAGE gel to see the extent of potential intermolecular crosslinked products . Aliquots were subsequently split and digested with either trypsin or proteinase K at an enzyme: protein ratio of 1:10 . Digestion was quenched using a final concentration of 10 mM AEBSF ( ApexBio ) , and samples were then acidified with formic acid for analysis by mass spectrometry . For TATA , 100 μM synuclein in 50 mM sodium phosphate buffer was reacted with 0 . 5 mM TATA-12C3/13C3 ( Creative Molecules ) . Samples were incubated for 5 minutes with 254 nm UV light from the same lamp as was used for the ABAS reactions . Samples were then split and digested with either proteinase K or trypsin at an enzyme: protein ratio of 1:20 . For SDA reactions , 20 μL of 1mg/mL α-synuclein was crosslinked using 1 mM SDA ( Creative Molecules , Inc . ) . Aliquots were incubated for 15 minutes in the dark prior to incubation under the same UV lamp as used previously for ABAS reactions but changing the wavelength to 366 nm . Samples were then run on an SDS-PAGE gel , and bands representing the α-synuclein monomer were excised and subjected to in-gel trypsin digestion . After in-gel digestion , samples were acidified using formic acid prior to mass spectrometric analysis . The CBDPS crosslinking reaction mixture consisted of 238 μL of 50 μM α-synuclein , with 0 . 12 mM CBDPS . Samples were split and digested with either proteinase K or trypsin at an enzyme: protein ratio of 1:10 . Digests were quenched with 10 mM AEBSF and samples were enriched using monomeric avidin beads ( Thermo Scientific ) . Enriched samples were acidified for mass spectrometric analysis using formic acid . Mass spectrometric analysis was then performed using a nano-HPLC system ( Easy-nLC II , ThermoFisher Scientific ) , coupled to the ESI-source of an LTQ Orbitrap Velos or Fusion ( ThermoFisher Scientific ) , using conditions described previously [11] . Briefly , samples were injected onto a 100 μm ID , 360 μm OD trap column packed with Magic C18AQ ( Bruker-Michrom , Auburn , CA ) , 100 Å , 5 μm pore size ( prepared in-house ) and desalted by washing with Solvent A ( 2% acetonitrile:98% water , both 0 . 1% formic acid ( FA ) ) . Peptides were separated with a 60-min gradient ( 0–60 min: 4–40% solvent B ( 90% acetonitrile , 10% water , 0 . 1% FA ) , 60–62 min: 40–80% B , 62–70 min: 80% B ) , on a 75 μm ID , 360 μm OD analytical column packed with Magic C18AQ 100 Å , 5 μm pore size ( prepared in-house ) , with IntegraFrit ( New Objective Inc . , Woburn , MA ) and equilibrated with solvent A . MS data were acquired using a data-dependent method . The data dependent acquisition also utilized dynamic exclusion , with an exclusion window of 10 ppm and exclusion duration of 60 seconds . MS and MS/MS events used 60000- and 30000-resolution FTMS scans , respectively , with a scan range of m/z 400–2000 in the MS scan . For MS/MS , the CID collision energy was set to 35% . Data were analyzed using the 14N15N DXMSMS Match program from the ICC-CLASS software package [27] . SDA crosslinking data was analyzed using Kojak [28] and DXMSMS Match . For scoring and assignment of the MS/MS spectra , b- and y-ions were primarily used , with additional confirmation from CID-cleavage of the crosslinker where this was available . Chemical surface modification with pyridine carboxylic acid N-hydroxysuccinimide ester ( PCAS ) ( Creative Molecules ) was performed as described previously [29] . Briefly , α-synuclein was prepared at 50 μM in 8 M urea in PBS , pH 7 . 4 ( unfolded state ) , or in only PBS ( folded state ) . Either the light or the heavy form of the 13C-isotopically-coded reagent ( PCAS-12C6 or PCAS-13C6 ) was then added to give a final concentration of 10 mM . Reaction mixtures were incubated for 30 minutes and quenched with 50 mM ammonium bicarbonate . Samples were then mixed at a 1:1 ratio , combining folded ( PCAS-12C ) with unfolded ( PCAS-13C ) samples , as well as in reverse as a control . Samples were acidified with 150 mM acetic acid and digested with pepsin at a 20:1 protein: enzyme ratio overnight at 37°C . After digestion samples were prepared for mass spectrometry analysis using C18 zip-tips ( Millipore ) . Zip-tips were equilibrated with 30 μL 0 . 1% TFA , sample was introduced , then washed with 30 μL 0 . 1% TFA and eluted with 2 μL of 0 . 1% formic acid/50% acetonitrile . Samples were analyzed by LC-MS/MS as described above . Top-down ECD-FTMS hydrogen/deuterium exchange was performed as described previously [30] . Briefly , protein solution and D2O from separate syringes were continuously mixed in a 1:4 ratio ( 80% D2O final ) via a three-way tee which was connected to a 100 μm x 5 cm capillary , providing a labeling time of 2 s . The outflow from this capillary was mixed with a quenching solution containing 0 . 4% formic acid in 80% D2O from the third syringe via a second three-way tee and injected into a Bruker 12 T Apex-Qe hybrid Fourier Transform mass spectrometer , equipped with an Apollo II electrospray source . In-cell ECD fragmentation experiments were performed using a cathode filament current of 1 . 2 A and a grid potential of 12 V . Approximately 800 scans were accumulated over the m/z range 200–2000 , corresponding to an acquisition time of approximately 20 minutes for each ECD spectrum . Deuteration levels of the amino acid residues were determined using the HDX Match program [31] ( S1 Fig ) . Synuclein UVPD spectra were collected on a Thermo Scientific Orbitrap Fusion Lumos Tribrid mass spectrometer equipped with a 2 . 5-kHz repetition rate ( 0 . 4 ms/pulse ) 213 nm Nd:YAG ( neodymium-doped yttrium aluminum garnet ) laser ( CryLas GmbH ) with pulse energy of 1 . 5 ± 0 . 2 μJ/pulse and output power of 3 . 75 ± 0 . 5 mW for UVPD . The solution was exchanged with deuterium using the same three-way tee setup , although in this case a 50 μm x 7cm capillary provided a labeling time of ~1s . Spectra were acquired for 8 or 12 ms , and resultant spectra were averaged and used for the data analysis with the HDX Match program as above . CD spectra were recorded on Jasco J-715 spectrometer under a stream of nitrogen . The content of α-helical and β-sheet structures was calculated using BeStSel web server [32] . Crosslink guided discrete molecular dynamics ( CL-DMD ) simulations were performed according to the protocol described in our previous work [11] . Briefly , discrete molecular dynamics ( DMD ) is a physically based and computationally efficient approach for molecular dynamics simulations of biological systems [16 , 17] . In DMD , continuous inter-atom interaction potentials are replaced with their discretized analogs , allowing the representation of interactions in the system as a series of collision events where atoms instantaneously exchange their momenta according to conservation laws . This approach significantly optimizes computations by replacing integration of the motion equations at fixed time steps with the solution of conservation-law equations at event-based time points [33] . In order to incorporate experimental data for inter-residue distances between corresponding atoms into DMD simulations , we introduced a series of well-shape potentials that energetically penalize atoms whose interatomic distance do not satisfy experimentally determined inter-atom proximity constraints . The widths of these potentials are determined by the cross-linker spacer length and side chain flexibility [11] . Starting from the completely unfolded structure of α-synuclein molecule , we performed an all-atom Replica Exchange ( REX ) [34] simulations of the protein where 24 replicas with temperatures equally distributed in the range from 0 . 375 to 0 . 605 kcal/ ( mol kB ) , are run for 6 x 106 DMD time steps ( S2 Fig ) . The simulation temperature of each of the replicates periodically exchanged according to the Metropolis algorithm allowing the protein to overcome local energetic barriers and increase conformational sampling . During the simulations we monitored the convergence of the system energy distribution specific heat curve , calculated by Weighted Histogram Analysis Method ( WHAM ) [35] which was used as the indicator of system equilibration . We discarded the first 2 x 106-time steps of system equilibration during the analysis . Next , we ranked all of the structures among all of the trajectories , and selected the ones with lowest 10% of the energies , as determined by the DMD Medusa force field [36] . These structures were then clustered using the GROMACS [37] distance- based algorithm described by Daura et al . [38] . It uses root-mean-square deviation ( RMSD ) between backbone Cα atoms as a measure of structural similarities between the cluster representatives . A RMSD cut-off was chosen to correspond to the peak of the distribution of pair-wise RMSDs for all of the low-energy structures . Because the energies of the resulting centroids representative of the clusters are very close to each other ( S3 Fig ) and picking one of them would potentially introduce a bias related to our scoring energy function , we presented them all as our predicted models of the α-synuclein globular structure . We then calculated the root-mean-square deviation of atomic positions within each cluster and used this as a measure of fluctuations of the structures of corresponding centroids ( Figs 2 and 3 ) . In order to obtain information on the global folding of α-synuclein , we performed clustering analysis on the lowest-energy structures obtained during CL-DMD simulations .
In summary , we have determined de novo the conformational ensemble of native α-synuclein in solution by short-distance crosslinking constraint-guided DMD simulations , and validated this structure with experimental data from CD , HDX , SM , and LD-CL experiments . The predicted conformational ensemble is represented by rather compact globular conformations with transient secondary structure elements . The obtained structure can serve as a starting point for understanding the mis-folding and oligomerization of α-synuclein .
|
As the population ages , neurodegenerative diseases such as Parkinson’s disease will become an increasing problem in many countries . Aggregation of the protein α-synuclein is the primary cause of Parkinson’s disease , but there is still a dearth of structural information pertaining to the native , non-aggregating form of this protein . A better understanding the structural state of the native protein may prove useful for the design of new therapeutics to combat this disease . In order to obtain more structural information on this protein , we have recently modelled the native α-synuclein protein . These models were generated using a novel approach which combines protein crosslinking and discrete molecular dynamics simulations . We have found that the α-synuclein protein can adopt several shapes , all with a similar topology , resembling a three fingered closed claw . A region of the protein important for aggregation was found to be protected from the surrounding biological environment in these conformations , and the stabilization of these structures may be a fruitful avenue for future drug research into mitigating the cause and effect of Parkinson’s disease .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"and",
"discussion"
] |
[
"chemical",
"bonding",
"molecular",
"dynamics",
"protein",
"structure",
"prediction",
"protein",
"structure",
"intrinsically",
"disordered",
"proteins",
"physical",
"chemistry",
"protein",
"structure",
"determination",
"proteins",
"chemistry",
"cross-linking",
"molecular",
"biology",
"protein",
"structure",
"comparison",
"biochemistry",
"biochemical",
"simulations",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"computational",
"chemistry",
"computational",
"biology",
"macromolecular",
"structure",
"analysis"
] |
2019
|
Conformational ensemble of native α-synuclein in solution as determined by short-distance crosslinking constraint-guided discrete molecular dynamics simulations
|
The number of mRNA and protein molecules expressed from a single gene molecule fluctuates over time . These fluctuations have been attributed , in part , to the random transitioning of promoters between transcriptionally active and inactive states , causing transcription to occur in bursts . However , the molecular basis of transcriptional bursting remains poorly understood . By electron microscopy of single PHO5 gene molecules from yeast , we show that the “activated” promoter assumes alternative nucleosome configurations at steady state , including the maximally repressive , fully nucleosomal , and the maximally non-repressive , nucleosome-free , configuration . We demonstrate that the observed probabilities of promoter nucleosome configurations are obtained from a simple , intrinsically stochastic process of nucleosome assembly , disassembly , and position-specific sliding; and we show that gene expression and promoter nucleosome configuration can be mechanistically coupled , relating promoter nucleosome dynamics and gene expression fluctuations . Together , our findings suggest a structural basis for transcriptional bursting , and offer new insights into the mechanism of transcriptional regulation and the kinetics of promoter nucleosome transitions .
The number of gene product molecules fluctuates over time and between cells [1] . The magnitude of such fluctuations ( “expression noise” ) is generally expressed in terms of variance over the mean of gene expression ( “Fano factor” ) , or variance over mean squared ( “coefficient of variation” [CV2] ) . Gene expression may be viewed as a sequence of molecular transitions; two noise components are then distinguishable: “intrinsic noise , ” which derives from the random choice between alternative transitions and the statistical distribution of dwell times between transitions , and “extrinsic noise , ” which arises from fluctuations in the cellular concentrations of the biochemical factors that promote the transitions [2] . Stochastic models have been proposed to account for the intrinsic noise of gene expression . An essential component of such models is the assumption that genes randomly transition between states that are either transcriptionally active ( “ON state” ) , or inactive ( “OFF state” ) [3] . In the ON state a “burst” of transcripts is released , whose magnitude both depends on the rate of transcription and the average life-time of the ON state ( Figure 1A ) . The notion of transcriptional bursting contrasts with the conventional ( “deterministic” ) model of a transcriptionally active gene , which assumes continual competence for transcription , and where regulation of mRNA synthesis is limited to changes in the rate of transcription ( Figure 1B ) . In the stochastic model ( Figure 1A ) , transcriptional activators may stimulate transcription by either modulating the frequency of transcriptional bursting , burst size , or both . Which of these possible mechanisms is employed may be tested by measurements of intrinsic noise as a function of the average gene product abundance , for model calculations indicate that the intrinsic noise changes in characteristic ways depending on the identity of the steps that are tuned to alter expression ( Figure 1C ) [4] . However , the molecular basis for transcriptional bursting is not understood , and alternative mechanisms , such as random partitioning of mRNAs at cell division , have been proposed to account for the observed variability in gene product abundance [5] . The wrapping of DNA into nucleosomes limits access of activators and general transcription factors to promoter DNA and impedes the transcribing RNA polymerase [6]–[9] . Consistently , in vivo studies support the notion that nucleosomes are general repressors of transcription [10] , [11] . The inducible PHO5 promoter of yeast has served as a classical paradigm for studies of the relationship between promoter chromatin structure and transcription [12] . Structural studies of the PHO5 promoter pointed at the possibility of alternative nucleosome configurations for the induced PHO5 promoter , however , without directly demonstrating their existence [13]–[15] . These and other observations have given rise to the notion that fluctuations in promoter chromatin structure might underlie transcriptional bursting [4] , [13] , [16] . Critical testing of this theory calls for the analysis of promoter chromatin structure at the level of single gene molecules , rather than molecule ensemble averages after endonucleolytic cleavage , the conventional experimental approach . Here , we report the analysis of the promoter nucleosome configuration of single PHO5 gene molecules by electron microscopy ( EM ) . Our data demonstrate the existence of alternative promoter nucleosome configurations at steady state PHO5 expression , including the maximally repressive , fully nucleosomal , and maximally non-repressive , nucleosome-free configuration . We show that the observed configurational probability distribution of PHO5 promoter nucleosomes is obtained by a simple stochastic process of nucleosome assembly , disassembly , and position-specific sliding . Our analysis thus provides a molecular basis for transcriptional bursting; and we confirm that PHO5 expression indeed bears the signature of such bursting , with bursting frequency , rather than burst size , as the parameter that responds to transcriptional activators . We demonstrate the possibility of an integrated model of promoter chromatin dynamics and gene expression that quantitatively accounts for measurements of gene expression noise and EM data . The model allows us to predict the life times of microscopically observable promoter nucleosome configurations under repressing and activating conditions , and identifies specific promoter nucleosome transitions as essential for activated transcription .
To analyze the nucleosome configuration of single PHO5 gene molecules , we isolated chromatin rings encompassing the promoter nucleosome positions N-3 to N-1 and the open reading frame of the yeast PHO5 gene ( Figure 2A ) . Chromatin rings were formed in vivo by induction of site-specific recombination between recombination sequences ( RS ) flanking the PHO5 locus ( Figure 2A ) [14] . A cluster of lexA operator sequences allowed for ring purification by expression of an adaptor protein that contained LexA fused to a tandem affinity tag [17] . The promoter DNA of purified chromatin rings and isolated nuclei exhibit closely similar sensitivities to restriction endonucleases , suggesting essentially identical chromatin structures [14] , [18]; and PHO5 is fully inducible after ring formation ( Figure S3 ) . Transcriptionally “active” PHO5 molecules were isolated from pho80Δ cells in which PHO5 is expressed constitutively [19] . Purified chromatin rings were incubated with trimethylpsoralen and exposed to ultraviolet ( UV ) light , resulting in inter-strand crosslinking of the DNA double helix in nucleosomal linkers , but not core particle DNA [20] , thus “etching” nucleosome configurations into the DNA . Purified ring DNA was denatured , spread onto ethidium-coated carbon grids , stained with uranyl acetate , and rotary metal shadowed prior to EM . Positions previously occupied by nucleosome core particles thus appear as single-stranded DNA bubbles , connected by double stranded linker DNA that resisted denaturation due to crosslinking [20] . Single-stranded DNA bubbles were densely spaced on PHO5 rings isolated from repressed cells , separated by only short segments of crosslinked double stranded DNA ( Figure 2B ) . Larger than expected bubbles suggested fusion of nucleosome bubbles due to failure of crosslinking of the intervening linker DNA , which is rather short in the open reading frame of the gene ( Materials and Methods ) . In contrast , the majority of PHO5 rings isolated from activated cells exhibited larger contiguous segments of crosslinked DNA ( Figure 2C ) . To determine whether these continuously crosslinked regions coincided with promoter sequences , we linearized rings by NcoI restriction enzyme digestion ( Figures 2A and 3 ) . Larger nucleosome-free segments were found only at or close to one end of linearized rings , and not in their interior ( Figure 3 ) . At the opposite end , which bears the lexA operator cluster , molecules were forked , suggesting that the binding of LexA adaptor proteins at the cluster prevented its crosslinking . Consistently , forked ends were not observed in control experiments with naked DNA molecules ( Figure S1 ) . The fork thus oriented molecules , and enabled us to identify the promoter nucleosome bubbles on all PHO5 gene molecules . Eight promoter nucleosome configurations were observed , representing all combinations of occupied and unoccupied positions N-1 to N-3 ( Figure 3 ) . To assign nucleosome bubble identities and thus determine the relative frequency of each promoter nucleosome configuration , we determined for each linearized ring the positions of upstream activation sequences 1 ( UASp1 ) and 2 ( UASp2; position N-2 ) , and the transcription start site ( position N-1 ) by measuring their expected distance from the proximal DNA end . Both upstream activation sequences bear a binding site for the transcriptional activator Pho4 , which is essential for PHO5 expression [21] . The relative frequencies of promoter nucleosome configurations for PHO5 rings isolated from PHO4 wild type cells is shown in Figure 4A . Nucleosome occupancies determined by EM were in good agreement with nuclease accessibility measurements on isolated nuclei [14] , [15] . For instance , micrococcal nuclease and restriction endonuclease analysis of position N-1 indicated an occupancy of position N-1 between 0 . 5 and 0 . 6 for the “active” promoter , closely similar to the occupancy of 0 . 52 inferred by EM ( Figure S2; Table S1 ) . How can the observed configurational probability distributions of the promoter nucleosomes be explained ? In the following we shall demonstrate that the observed probability distributions can be explained as the result of an intrinsically stochastic process , i . e . , a process where the future configuration and the configuration's life-time can be predicted only probabilistically . Specifically , we assume that the probability of finding the promoter in state j at time t+h , given that the promoter was in configuration k at time t , equals h for sufficiently small time intervals h , where , the transition probability per time and molecule , depends only on j and k ( assumption of a time homogeneous Markov process ) . The steady state probabilities p0 , … , p7 of nucleosome configurations 0 , … , 7 of such a process obey the following matrix equation [13] , [15]: ( i ) with diagonal elements . We refer to the 's as the “kinetic parameters” of the process . Experimentally , steady state was achieved by isolating chromatin rings from pho80Δ cells ( see above ) . The task at hand is to find 's that are consistent with equation ( i ) , or more precisely , a matrix with diagonal elements as above whose kernel is spanned by the observed steady state distribution ( p0 , … , p7 ) , or a vector very similar to it . Transitions between nucleosome configurations may be due to assembly , disassembly , or sliding of a nucleosome . Accordingly , we may distinguish three different kinds of transitions . We call the stochastic process “simple” if transitions of the same kind have the same kinetic parameter value , for assembly , for disassembly , and for sliding transitions; the term “transition topology” refers to the set of all possible transitions . The assumption of a simple process reduces the task of finding the desired matrix to drawing the “correct” transition topology . A transition topology that is limited to transitions between configurations that differ by one nucleosome , and that has therefore only two kinds of transitions—nucleosome assembly and disassembly—may serve as a starting point ( Figure 4B ) . This topology , with on some appropriate time scale and determined by maximum likelihood analysis ( Materials and Methods ) , accounted well for the observed probabilities of nucleosome number , but not the probabilities of individual configurations ( Figure 4A ) . The discrepancy between prediction and experimental observation could thus be attributed to a redistribution of probability mass between nucleosome configurations with equal nucleosome number , and hence to the sliding of nucleosomes between promoter positions . With and similar , but lower than ; and and similar , but higher than ( Figure 4A ) , the observed distribution suggested sliding of nucleosomes out of position N-2 ( Figure 4D; “unidirectional sliding topology” ) . Indeed , observed probabilities and probabilities calculated on the basis of the transition topology of Figure 4D were closely similar ( Figure 4C ) , with determined by maximum likelihood analysis . Remarkably , the assumption that disassembly of nucleosomes in position N-1 requires a nucleosome-free position N-2 ( Figure 4F ) , suggested by nucleosome accessibility analysis over the time course of PHO5 induction [22] , led to a virtually perfect agreement between measurements and predictions ( Figure 4E ) . In contrast , other conceivable topologies , for instance for “stable nucleosome retention” ( Figure 4G ) [13] , “all-or-none removal” of promoter nucleosomes ( Figure 4H ) , and “cyclical deterministic” processes , such as the topology of Figure 4I , were refuted by our data . To further test our topological hypotheses , we analyzed the configurational probability distributions in the PHO5 tata box mutant ( Figure 5A–5C ) and strains that bore mutations in PHO4 or PHO2 ( Figure 5B–5D ) . ( The latter two genes encode the transcriptional activators of PHO5 . ) All observed distributions were well explained by the unidirectional sliding topologies of Figure 4D and 4F; our data generally supported topology 4D over 4F , except for the pho4[85–99] mutant , where topology F enjoyed greater statistical support ( see legend to Figure 5B ) . Thus , all observed configurational probability distributions were well explained by a simple stochastic process of nucleosome removal and reformation with no more than two degrees of freedom . Our findings support the hypothesis that the PHO5 promoter stochastically transitions between alternative nucleosome configurations at steady state . Notably , while our data clearly indicated a net loss of nucleosomes from the promoter upon induction of PHO5 ( compare Figures 4A and 5C , for instance ) , no such loss was observed over the open reading frame that could have been attributed to transcription ( Table S1 ) . The structural dynamics of promoter and open reading frame nucleosomes appear to be fundamentally different . Nucleosomes inhibit the binding of transcription factors at the PHO5 promoter [6] . The nucleosome-free and fully nucleosomal configurations are , therefore , either maximally conducive or inconducive to transcription . Hence , our conclusion that the “active” PHO5 promoter stochastically transitions between the nucleosome-free and fully nucleosomal configuration implies that PHO5 transcription occurs in random bursts . We previously determined the intrinsic protein noise of PHO5 expression for 23 mutants that either bore a mutation in the Pho4 activation domain or the upstream activation sequences of the PHO5 promoter ( Figure 6 ) [15] . In the absence of bursting ( Figure 1B ) , a flat Fano factor profile is expected ( Figure 1C ) . The observed profile , however , deviated significantly from this expectation ( Figure 6A ) , and furthermore suggested that Pho4 regulates PHO5 expression by modulating the burst frequency of transcription , α , rather than burst size , εβ−1 ( compare Figures 6A and 1C ) . We refer to the kinetic parameters that respond to regulatory input , such as α , as “regulatory parameters . ” If burst frequency is the regulatory parameter for PHO5 expression , then the observed net loss of promoter nucleosomes upon PHO5 induction is due to accelerated nucleosome removal , and not inhibition of nucleosome assembly; the former changes the frequency of transcriptional bursting , the latter burst size . Comparison of PHO5 molecules isolated from the TATA box wild type and tata box mutant ( Figures 4A and 5A ) , and the PHO2 wild type and pho2Δ mutant ( Figure 5D and 5E ) allowed for additional testing of this implication . Pho2 binds to several binding sites at position N-2 following nucleosome removal [6] and might thus sterically exclude nucleosomes from position N-2 . The same argument applies for the TATA box binding protein ( TBP ) and the TATA box . However , neither mutation resulted in an increase in nucleosome occupancy at N-2 or N-1 , respectively . Our results thus argue against steric exclusion of nucleosomes by transcription factors , and suggest that nucleosome formation does not compete with transcription factor binding . Thus , both noise analysis and EM data corroborate the hypothesis that the net loss of promoter nucleosomes upon transcriptional induction is due to the acceleration of nucleosome removal , and not inhibition of nucleosome assembly , supporting the notion of activator-mediated recruitment of chromatin remodeling factors . Our data show that the majority of “active” PHO5 molecules exhibited promoter nucleosome configurations between the two extremes of the fully nucleosomal and the nucleosome-free configuration ( Figure 4A ) . The fundamental problem of defining the relationship between promoter chromatin structure and transcription here presents itself again at the single molecule level: which promoter nucleosome configurations , if any , beside the naked configuration might be conducive to transcription ? The following considerations show that our data restrict the number of possible answers to this problem ( “fundamental problem” ) . The transcriptionally active molecules constitute a subset of the transcriptionally conducive ones . It follows that the probability of the ON state , , provides a lower bound for the combined probabilities of all nucleosome configurations that are conducive to transcription , , i . e . , . In the following , we show that , the probability of the ON state , where ∧ refers to the PHO4 wild type , can be determined from the quantitative relationship between mean protein abundance and the CV2 for the intrinsic protein noise , with mean abundance of mRNA molecules in PHO4 wild type cells , and the kinetic parameters for mRNA degradation δ , protein degradation ζ , and translation η given ( Figure 1A ) . We determined by fluorescence in situ hybridization ( FISH ) ( Figure 7A , 7B ) . The kinetic parameter for mRNA degradation δ , was determined by northern blotting using PHO80 wild type cells , where the PHO5 promoter was induced by transferring cells into phosphate-free medium followed by addition of inorganic phosphate to shut down transcription ( Figure 7C , 7D ) . ( We assume , here , that the promoter rapidly becomes inactive following addition of phosphate; this assumption is supported by the close fit of the mRNA decay curve to a single exponential function [Figure 7D]; the half life thus determined was identical to the average half life of mRNAs in yeast [23] . Importantly , is rather insensitive to changes in δ [Figure S4A]; the inferred value of [see below] did not critically depend on δ . ) The parameter for protein degradation , ζ , was calculated from the cell cycle time , as Pho5 activity decays at the rate of cell division ( unpublished data ) . The average number of Pho5 molecules , , under repressing conditions ( indicated by ′ ) had been determined previously [24] . Together with knowledge of the average number of mRNA molecules , , from FISH ( Figure 7B ) , an estimate for the translation parameter η was derived from the steady state condition . Like our model of promoter nucleosome dynamics , the stochastic model of gene expression ( Figure 1A ) is based on the assumption of a time-homogeneous Markov process with transitions between discrete molecular states , defined by the number of mRNA and protein molecules , and the promoter state ( ON or OFF ) [25]–[27] . Steady state expression noise and mean can be derived analytically for a given set of kinetic parameter values ( see Materials and Methods ) . For given , and , the kinetic parameter for transcription , ε , is provided by the steady state condition . Furthermore , , which specifies the time scale of promoter state fluctuations , is determined by . This can be understood intuitively by considering that fast ON/OFF fluctuations , i . e . , small , correspond to short dwell times in both ON and OFF states; short dwell times in both states reduce the transcriptional burst size and provide little time for the degradation of gene products during OFF periods , respectively , thus reducing the size of protein molecule number fluctuations about the mean . With thus determined , and given , α and β can be calculated , using the steady state condition . Arbitrary values for could account for our measurements of and ( Figure 6B ) . However , for , a noise profile was obtained that agreed remarkably well with the observed profile , with the transcriptional burst frequency , α , as regulatory parameter ( Figure 6B ) . The 0 . 6 value for together with our EM data ruled out many conceivable solutions of the ‘fundamental problem’ . Indeed , the only set of nucleosome configurations united by a common structural feature with a total probability greater than 0 . 6 , and thus satisfying our previous requirement of , was the set of configurations that lack the nucleosome in position N-2 ( configurations 2 , 4 , 6 , and 7; Figure 4B ) . This result suggests that configurations 2 , 4 , 6 , and 7 are transcriptionally conducive , while configurations 0 , 1 , 3 , and 5 are inconducive . Since the transcriptionally active promoter states are a subset of the states with conducive nucleosome configurations , , where is the probability of the active state , given that the promoter exhibits a transcriptionally conducive nucleosome configuration . If promoter states with a conducive nucleosome configuration were transcriptionally active , i . e . , at all times , and thus , PHO5 expression would exclusively be controlled by modulation of the kinetic parameters for nucleosome disassembly and sliding; and steady state would be a linear function of : . However , while FISH analysis indicated that increased ∼200-fold upon PHO5 induction ( Figure 7A , 7B ) , our EM data showed that rose by a factor of about 4 only ( Figure 4; Table S1 ) —too little to explain the observed increase in PHO5 transcription . It follows that increased by a factor of ∼50 upon PHO5 induction; this factor indicates the increase in transcription that remains unexplained by promoter chromatin remodeling . We conclude that the Pho4 activator stimulates one or more additional steps of the expression process following nucleosome removal . A stochastic process based on the transition topology of Figure 8A integrates our findings: Beside the transitions between nucleosome configurations according to the topology of Figure 4D , the model encompasses transitions between transcriptionally active and inactive promoter states . The model thus introduces two additional kinetic parameters: for transitions ( yellow arrows ) from conducive into active states , λ , and transitions ( blue arrows ) from active into conducive states , μ ( Figure 8A ) . Consistent with burst frequency control , regulation occurs at two levels: transitioning from inconducive configurations ( white ) to conducive configurations ( light gray ) , and hence by modulation of and , and transitioning from conducive to active promoter states ( dark gray ) , i . e . , modulation of λ ( Figure 8A ) . The model allows for nucleosome assembly transitions from active into inconducive promoter states ( white ) in accordance with our finding that nucleosome formation does not compete with transcription factor binding . All kinetic parameters , including λ and μ , were definitely determined by our data ( Figure 8A; Text S1 ) . As claimed—without further adjustment of parameter values or introduction of new parameters—the model successfully integrated EM and noise data: predictions of configurational probabilities based on the topologies of Figure 4D and of Figure 8A were identical ( not shown ) ; and for virtually any smooth path—a straight line for instance—that connects the two parameter vectors and for the PHO4 wild type and pho4Δ mutant , respectively , a noise profile was obtained that naturally fit the observed profile of PHO5 expression ( Figure 8B ) . In contrast , paths that assumed regulation also of burst size yielded predictions that were inconsistent with experimental observations ( Figure 8B ) .
The chromatin structure of eukaryotic promoters is subject to poorly understood “remodeling” upon transcriptional activation . To clarify the relationship between the structural dynamics of promoter chromatin and gene expression , we here proposed a stochastic model for the PHO5 gene of yeast ( Figure 8A ) . This model has the following essential properties: ( AR ) Assumption of a random process: At steady state , the promoter stochastically transitions between alternative nucleosome configurations; allowed transitions include nucleosome assembly and disassembly transitions between configurations that differ by one nucleosome , and sliding transitions that move nucleosomes out of , but not into , the central N-2 position of the promoter . ( AS ) Assumption of a simple process: Nucleosome transition probabilities per time and molecule depend only on the kind of transition—assembly , disassembly and sliding—rather than nucleosome position or configuration . ( AC ) Conduciveness hypothesis: Removal of nucleosome N-2 is necessary for transcription . This specifies the nucleosome configurations that are conducive to transcription . ( AD ) Assumption of nucleosomal dominance: Nucleosomes exclude transcription factors from promoter DNA [6] , but not vice versa . ( AF ) Regulatory assumption: The transcriptional activator of PHO5 , Pho4 , stimulates promoter nucleosome disassembly and sliding and , following nucleosome removal , assembly of a scaffold complex of general transcription factors that supports transcription [28] . Together , AR , AC , and AD imply that transcription occurs in the form of stochastic “bursts” ( Figure 1A ) ; the fundamental bursting frequency is determined by the dynamics of promoter nucleosome fluctuations . The essential effect of “chromatin remodeling” is a shift of probability mass from promoter configurations with more to those with fewer nucleosomes . Loss of promoter nucleosomes by accelerated removal ( AF ) , rather than steric exclusion ( AD ) , means that nucleosomes are an integral part of the regulatory mechanism for transcription , and not passive repressors whose inhibitory effect is overcome by mass action . However , activated transcription is not explained by accelerated nucleosome removal alone ( AF ) . This model has withstood our critical testing , while plausible alternatives could be refuted . Importantly , our findings provide direct structural support for the hypothesis of transcriptional bursting , a concept that has proved useful to account for and mechanistically interpret measurements of gene expression noise [3] , [4] , [15] , [29]–[32] . Other , previous findings may also be explained on our theory , as detailed below . A critical test of the random process assumption AR required the establishment of methods that allowed us to investigate the nucleosome configuration of single PHO5 gene molecules . Previous analyses of in vivo chromatin remodeling relied on endonucleolytic cleavage and averaging over large numbers of gene molecules; both averaging and DNA cleavage erased the information necessary to provide a test of our hypothesis: the probability distribution of promoter nucleosome configurations . Remarkably , a simple stochastic process in accordance with assumptions AR and AS , and hence two degrees of freedom only , predicted each of the experimentally observed probability distributions with surprising accuracy when based on either the topology of Figure 4D or 4F . Both topologies were overwhelmingly supported by our data against conceivable alternatives ( Figure 4G–4I , for instance ) . It may be argued that the observed configurational probability distributions of promoter nucleosomes are the product of a deterministic process driven by an extrinsic oscillation , such as the cell cycle , with randomly distributed phase difference between cells , rather than an intrinsically random process , as claimed by AR . The topology of Figure 4I represents such an alternative process , where transitions between configurations are deterministic , and only the life times of configurations are assumed to be statistically distributed . Our analysis does not exclude the possibility of such an explanation; but it suggests that it would require many more degrees of freedom ( kinetic parameters ) . The model with fewer degrees of freedom is to be preferred , however , not because it might be considered more likely , but because it has greater predictive power and is , therefore , more easily falsifiable . Some data support the assumption that Pho4 binding stimulates disassembly of only the most proximal nucleosome [22] , [33]; accelerated disassembly of nucleosomes in position N-1 would thus require prior removal of nucleosomes in position N-2 , which renders UASp2 accessible to activator binding [6] . This assumption may explain the statistical support for the transition topology of Figure 4F against its rival of Figure 4D by some ( Figures 4E , 5B ) , but not all ( Figure 5A ) , datasets for “activated” gene molecules . In contrast , analysis of “repressed” molecules consistently and strongly supported the topology of Figure 4D over 4F ( Figure 5C–5E ) , as expected in the absence of accelerated disassembly . In any case , both topologies provide rather similar statistical predictions for “active” molecules ( Figure 4C , 4E ) , complicating their distinction by statistical means . The regulatory hypothesis AF implies that loss of promoter nucleosomes is a cause of activated transcription , rather than its consequence . Nucleosome disassembly can be strongly dependent on Swi2 , the catalytic subunit of the ATP-dependent remodeling enzyme SWI/SNF [34] , [35] . Similar observations of genetic dependence , and of activator-remodeler interactions are frequently invoked in support of this assumption . This argument overlooks , however , that the same observations are equally consistent with the opposite assumption—that nucleosome loss is a consequence of promoter activation , possibly due to steric exclusion by transcription factors . Indeed , our data support the notion of continual disassembly of promoter nucleosome even in the absence of activators ( Figure 5C–5E ) ; this may explain the rapid binding of newly synthesized histones to transcriptionally “inactive” promoters [36] . A critical test of AF , therefore , requires the distinction between cause and effect of nucleosome loss . The two-state promoter model ( Figure 1A ) provided a means for this distinction . Given AR , AC , and AD , AF predicts an increase in the frequency of transcriptional bursting , whereas its rival hypothesis implies an increase in burst size . Noise analysis bore out the former expectation ( Figure 6 ) , refuting nucleosome loss by mass action . Additional support for AF was provided by our experimental test of AD ( hypothesis of nucleosomal dominance ) . Neither mutation of the TATA box , nor absence of the Pho2 transcription factor caused an increase in promoter nucleosome occupancy ( compare Figure 4A with 5A , and Figure 5D with 5E , respectively ) —again refuting the hypothesis of nucleosome loss by mass action , however corroborating both AD and AF . AD may explain the poor correlation between the dwell time of activators at their DNA recognition sequences in vivo and their binding affinity [37] . AF furthermore implies that activated transcription encompasses accelerated assembly of a scaffold of general transcription factors [28] following nucleosome removal . Alternatively it may be assumed that nucleosome removal is either sufficient for activated transcription [38] , or that Pho4 stimulates other steps than scaffold assembly , such as the rate of transcription in the active state , ε . The first of these alternatives is refuted by our finding that nucleosome loss did not quantitatively account for activated transcription . The second alternative implies that PHO5 transcription is regulated , at least in part , by changes in burst size . For this latter assumption the integrated model of Figure 8A predicted a noise profile that was inconsistent with experimental observations ( Figure 8B ) , refuting this second alternative , too . The same observations , however , corroborated AF ( Figure 8B ) . The discovery of a stalled RNA polymerase at transcriptionally inactive promoters in other eukaryotes [39]–[42] does not contradict the notion that steps prior to the release of stalling are rate-limiting to transcription . The PHO5 promoter assumes the active state even in the absence of Pho4 , releasing bursts of transcripts at low frequency ( Figure 7B ) . This may explain the need for additional mechanisms of regulation , such as stalling , to suppress bursting , which in the case of genetic regulators of embryogenesis [41] are likely to have deleterious consequences . A low probability per time of forming a stalled polymerase-promoter complex under repressing conditions may lead , eventually , to complex formation with near certainty when integrated over a sufficiently long time . The integrated model of Figure 8A allowed us to infer the time scale of promoter nucleosome transitions from measurements of cell cycle time and mRNA half life , suggesting an average life time for unoccupied promoter nucleosome positions , ( 1/ ) , of ∼1 min ( Text S2 ) . This estimate is minimally affected by possible error margins for cell cycle time or mRNA half life ( Figure S4 ) . An experimental test of this implication of our theory will have to await the development of independent methods for determining the kinetic parameters of nucleosome transitions in vivo . We note however that the short half life of unoccupied nucleosome positions may explain the rapid association of newly synthesized histones with promoter DNA , observed on a genome-wide scale [36] . The central role for transcription of the nucleosome in position N-2 ( AC ) , was imposed by our finding , from noise analysis ( Figure 6B ) , that the total probability of active promoter states is 0 . 6 under fully activating conditions ( PHO4 pho80Δ cells ) , providing a lower bound for the total probability of conducive nucleosome configurations . Our EM data indicated that the only set of configurations consistent with this lower bound , and united by a common structural feature , was the set of configurations with a nucleosome-free position N-2 , and not N-1 , contrary to previous conjectures [13] , [15] . While this conclusion provides a functional explanation for the lower nucleosome occupancy at position N-2 ( Figure 4A ) , it raises the questions of how the requirement for nucleosome removal from position N-2 may be mechanistically explained , and why removal of nucleosomes from position N-1 is not required ? The PHO5 TATA box resides at the 5′-edge of nucleosomes in position N-1 and , under inducing conditions , is freely accessible in a subset of promoter molecules due to a 3′-directed shift in the position of nucleosomes in this position by about 30 base pairs [14] , which was also discernable by EM ( analysis not shown ) . This positional shift and spontaneous partial unwrapping of nucleosomal DNA [7] , [43] , together with high local concentrations of TBP due to activator-mediated recruitment to the promoter [6] , might allow for efficient binding of TBP at the TATA box . Subsequent assembly of the transcription machinery might provide the free energy for further unwrapping of nucleosomal DNA to eventually render the transcription start site accessible , without complete disassembly of the nucleosome [44] . Loss of nucleosomes from position N-2 enables Pho4 binding at UASp2 [6] , and may thus render removal or remodeling of the nucleosome in the proximal N-1 position more effective ( see above ) . However , loss of Pho4 binding at UASp2 by mutation of UASp2 did not abolish activated PHO5 expression; the UASp2 mutant retained ∼25% of its wild type PHO5 expression ( Figure 6B ) [45] . In contrast , inhibition of N-2 nucleosome removal by replacement of the N-2 sequence with a strong nucleosome positioning sequence abolished activated transcription entirely [46] , as predicted by AC . Together , these findings point to an inhibitory effect of this nucleosome beyond blocking access to UASp2 . A possible explanation is that loss of nucleosome N-2 allows general transcription factors , such as TBP , to slide along the DNA toward the core promoter , following their recruitment by Pho4 to promoter DNA at UASp1 . Consistently , bacterial transcription factors find their operator sequence in vivo by a combination of three-dimensional diffusion and DNA sliding [47] . This provides a possible explanation for the observation that tethering of a bacterial repressor protein between upstream activation sequences and the TATA box of the GAL1 promoter of yeast severely inhibits GAL1 transcription [48] . Nucleosomal inhibition of “promoter scanning” by general transcription factors might also explain the occurrence and position of a “nucleosome free region” at many promoters from yeast to human [49] , [50] , analogous to the N-2 region of the PHO5 promoter .
Strains and plasmids used in this study are summarized in Table S2 . Plasmid pSH17 , bearing TEF2:LexA-TAP and GAL1:R , was kindly provided by S . Hamperl and J . Griesenbeck . Purification of chromatin rings was performed as previously described [14] , [17] , [51] , except that calmodulin affinity purification was performed first , followed by IgG-sepharose affinity purification and TEV cleavage ( 6His-tagged TEV was a generous gift of V . Thai ) . Crosslinking was performed essentially as described [52] with the following modifications . Following chromatin ring elution from the IgG column , samples were pooled and placed onto a 10 cm petri dish that was floating on an ice water slurry , and positioned 5 cm away from five 366 nm UV bulbs in a Stratalinker 2400 ( Stratagene ) ; 0 . 05 volumes of 400 µg/ml trimethylpsoralen was added and the sample was then incubated in the dark on ice for 5 min . Samples were then irradiated by UV for 5 min . Addition of psoralen , incubation in the dark , and crosslinking were performed a total of seven times for each sample . Following crosslinking , the sample was treated with RNaseA for 2 h at 37°C followed by a Proteinase K/SDS treatment for 4 h at 55°C . DNA was extracted with phenol/chloroform and precipitated . DNA was resuspended , digested with NcoI , purified using a DNA Clean and Concentrator kit ( ZymoResearch ) , and eluted from the column with 8 µl of TEN ( 30 mM TEACl , 20 mM EDTA , 10 mM NaCl ) . Denaturing , spreading , staining with uranyl acetate , and rotary metal shadowing was performed as previously described [52] . Heterogeneous bubble sizes were due , at least in part , to the sequence specificity of psoralen intercalation , as psoralen preferentially intercalates into dinucleotides TA and AT [53]—resulting either in bubbles that were larger than the expected nucleosome size , when linker DNA failed to crosslink , or in bubbles that were smaller than the expected nucleosome size where no nucleosomes had been present ( Figure S1 ) . Because of longer linker lengths , fused nucleosome bubbles occurred seldom on promoter DNA . Smaller than expected bubbles may also have been due to crosslinking of DNA that transiently unspooled from the histone octamer . Bubbles on naked control DNA added to chromatin ring preparations measured 90 base pairs in length , on average . We therefore excluded bubbles smaller than 90 base pairs when counting promoter nucleosome bubbles . ( Bubbles attributable to preinitiation complex formation are on the order of ∼60 base pairs [28] . ) To determine promoter nucleosome positions , we determined the positions of UASp2 ( N-2 position ) , the TATA box , and the transcription start site ( N-1 position ) for every gene molecule , converting base pair distances into contour length by relating the measured contour length of the entire gene molecule ( average over both strands ) to the known length of the gene molecule in base pairs . Promoter nucleosome occupancies thus determined were in good agreement with the results of restriction nuclease accessibility assays in nuclei ( Figure S2 ) , and isolated chromatin rings [13] . Although we cannot exclude the possibility that smaller than expected bubbles were also due to intermediate structures of nucleosome assembly and disassembly , the paucity of sub-nucleosome size DNA fragments in previous micrococcal nuclease digestions of PHO5 promoter chromatin suggests that the number of such intermediates is small [14] . Images were taken on a JEOL 1230 electron microscope at 120 keV at 20 , 000-fold magnification . Images were processed and analyzed in ImageJ . At least 200 individual PHO5 molecules were analyzed for each dataset . Calculations for stochastic gene expression models were based on the following master equation [15]: ( ii ) for all j , m , n , where is the probability at time t of finding the PHO5 promoter in state j , and the cell with m transcript molecules and n protein molecules expressed under control of the PHO5 promoter; andwith the identity mapping , the “step operator” defined by , and if j is an active promoter state , but 0 otherwise . Equation ( i ) may derived from ( ii ) by summing over all m and n and applying the steady state assumption , for all j . For the two-state model ( Figure 1A ) , equation ( ii ) simplifies towith k , j = ON , OFF , , and . Solutions for steady state noise and mean were obtained analytically , as previously described [15] . Values of the kinetic parameter for nucleosome disassembly , , and sliding , , were determined by maximizing the likelihood , LT , of topology with , given the EM results R: ( iii ) where is the number of molecules with promoter nucleosome configuration j in R , and is the theoretical probability of configuration j for and given T and the stochastic process model ( ii ) . Thus , that parameter hypothesis was accepted , which maximizes the probability of the EM data R given the stochastic process model ( ii ) and topology T . The statistical support , , of topology T against topology H by data R , is ( iv ) where and are the maximum likelihoods of topologies T and H given R . All calculations were performed using Mathematica 8 ( Wolfram ) .
|
In eukaryotes , such as plants , fungi , and animals , the DNA is wrapped around basic protein cores called nucleosomes at more or less regular intervals . This wrapping discourages transcription , the first step in gene expression . By isolating PHO5 gene molecules from yeast cells and analyzing their structure by electron microscopy , we provide evidence that the “nucleosomes” completely unwrap and then re-wrap in an intrinsically stochastic manner . Only nucleosomes that wrap the regulatory sequences of the gene ( promoter ) were observed to unspool; no such unspooling was found across the body of the gene . Random unwrapping and re-wrapping generates an ensemble of alternative promoter nucleosome configurations , some conducive to transcription , others not . Mounting evidence suggests that transcription occurs in bursts , where transcripts are released in close succession , interrupted by intervals of transcriptional inactivity; this may lead to significant stochastic fluctuations in gene expression . Although the mechanism of this behavior is not understood , our findings now provide a structural basis for it , suggesting that spooling and unspooling of promoter DNA from the nucleosomes determines the fundamental frequency of transcriptional bursting .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biophysics",
"biology"
] |
2013
|
Linking Stochastic Fluctuations in Chromatin Structure and Gene Expression
|
The cell's cytoplasm is crowded by its various molecular components , resulting in a limited solvent capacity for the allocation of new proteins , thus constraining various cellular processes such as metabolism . Here we study the impact of the limited solvent capacity constraint on the metabolic rate , enzyme activities , and metabolite concentrations using a computational model of Saccharomyces cerevisiae glycolysis as a case study . We show that given the limited solvent capacity constraint , the optimal enzyme activities and the metabolite concentrations necessary to achieve a maximum rate of glycolysis are in agreement with their experimentally measured values . Furthermore , the predicted maximum glycolytic rate determined by the solvent capacity constraint is close to that measured in vivo . These results indicate that the limited solvent capacity is a relevant constraint acting on S . cerevisiae at physiological growth conditions , and that a full kinetic model together with the limited solvent capacity constraint can be used to predict both metabolite concentrations and enzyme activities in vivo .
Understanding an organism's metabolism at a system level and obtaining quantitative predictions for the different metabolic variables requires the identification and modeling of the physicochemical and regulatory constraints that are relevant at physiological growth conditions . Recently , there has been a surge of interest on how macromolecular crowding , i . e . , the crowding of the cytoplasm by various molecular components , affects cellular function , including cell metabolism [1] , [2] . At the local scale it is well known that molecular crowding affects the rate of biochemical reactions , diffusion , protein folding and protein-protein association/dissociation [2] , [3] . More recently , we have shown that macromolecular crowding acts also at a global scale by imposing a limited solvent capacity . Specifically , we have shown that a flux balance modeling framework that incorporates the limited solvent capacity is successful in predicting the maximum growth rate , the sequence of substrate uptake from a complex medium and , to an extent , the changes in intracellular flux rates upon varying growth rate of the bacterium , Escherichia coli [4] , [5] . Yet , these studies were limited by the absence of a full kinetic model of E . coli cell metabolism , hindering our ability to investigate the impact of the solvent capacity constraint on in vivo metabolite concentrations and enzyme activities . During cellular metabolism the concentration of enzymes and metabolites are continuously adjusted in order to achieve specific metabolic demands . It is highly likely that during evolution global metabolic regulation has evolved such as to achieve a given metabolic demand with an optimal use of intracellular resources . However , the size of enzymes and intermediate metabolites are dramatically different . Enzymes are macromolecules that occupy a relatively large amount of space within a cell's crowded cytoplasm , while metabolites are much smaller . This implies that metabolite concentrations are likely to be adjusted to minimize the overall “enzymatic cost” ( in terms of space cost ) . Here we study the validity of this hypothesis by focusing on the glycolysis pathway of the yeast , Saccharomyces cerevisiae , for which a kinetic model is available . We show that the maximum glycolysis rate determined by the limited solvent capacity is close to the values measured in vivo . Furthermore , the measured concentration of intermediate metabolites and enzyme activities of glycolysis are in agreement with the predicted values necessary to achieve this maximum glycolysis rate . Taken together these results indicate that the limited solvent capacity constraint is relevant for S . cerevisiae at physiological conditions . From the modeling point of view , this work demonstrates that a full kinetic model together with the limited solvent capacity constraint can be used to predict not only the metabolite concentrations , but in vivo enzyme activities as well .
The cell's cytoplasm is characterized by a high concentration of macromolecules [1] , [2] resulting in a limited solvent capacity for the allocation of metabolic enzymes . More precisely , given that enzyme molecules have a finite molar volume vi only a finite number of them fit in a given cell volume V . Indeed , if ni is the number of moles of the ith enzyme , then ( 1 ) where V0 accounts for the volume of other cell components and the free volume necessary for cellular transport as well . Equation 1 can be also rewritten as ( 2 ) where ρi = nimi/V is the enzyme density ( enzyme mass/volume ) , μi is the molar mass vspec is the specific volume , and φ = V0/V is the fraction of cell volume occupied by cell components other than the enzymes catalyzing the reactions of the pathway under consideration , including the free volume necessary for diffusion . The specific volume has been assumed to be constant for all enzymes , an approximation that has been shown to be realistic at least for globular proteins [6] . In this new form we can clearly identify the enzyme density ( or mass , given that the volume is constant ) as the enzyme associated variable contributing to the solvent capacity constraint . This choice is more appropriate than the enzyme concentration Ci = ni/V ( moles/volume ) because the specific volume is approximately constant across enzymes , while the molar volume can exhibit significant variations . For example , according to experimental data for several globular proteins [6] , the molar volume exhibits a 70% variation while the specific volume is almost constant , with a small 2% variation . The solvent capacity constraint ( Equations 1 and 2 ) thus imposes a limit to the amount of catalytic units ( i . e . , enzymes ) that can be allocated in the cell cytoplasm . In the following we show that this in turn leads to a constraint for the maximum metabolic rate . The rate of the ith reaction per unit of cell dry weight ( mol/time/mass ) is given by ( 3 ) where Ai is the specific enzyme activity , Ci is the enzyme concentration in molar units , ki is the catalytic constant and M is the cell mass . The coefficient xi is determined by the specific kinetic model: it takes values in the range of 0≤xi≤1 , and it is a function of metabolite concentrations . For example , if the ith reaction is described by Michaelis-Menten kinetics with one substrate then xi = Si/ ( Ki+Si ) , where Si is the substrate concentration and Ki is the equilibrium constant . More generally , xi is a function of the concentration of substrates , products and other metabolites regulating the enzyme activity . The fact that the reaction rates are proportional to the enzyme densities ( Equation 3 ) suggests that the limited solvent capacity constraint ( Equation 2 ) has an impact on the reaction rates as well . Indeed , from Equations 2 and 3 we obtain ( 4 ) where R is the cell metabolic rate ( or pathway rate ) , ri = Ri/R is the rate of reaction i relative to the metabolic rate , and ( 5 ) where ρ = M/V is the cell density . We refer to ai as the crowding coefficients [4] , [5] , because they quantify the contribution of each reaction rate to molecular crowding . The crowding coefficient of a reaction i increases with increasing the enzyme's molar mass μi and decreases with increasing catalytic activity ki . It is also a function of the metabolite concentrations through xi . To illustrate the impact of the limited solvent capacity constraint , we first analyze a hypothetical example , in which we use the relative reaction rates as input parameters , and the metabolite concentrations are the variables to be optimized . Given the reaction rates and the “optimal” metabolite concentrations , the enzyme activities are determined by Equation 3 . Finally , the maximum metabolic rate is computed using Equation 4 . Consider a metabolic pathway consisting of two reversible reactions converting metabolite M1 into M2 ( reaction 1 ) and M2 into M3 ( reaction 2 ) , catalyzed by enzymes e1 and e2 , respectively ( Figure 1 , inset ) . The reaction rates per unit of cell mass , R1 and R2 , are modeled by reversible Michaelis-Menten rate equations , using Equation 3 with ( 6 ) ( 7 ) where K1eq and K2eq are the equilibrium constants of reaction 1 and 2 , respectively , Kim is the Michaelis-Menten constant of metabolite m in reaction i . From Equations 4 to 7 we finally obtain ( 8 ) For the purpose of illustration , we assume 1−φ = 0 . 01 , ( mmol/h/min ) −1 ( as suggested by typical values reported in [5] ) , all Michaelis constants equal to 1 mM , and fixed pathway ends metabolite concentrations [M1] = 3 mM and [M2] = 1 mM . Furthermore , mass conservation for M2 implies that R1 = R2 = R ( r1 = r2 = 1 ) in the steady state , where R is the pathway rate . When reaction 1 is close to equilibrium [M2]≈[M1]K1eq = 3 mM , the first term in the right hand side becomes very large , resulting in a small pathway rate ( Figure 1 ) . When reaction 2 is close to equilibrium [M2]≈[M3]/K2eq = 1 mM , the second term in the right hand side becomes very large , again resulting in a small pathway rate ( Figure 1 ) . At an intermediate [M2]* between these two extremes the pathway rate achieves its maximum . Therefore , given the solvent capacity constraint , there is an optimal metabolite concentration resulting in a maximum pathway rate . Next , we investigate whether the observation of an optimal metabolite concentration for maximum pathway rate extrapolates to a more realistic scenario . For this purpose we use the glycolysis pathway of the yeast S . cerevisiae ( Figure 2A ) as a case study . Glycolysis represents a universal pathway for energy production in all domains of life . In S . cerevisiae it has been studied extensively resulting in the description of a rate equation model for each of its reactions [7] , [8] . In particular , we consider the kinetic model developed in [7] ( see Methods ) . To compare our predictions with experimentally determined values we consider the glycolysis reaction rates and metabolite concentrations reported in [7] and the enzyme activities reported in [8] . In analogy with the three metabolites case study ( Figure 1 ) , first we investigate the dependency of the glycolysis rate R , represented by the glucose uptake , on the concentration of a given metabolite . In this case we fix all other metabolite concentrations and all relative reaction rates ( reaction rate/glycolysis rate ) to their experimentally determined values . By doing so the predicted glycolysis rate is an implicit function of the free metabolite concentration alone , through Equation 4 . For example , Figure 2B displays the maximum metabolic rate R as a function of the concentration of fructose-6-phosphate ( F6P ) . R is predicted to achieve a maximum around a F6P concentration of 0 . 4 mM , close to its experimentally determined value of 0 . 5 mM [7] ( red triangle in Figure 2B ) . Similar conclusions are obtained for D-glyceraldehyde-3-phosphate ( GAP ) ( Figure 2C ) and glycerone-phosphate ( DHAP ) ( Figure 2D ) . This analysis corroborates that there is an optimal metabolite concentration maximizing R and , more importantly , that this concentration is very close to the experimentally determined metabolite concentrations . In all cases the measured metabolite concentrations are within the range of 50% or more of the maximum glycolysis rate . To further test the optimal metabolite concentration hypothesis , we perform a global optimization and simultaneously compute the optimal concentrations of the glycolysis intermediate metabolites . In this case we fix the concentrations of external glucose and co-factors and all relative reaction rates to their experimentally determined values . By doing so the predicted glycolysis rate is an implicit function of the glycolysis intermediate metabolite concentrations , through Equation 4 . The optimal intermediate metabolite concentrations are those maximizing Equation 4 . Figure 3A displays the predicted optimal metabolite concentrations as a function of their experimentally determined values ( black symbols ) , the line representing a perfect match . The agreement is remarkably good given the wide range of metabolite concentrations . For phospho-enol-pyruvate ( PEP ) , the predicted value is very sensitive to the model parameters , as indicated by the wide error bars . For fructose 1 , 6-biphosphate ( FBP ) the predicted value is smaller by a factor of five than the experimental value , but it is still within range . Taken together , these results indicate that the measured concentrations of intermediate metabolites in the S . cerevisiae glycolysis are close to the predicted optimal values maximizing the glycolysis rate given the limited solvent capacity constraint . Using the optimal intermediate metabolite concentrations we can make predictions for the enzyme activities as well . Indeed , from the first equality in Equation 3 it follows that ( 9 ) The reaction rates relative to the glycolysis rate ri are obtained from experimental data , while xi are obtained after substituting the predicted optimal metabolite concentrations on the reaction's kinetic models . Figure 3B displays the predicted enzyme activities ( in units of the glycolysis rate ) as a function of the experimentally determined values ( black symbols ) , the line representing a perfect match . In most cases we obtain a relatively good agreement between experimentally measured and predicted values , with the exception of phosphofructokinase ( pfk ) , for which the measured enzyme activities are significantly overestimated . Of note , for pyruvate kinase ( pk ) the predictions are significantly affected by the model parameters , as indicated by the wide error bars . The preceding analysis does not exclude the possibility that other constraints could result in a good agreement as well . To address this point we consider the more general optimization objective R = ( 1−φ ) /ΣNi = 1 ( airi ) H , parametrized by the exponent H . Although this objective is not inspired by a biological intuition , it allows us to explore other possibilities beyond the original case H = 1 . Figure 3 show our predictions for the case H = 0 . 1 ( red symbols ) and H = 10 ( blue symbols ) , representing a milder and a stronger dependency with the crowding coefficients ai , respectively . For H = 0 . 1 , 1 . 0 and 10 the predicted metabolite concentrations are in good agreement with the experimental values . Furthermore , when we allow sub-optimal metabolite concentrations resulting in a glycolysis rate below it s maximum our predictions are also in the range of the experimental values ( see Protocol S1 , Table IV ) . These results indicate that it is sufficient that the optimization objective is a monotonic decreasing function of the crowding coefficients . When the latter is satisfied the metabolite concentrations are up to a great extent constrained by the kinetic model . This is not , however , the case for the enzyme activities . For H = 0 . 1 and the enzymes pfk , tpi and pk , there is a large deviation from the perfect match line . For H = 10 and the enzymes tpi and pk , there is a large deviation from the perfect match line as well . Overall , H = 1 gives the better agreement between enzyme activity predictions and their measured values . In addition , it provides a clear biophysical interpretation of the solvent capacity constraint ( H = 1 ) . Finally , we use Equation 4 to compute the maximum glycolysis rate as determined by the limited solvent capacity constraint . The global optimization predicts the glycolysis rate R = ( 1−φ ) ×12 . 5 mmol/min/g dry weight . Taking into account that about 30% [9] of the cell is occupied by cell components excluding water , that proteins account for ∼45% of the dry weight [10] , and that of these glycolytic enzymes account for ∼22% [11] of the protein mass we obtain 1−φ∼0 . 03 . Therefore , given that glycolysis enzymes occupy only 3% of the cell volume , we obtain R∼0 . 38 mmol/min/g dry weight . This prediction is in very good agreement with the experimentally determined glycolysis rate of S . cerevisiae , ranging between 0 . 1 to 0 . 4 mmol/min/g dry weight [8] , [12] .
The successful modeling of cell metabolism requires the understanding of the physicochemical constraints that are relevant at physiological growth conditions . In our previous work focusing on E . coli we have reported results indicating that the limited solvent capacity is an important constraint on cell metabolism , especially under nutrient-rich growth conditions [4] , [5] . Using a flux balance approach that incorporates this constraint we predicted the maximum growth rate in different carbon sources [4] , the sequence and mode of substrate uptake and utilization from a complex medium [4] , and the changes in intracellular flux rates upon varying E . coli cells' growth rate [5] . More importantly , these predictions were in good agreement with experimentally determined values . Here we have extended the study of the impact of the limited solvent capacity by ( i ) considering a different organism ( S . cerevisiae ) , and ( ii ) a full kinetic model of glycolysis . Using the full kinetic model of S . cerevisiae glycolysis , we have demonstrated that the predicted optimal intermediate metabolite concentrations and enzyme activites are in good agreement with the corresponding experimental values . Discrepancies were only observed in association with two different steps in the glycolysis pathway , namely the reaction catalyzed by pfk and the reactions between BPG and PEP . The experimental values measurements from cell extracts [8] and , except for potential experimental caveats , they represent phyiological conditions . We thus we believe that the larger deviations for these enzymes are determined by inconsistencies in the kinetic model equations and/or kinetic model parameters . Finally , the glycolysis rate achieved at the optimal metabolite concentrations is in the range of the experimentally measured values . From the quantitative modeling point of view our results indicate that a full kinetic model together with the solvent capacity constraint can be used to make predictions for the metabolite concentrations and enzyme activities . Thus , we propose the simultaneous optimization of intermediate metabolite concentrations , maximizing the metabolic rate given the solvent capacity , as a method to computationally predict the concentrations of a metabolic pathway's intermediate metabolites and enzyme activities . We have demonstrated the applicability of this method by computing the concentration of S . cerevisiae glycolysis intermediate metabolites , resulting in a good agreement with published data . The hypothesis that high concentration of macromolecules in the cell's cytoplasm imposes a global constraint on the metabolic capacity of an organism has been studied in the past [13] , [14] , [15] . In most cases [14] , [15] it has been postulated that there is a bound to the total enzyme concentration ( moles/volume ) . Yet , -to our knowledge- , no clear explanation has been provided to support that choice . In contrast , our starting postulate is an undeniable physical constraint , the total cell volume ( Equation 1 ) . Under this constraint , the enzyme molar volumes are the primary magnitude quantifying the enzymatic cost . In turn , since the enzyme-specific volumes are approximately constant , we can use the enzyme density ( mass/volume ) as an alternative measure of enzymatic cost . This modeling framework has advantages and disadvantages with respect to more traditional approaches based on dynamical systems modeling . As an advantage , our method does not require as input parameters the enzyme activities but rather make quantitative predictions for them . On the other hand , our method is based on a steady-state approximation . Therefore , in its present form , it cannot be used to understand dynamical processes , such as the observed metabolite concentration oscillations in S . cerevisiae cells when growing at high glucose concentrations [7] .
We use the S . cerevisiae glycolysis model reported in [7] ( see Protocol S1 for details ) . The only modification is the extension of the phsophofructokinase ( pfk ) kinetic model from an irreversible to a reversible model . The catalytic constants were obtained from experimental estimates for Saccharomyces carlsbergensis [16] , except for glycerol 3-phosphate dehydrogenase that was obtained from an estimate for Eidolon helvum [17] . For the cell density we use an estimate reported for E . coli , ρ = 0 . 34 g/ml [18] . The specific volume was estimated for several proteins using the molar volumes and masses reported in [6] , resulting in average of 0 . 73 ml/g and standard deviation of 0 . 02 ml/g . See Protocol S1 for details . The optimal metabolite concentrations are obtained maximizing Equation 4 with respect to the free metabolite concentrations . In the case of Figure 2B–2D , all metabolite concentrations are fixed to their experimental values , except for the metabolite indicated by the X-axis . In the case of Figure 3A and 3B , all intermediate metabolite concentrations are optimized , keeping fixed the concentration of external glucose and cofactors ( ATP , ADP , AMP , NADH , NAD ) . In both cases the reaction rates relative to the glycolysis rate ( ri ) were taken as input parameters , using the values reported in [7] . The maximization was performed using simulated annealing [19] . To analyze the sensitivity of our predictions to the model parameters we have generated random sets of kinetic parameters , assuming a 10% variation of the fixed metabolite concentrations and all kinetic constants except for the catalytic activities . For the latter we assumed a larger variation of 50% , because they were estimated from a different organism . For each set of parameters we make predictions for the metabolite concentrations and enzyme activities . Figure 3 reports the mean values and standard deviations .
|
The concentration of enzymes and metabolites is continuously adjusted in order to achieve specific metabolic demands . It is highly likely that during evolution global metabolic regulation has evolved such as to achieve a given metabolic demand with an optimal use of intracellular resources . However , the size of enzymes and intermediate metabolites is dramatically different . Enzymes are macromolecules that occupy a relatively large amount of space within a cell's crowded cytoplasm , while metabolites are much smaller . This implies that metabolite concentrations are likely to be adjusted to minimize the overall “enzymatic cost” ( in terms of space cost ) . In this work , we explore this hypothesis using Saccharomyces cerevisiae glycolysis as a case study . Our results indicate that metabolite concentrations attain optimal values , minimizing the intracellular space occupied by metabolic enzymes . And , at these optimal concentrations , glycolysis achieves the maximum rate given the intracellular volume fraction occupied by glycolysis enzymes . Taken together with previous studies for Escherichia coli , our results indicate that macromolecular crowding is a general constraint on cell metabolism .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"biophysics/theory",
"and",
"simulation",
"computational",
"biology/metabolic",
"networks"
] |
2008
|
Impact of Limited Solvent Capacity on Metabolic Rate, Enzyme Activities, and Metabolite Concentrations of S. cerevisiae Glycolysis
|
Reference panels from the 1000 Genomes ( 1000G ) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency ≥0 . 5% across European ancestry populations . Within the European Network for Genetic and Genomic Epidemiology ( ENGAGE ) Consortium , we have undertaken the first large-scale meta-analysis of genome-wide association studies ( GWAS ) , supplemented by 1000G imputation , for four quantitative glycaemic and obesity-related traits , in up to 87 , 048 individuals of European ancestry . We identified two loci for body mass index ( BMI ) at genome-wide significance , and two for fasting glucose ( FG ) , none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry . Through conditional analysis , we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI ( RSPO3 ) and FG ( GCK and G6PC2 ) . The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele , H177Y , which has recently been demonstrated to have a functional role in glucose regulation . Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements , which for FG mapped to promoter and transcription factor binding sites in pancreatic islets , in particular . Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci , integrated with genomic annotation in relevant tissues , can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated .
Quantitative human glycaemic and obesity-related traits , including fasting plasma glucose and insulin ( FG and FI ) , body mass index ( BMI ) , and waist-hip ratio ( WHR ) are highly heritable [1–5] , and are well established risk factors for type 2 diabetes ( T2D ) and cardiovascular disease [6–10] . Large-scale genome-wide association studies ( GWAS ) have proved to be extremely successful in the identification of loci harbouring genetic variants contributing to these traits in multiple ethnic groups [11–27] . This process has been facilitated by technical advances in the development of imputation methods [28] that allow evaluation of association with genetic variants not directly assayed on genotyping arrays , but present instead in more dense phased reference panels , such as those made available through the International HapMap Consortium [29 , 30] . However , the detected loci are typically characterised by common variant association signals , defined by lead SNPs with minor allele frequency ( MAF ) of at least 5% , which extend over large genomic intervals because of linkage disequilibrium ( LD ) . They also often map to non-coding sequence , making direct biological interpretation of their effect more difficult than for non-synonymous variants . The lead SNPs at GWAS loci are overwhelmingly of modest effect , and together account for only a small proportion ( generally less than 5% ) of the overall trait variance [17–19 , 26 , 27] . As a consequence , there has been limited progress in identifying the genes through which GWAS association signals are mediated , and characterisation of the downstream molecular mechanisms influencing glycaemic and obesity-related traits remains a considerable challenge . There has been much recent debate as to the role that low frequency and rare variation ( MAF<5% ) might play in explaining the “missing heritability” of complex human traits [31–33] . It has been hypothesized that some of these variants will have larger effects on traits than common SNPs because they are likely to have arisen as a result of relatively recent mutation events , and thus will have been less subject to purifying selection [34] . Unfortunately , such variation is not well captured by traditional GWAS genotyping arrays , by design , even when supplemented by HapMap imputation [35–37] . However , more recent , higher density reference panels released by the 1000 Genomes ( 1000G ) Project Consortium [38] , constructed on the basis of low-pass whole-genome re-sequencing , provide haplotypes at more than 37 million variants for 1 , 094 individuals from multiple ethnic groups , and facilitate imputation of genetic variation with MAF as low as 0 . 5% across diverse populations [39–41] . Within the European Network for Genetic and Genomic Epidemiology ( ENGAGE ) Consortium , we sought to assess the advantages and limitations of high-density imputation for the discovery and fine-mapping of loci for glycaemic and obesity-related traits . We considered 22 European ancestry GWAS ( S1 Table ) , each imputed up to the 1000G “all ancestries” reference panel ( Phase 1 interim release , June 2011 ) , in up to ( after quality control ) : 87 , 048 individuals for BMI; 54 , 572 individuals for WHR; 46 , 694 individuals for FG; and 24 , 245 individuals for FI ( S2 and S3 Tables ) . To account for the impact of overall obesity on central adiposity [18 , 27] and insulin sensitivity [19] , we considered WHR and FI after adjustment for BMI ( denoted WHRadjBMI and FIadjBMI , respectively ) . With these high-density imputed data , we aimed to: ( i ) discover novel signals of association for glycaemic and obesity-related traits , including within established GWAS loci; ( ii ) evaluate the impact of low-frequency variation to common SNP GWAS signals; ( iii ) consider the contribution of genetic variants at GWAS loci in explaining trait variance; and ( iv ) refine the localisation of potential causal variants underlying GWAS association signals and assess the mechanisms through which they impact glycaemic and obesity-related traits .
Within each study , we performed stringent quality control of the genotype scaffold before imputation , minimally including sample and variant call rate and deviation from Hardy-Weinberg equilibrium ( S1 Table ) . Each scaffold was imputed up to the 1000G multi-ethnic reference panel ( Phase 1 interim release , June 2011 ) , which includes 762 European ancestry haplotypes , using IMPUTEv2 [42] , minimac [39] or specialist in-house software ( S1 Table ) . Making use of the multi-ethnic reference panel , including haplotypes from all ancestry groups , has been demonstrated to reduce error rates and to improve imputation quality , particularly of lower frequency variants [28] . Imputed variants were retained for downstream evaluation and association testing if they passed traditional GWAS quality control thresholds ( IMPUTEv2 info score ≥ 0 . 4; minimac r2 ≥ 0 . 3 ) [43] . We considered the quality of imputation ( as measured by the IMPUTEv2 info score ) of variants from the 1000G reference panel in two contributing studies ( S4 Table ) : the 1958 British Birth Cohort from the Wellcome Trust Case Control Consortium ( 58BC-WTCCC , 2 , 802 individuals from Great Britain ) ; and the 1966 Northern Finnish Birth Cohort ( NFBC1966 , 5 , 276 individuals from Lapland and the Province of Oulu in Northern Finland ) . In 58BC-WTCCC , 98 . 8% of common SNPs ( MAF≥5% , 6 . 3 million ) and 97 . 0% of low-frequency variants ( 0 . 5%≤MAF<5% , 3 . 8 million ) passed imputation quality control filters , of which 72 . 9% are not present in HapMap reference panels . However , imputation of rarer variants ( 0 . 1%≤MAF<0 . 5% , 3 . 4 million ) proved less successful in 58BC-WTCCC , with only 80 . 5% passing quality control filters . The quality of imputation in NFBC1966 was comparable to that observed in 58BC-WTCCC: 99 . 7% of common SNPs ( 5 . 9 million ) and 94 . 4% of low-frequency variants ( 3 . 7 million ) . However , amongst rarer variants , the quality of imputation was noticeably poorer in NFBC1966 ( 62 . 8% ) than 58BC-WTCCC , presumably reflecting less representation of low-frequency haplotypes from the isolated Northern Finnish population in the 1000G reference panel . We have demonstrated that high-density imputation provides >90% coverage of low-frequency variants present in the 1000G reference panel in two diverse European ancestry populations . Our study thus enables association testing with more than three million high-quality variants with 0 . 5%≤MAF<5% that would not have been directly interrogated in previous GWAS of glycaemic and obesity-related traits that have been supplemented by HapMap imputation alone . With the sample sizes available in this study , we have estimated that for any of these variants explaining at least 0 . 2% of the overall trait variance ( i . e . effect size of 0 . 32 SD units for 1% MAF , and effect size of 0 . 15 SD units for 5% MAF ) , we have >99 . 9% power to detect their association with BMI , WHR , and FG , and >93 . 9% power to detect their association with FI . Within each study , we tested for association of each directly typed and well imputed variant with BMI , WHRadjBMI , FG and FIadjBMI , separately in males and females , in a linear regression modelling framework ( Methods , S2 and S3 Tables ) . Association summary statistics were then combined across studies in sex-specific and sex-combined fixed-effects meta-analyses for each trait . Variants passing quality control in fewer than 50% of the contributing studies for each trait were excluded from the meta-analysis . Association signals at genome-wide significance ( p<5x10-8 ) and with lead SNPs independent ( r2<0 . 05 ) and mapping more than 2Mb from those previously reported for the traits were considered novel . By convention , loci were labelled with the name ( s ) of the gene ( s ) located closest to the lead SNP , unless more compelling biological candidates mapped nearby ( Table 1 , S1 , S2 , S3 and S4 Figs ) . We identified two novel loci achieving genome-wide significance for BMI in the sex-combined meta-analysis: ATP2B1 ( rs1966714 , MAF = 0 . 46 , p = 1 . 9x10-8 ) ; and AKAP6 ( rs12885467 , MAF = 0 . 49 , p = 4 . 5x10-8 ) . For FG , we detected one novel locus in the sex-combined meta-analysis at RMST ( rs17331697 , MAF = 0 . 10 , p = 1 . 3x10-11 ) and a female-specific association at EMID2 ( rs6947345 , MAF = 0 . 017 , pMALE = 0 . 50 , pFEMALE = 3 . 8x10-8 ) . We did not identify any novel loci at genome-wide significance , in either sex-combined or sex-specific analyses , for WHRadjBMI or FIadjBMI . We observed no evidence of heterogeneity in sex-specific allelic effects across studies at the lead SNPs at the novel loci ( Table 1 ) . With the exception of the sex-specific association signal at EMID2 , the lead SNPs at all other novel loci were common . At AKAP6 and RMST , the common lead SNPs were present in HapMap ( S5 Fig ) but did not achieve genome-wide significance in large-scale European ancestry HapMap imputed meta-analyses conducted by the GIANT Consortium [17] ( for BMI in up to 123 , 865 individuals ) and the MAGIC Investigators [16] ( for FG in up to 46 , 186 individuals ) , despite substantial overlap with cohorts contributing to our study . We have estimated that , amongst individuals contributing to our 1000G imputed meta-analyses for BMI/FG , a maximum of 59%/37% also participated in the previous GIANT and MAGIC studies ( S5 Table ) . At RMST , our lead FG SNP approaches genome-wide significance in the MAGIC meta-analysis ( p = 6 . 5x10-6 ) , and this likely reflects stochastic variation . However , at AKAP6 , our lead BMI SNP demonstrates only nominal evidence of association ( p = 0 . 012 ) in the GIANT meta-analysis , suggesting that 1000G reference panels have enabled higher quality imputation at this locus . To investigate this assertion further , we compared the quality of imputation of the lead BMI SNP using HapMap and 1000G reference panels in two contributing studies of diverse European ancestry . In 58BC-WTCCC/NFBC1966 , there was a marginal improvement in the IMPUTEv2 info score from 0 . 972/0 . 939 using reference haplotypes from CEU HapMap to 0 . 996/0 . 971 using those from 1000G . At ATP2B1 , the common lead SNP was not present in HapMap ( S5 Fig ) . The lead SNP for BMI from the GIANT HapMap imputed meta-analysis [17] was rs2579106 , achieving nominal evidence for association ( p = 6 . 4x10-5 ) in a reported sample size of 123 , 864 individuals . This SNP reached near genome-wide significance in our 1000G imputed meta-analysis , despite the smaller sample size ( p = 3 . 3x10-7 , in 86 , 955 individuals ) . Furthermore , the HapMap and 1000G lead SNPs are in only modest LD with each other ( EUR r2 = 0 . 22 ) . Taken together , these data suggest that the discovery of this novel locus has been due to improved coverage through 1000G imputation , despite the lead SNP being common . We observed genome-wide significant evidence of association at 34 established loci for glycaemic and obesity-related traits , including GCKR with the same lead SNP for both FG and FI ( S6 Table ) . At 29 of these loci , our meta-analysis identified lead SNPs that were different from previous reports in which they were first discovered , of which 23 were not present in HapMap ( S7 Table ) . At 18 of these 29 loci , the new lead SNP was in strong LD ( r2≥0 . 8 ) with that previously reported , and consequently both variants had similar MAF and allelic effect size ( S6 Fig ) . At a further nine of the 29 loci , the new and previously reported lead SNPs were in moderate LD ( 0 . 2≤r2<0 . 8 ) with each other . For these , there was greater difference in MAF and allelic effect size for each pair of variants , but the new lead SNP was common and not consistently less frequent ( S6 Fig ) . At the remaining two loci , the new lead SNPs were not present in HapMap and were in only weak LD with those previously reported ( S7 Fig ) , mapping near BDNF for BMI ( r2 = 0 . 10 ) and RSPO3 for WHRadjBMI ( r2 = 0 . 04 ) . At both loci , multiple distinct signals of association have been recently reported by the GIANT Consortium in the largest meta-analyses of BMI and WHRadjBMI in European ancestry individuals genotyped with GWAS arrays , supplemented by imputation up to reference panels from the International HapMap Consortium [29 , 30] , and the Metabochip , in up to 339 , 224 and 224 , 459 individuals , respectively [26 , 27] . At BDNF , our new lead SNP ( rs4517468 ) was in moderate LD ( r2 = 0 . 31 ) with the index variant ( rs10835210 ) for the GIANT secondary signal of association for BMI at this locus , suggesting that they represent the same underlying effect on obesity . At established loci , amongst the 29 lead SNPs identified in our 1000G imputed meta-analysis that were different from the previous reports in which they were discovered , five of them are present on the Metabochip: NRXN3 ( BMI , rs7141420 ) , SH2B1 ( BMI , rs2008514 ) , MC4R ( BMI , rs663129 ) , LY86 ( WHRadjBMI , rs1294437 ) , and GCKR ( FG/FIadjBMI , rs1260326 ) . These variants were thus directly interrogated in the largest European ancestry meta-analyses , to date , of glycaemic and obesity related traits from the GIANT Consortium [26 , 27] and MAGIC Investigators [19] that made use of this array . At all five of these loci , our new lead SNP is either the same or is in strong LD ( EUR r2>0 . 75 ) with that reported in the trait-equivalent Metabochip effort . Four of these loci ( all except NRXN3 ) were densely typed as “fine-mapping” intervals on the array , providing evidence that 1000G imputation has been successful at predicting genotypes at untyped variants in these regions , even though the GWAS scaffolds used in our investigation were comparatively sparse . We investigated the evidence for multiple distinct association signals in the glycaemic and obesity-related trait loci achieving genome-wide significance in our study ( four novel and 34 established ) ( Table 1 and S6 Table ) . We undertook approximate conditional analyses , implemented in GCTA [44] , to select index SNPs for distinct association signals achieving “locus-wide” significance ( pCOND<10−5 ) to reflect the number of uncorrelated variants in a 2Mb window flanking the lead SNP ( Methods ) . We made use of summary statistics from the meta-analysis and genotypes from 58BC-WTCCC and NFBC1966 to approximate the LD between genetic variants ( directly typed and well imputed ) and hence the correlation in parameter estimates in the joint association model . Reassuringly , the index SNPs and association summary statistics ( effect sizes and p-values ) from the joint model were highly concordant for both reference studies ( S8 Table ) . Finally , we confirmed these GCTA association signals through exact reciprocal conditional analyses by adjustment for genotypes at each index SNP as a covariate in the linear regression model ( Methods , Fig 1 , Table 2 ) . We identified two distinct signals of association for WHRadjBMI mapping to the RSPO3 locus , indexed by rs72959041 ( MAF = 0 . 079 , pCOND = 2 . 5x10-10 ) and rs4509142 ( MAF = 0 . 49 , pCOND = 5 . 8x10-6 ) , corresponding to our new lead SNP and that previously reported [18] , respectively . More recently , both signals have also been reported by large-scale meta-analyses undertaken by the GIANT Consortium [27] . Our new lead SNP ( rs72959041 ) was reported as the index variant for their secondary association signal at this locus , whilst the index variant for our secondary signal of association ( rs4509142 ) was in strong LD with their lead SNP ( rs1936805 , r2 = 0 . 67 ) . The GIANT Consortium also identified a third distinct signal of association at this locus , stronger in females than in males , which was not detected in our conditional analyses , and presumably reflects reduced power due to our smaller sample size . We also identified two distinct signals of association for FG each mapping to GCK ( rs878521 , MAF = 0 . 21 , pCOND = 1 . 3x10-18; rs10259649 , MAF = 0 . 27 , pCOND = 4 . 6x10-10 ) and G6PC2 ( rs560887 , MAF = 0 . 31 , pCOND = 2 . 2x10-66; rs138726309 , MAF = 0 . 015 , pCOND = 5 . 7x10-23 ) . None of the index variants for these distinct association signals was present in HapMap ( S8 Fig ) , and only rs10259649 in GCK was well represented by a tag in that reference panel ( rs2908292 , r2 = 1 . 00 ) . We evaluated the additional heritability of glycaemic and obesity-related traits explained by lead SNPs at novel and established loci after 1000G imputation in 5 , 276 individuals from NFBC1966 ( Methods ) . For each trait , we calculated the phenotypic variance accounted for by: ( i ) previously reported lead SNPs at established loci; and ( ii ) new lead SNPs and index variants for distinct association signals at novel and established loci from the present study . The greatest increment in variance explained was observed for FG , where the novel loci and new lead SNPs after 1000G imputation together account for an increase from 1 . 9% to 2 . 3% . We also observed noticeable increments in variance explained after 1000G imputation for WHRadjBMI ( from 1 . 1% to 1 . 3% ) and BMI ( 3 . 2% to 3 . 5% ) . However , for FIadjBMI , only one new lead SNP at an established locus was identified after 1000G imputation , providing a negligible improvement in variance explained ( from 0 . 46% to 0 . 47% ) . We sought to take advantage of the improved coverage of common and low-frequency variation offered by 1000G imputation to localise potential causal variants ( MAF≥0 . 5% ) for the 42 distinct association signals achieving locus-wide significance in our conditional meta-analyses ( two distinct signals of association each at RSPO3 , GCK , and G6PC2 , one signal of association for both FG and FIadjBMI at the GCKR locus , and one signal of association at each of the other 34 novel and established loci ) . For each distinct signal , we constructed 99% credible sets of variants [45] that together account for 99% probability of driving the association on the basis of the ( conditional ) meta-analysis ( Methods , S9 Table ) . At the 29 established loci where we identified a new lead SNP after 1000G imputation , the posterior probability of driving the association signal was consistently higher than that for the variant previously reported ( S9 Fig ) . The greatest increases in posterior probability were observed at: GCKR ( FG/FIadjBMI , increase from 2 . 6%/1 . 8% to 93 . 5%/89 . 6% ) ; RSPO3 ( WHRadjBMI , increase from 0 . 4% to 78 . 6% ) ; PROX1 ( FG , increase from 13 . 2% to 76 . 9% ) ; and NRXN3 ( BMI , increase from 2 . 5% to 62 . 2% ) . Credible sets are well calibrated for common and low-frequency variants provided that imputation and meta-analysis provides complete coverage of variation with MAF≥0 . 5% at each locus . Smaller credible sets , in terms of the number of variants they contain , thus correspond to fine-mapping at higher resolution . We considered 99% credible sets containing fewer than 20 variants to be “tractable” , and amenable to follow-up through additional analyses of functional and regulatory annotation ( Table 3 , S10 Table ) . The most precise localisation was observed for FG loci including: MTNR1B ( rs10830963 accounts for more than 99 . 9% of the probability of driving the association ) ; both distinct signals at G6PC2 ( two variants each , mapping to <15kb interval ) ; and one signal at GCK ( indexed by rs878521 , mapping to <25kb interval ) . Of the 127 variants reported in these tractable credible sets , 74 ( 58 . 3% ) were not present in HapMap , and accounted for 42 . 4% of the probability of driving the association signals . None of the HapMap variants in the tractable credible sets was of low-frequency , compared to 20 . 8% of those present only in 1000G ( S11 Table ) . The tractable credible sets included coding variants at just three loci implicated in FG: GCKR , SLC30A8 , and the low-frequency association signal at G6PC2 . The lead SNP mapping to GCKR ( rs1260326 ) was the common coding variant L446P , which accounts for 93 . 5% of the probability of driving the FG association signal , and was present in HapMap . At the SLC30A8 locus , the probability of driving the association for FG was shared between 7 SNPs , in strong LD with each other , and including the coding variant R325W . This variant was present in HapMap , and was sufficient to explain the association signal of the lead non-coding SNP for FG in conditional analysis ( rs11558471 , p = 3 . 2x10-10 , pCOND = 0 . 052 ) at the locus . SLC30A8 R325W is also the lead SNP for T2D susceptibility at this locus in published European ancestry meta-analyses from the DIAGRAM Consortium [46] . Finally , the low-frequency index SNP for the secondary association signal mapping to G6PC2 ( rs138726309 , MAF = 0 . 015 ) was the coding variant H177Y , which accounts for 11 . 2% of the posterior probability of causality at this locus . For this association signal , none of the variants in the 99% credible set was present in HapMap , and thus would have been overlooked without 1000G imputation . This coding variant has recently been implicated in FG homeostasis in a meta-analysis of 33 , 407 non-diabetic individuals of European ancestry , genotyped with the Illumina exome array , and in agreement with our study , demonstrates a stronger signal of association in conditional analysis after accounting for the lead SNP at the G6PC2 locus [47] . The remaining variants in the tractable credible sets mapped to non-coding sequence . To gain insight into potential regulatory mechanisms through which these variants might impact glycaemic and obesity-related traits , we overlaid each of these credible sets , in turn , with chromatin state calls from eleven cell lines and tissues ( Methods ) . Across all traits , 99% credible set variants were enriched for overlap with enhancer elements ( Fig 2 ) . Focussing on FG , variants within the 99% credible set showed significant enrichment ( p<2 . 4x10-3 ) for active promoter and transcription factor binding site annotations compared to all others ( respectively: 3 . 8-fold , Fisher's combined p = 9 . 4x10-5; and 7 . 2-fold , Fisher’s combined p = 2 . 1x10-13 ) . Over cell types , this enrichment was most prominent in pancreatic islets ( Fig 2 ) . More than half of islet-annotated variants are not present in HapMap , and this would not have been observed without 1000G imputation . For example , at the novel FG RMST locus , 11 of the 14 variants in the 99% credible set are not present in HapMap , but all overlap active islet chromatin marks ( S10 Fig ) .
Through meta-analysis of 1000G imputed GWAS of glycaemic and obesity-related traits , we have identified two novel loci for BMI at genome-wide significance , and two for FG ( including one low-frequency variant association signal that is specific to females ) . These loci were not reported in larger meta-analysis efforts of European ancestry undertaken by the GIANT Consortium ( for BMI ) and the MAGIC Investigators ( for FG ) , despite the partial overlap of contributing studies [16–19 , 26 , 27] . Improved coverage and quality of imputation for common and low-frequency variation using 1000G reference panels has increased power . We also reported new lead SNPs at 29 established glycaemic and obesity-related trait loci achieving genome-wide significance in our meta-analyses , of which 23 were not present in HapMap , and identified multiple distinct signals of association for WHRadjBMI at RSPO3 and for FG at GCK and G6PC2 . Taken together , these novel loci , distinct association signals , and new lead SNPs have increased the trait variance explained for glycaemic and obesity-related traits , although the majority of the heritability remains unaccounted for . Despite more than 90% coverage of low-frequency variation after 1000G imputation , in diverse European ancestry populations , and equivalent power to detect association across the allele frequency spectrum for a fixed proportion of trait variance explained , the new lead SNPs at established and novel GWAS loci are predominantly common . These data argue strongly against the “synthetic association” hypothesis , which posits that common lead SNPs at GWAS loci will often reflect unobserved causal variants of lower frequency and greater effect size [32] . We recognise that our study has insufficient power to detect common or low-frequency association signals of more modest effect ( S12 Table ) . For example , we estimated that the power to detect association in this study , at genome-wide significance , of a variant of 1% MAF , explaining 0 . 05% of the overall trait variance ( effect size of 0 . 16 SD units ) , was 88 . 0% for BMI , but just 42 . 1% for WHRadjBMI , 27 . 7% for FG , and only 2 . 6% for FIadjBMI . Furthermore , the contribution of rare variants to glycaemic and obesity-related traits cannot be directly investigated with these data because of the low quality imputation for MAF<0 . 5% , but will require interrogation through deep whole-genome re-sequencing studies in large sample sizes . We have demonstrated that integration of 1000G imputation , genetic fine-mapping , and genomic annotation , facilitates fine-mapping of GWAS loci for glycaemic and obesity-related traits , and has provided insight into potential functional and regulatory mechanisms through which the effects of these association signals are mediated . In particular , variants in the 99% credible set for the low-frequency association signal mapping to G6PC2 are completely absent from HapMap , but include H177Y . The glucose lowering allele at this variant has been demonstrated to result in a significant decrease in protein expression mediated through proteasomal degradation , leading to a loss of G6PC2 function [47] . We also demonstrated enrichment for overlap of functional elements with variants in the tractable credible sets mapping to non-coding sequence , in particular enhancers . For FG , additional enrichment was observed across credible set variants mapping to promoter and transcription factor binding sites in pancreatic islets , in particular . Uncovering these types of enrichment is essential for prioritisation of variants for functional follow-up , and can be incorporated in statistical models to elucidate causal alleles . Also , at the level of an individual locus , functional annotation can help point to the underlying molecular mechanism through which the GWAS signal is mediated . At G6PC2 , for example , the lead SNP , rs560887 , in the 99% credible set for the second distinct ( non-coding ) association signal at this locus ( 79 . 5% posterior probability ) maps to an enhancer region that is active in pancreatic islets and embryonic stem cells , but repressed in most other cell types . These observations are in agreement with recent reports of clustering of T2D-associated risk variants in islet enhancers [48] and highlights a potential mechanism through which GWAS loci impact glucose homeostasis and disease risk . Despite the success of traditional GWAS genotyping arrays for the discovery of common variant association signals for complex human traits , because of the structure of LD for variation with MAF>5% , the gold standard approach to directly interrogating lower frequency variation is through re-sequencing studies . However , in agreement with recently published investigations of the contribution of low-frequency variants to a range of phenotypes [47 , 49–51] , our study highlights that effect sizes are modest , and require sample sizes for detection that are financially infeasible through re-sequencing on the scale of the whole genome ( or exome ) . We have demonstrated , in this study , that imputation of existing GWAS scaffolds up to reference panels from the 1000 Genomes Project Consortium [38] enables imputation of more than 90% of low-frequency variants in diverse European populations , at no additional cost other than computation and analyst time . Future GWAS of complex traits in European ancestry populations will be further enhanced by the Haplotype Reference Consortium ( www . haplotype-reference-consortium . org ) . This effort will create a reference panel of more than 60 , 000 haplotypes from re-sequencing of multiple cohorts , predominantly of European ancestry , enabling high-quality imputation to lower allele frequencies . Phase 3 of the 1000 Genomes Project includes haplotypes from diverse populations from each the five major global ethnicities , and thus would be expected to improve imputation quality over Phase 1 for low-frequency variants in East Asian , South Asian , African and American ancestry groups . The viability of imputation as an approach to recover genotypes at low-frequency variants in GWAS undertaken in populations that are not well represented by the 1000 Genomes Project might require whole-genome re-sequencing of some individuals from the study , in combination with haplotypes from the existing reference panel . Irrespective of the population under investigation , our study suggests that imputation is unlikely to provide sufficient coverage of variation with MAF<0 . 5% to enable gene-based testing of rare variants [52] . Imputation is restricted to those rare variants that are present in the reference panel , which are much more likely to be population specific . Furthermore , imputation of rare variants that are present in the reference panel is generally poor , although it is not clear how well calibrated the traditional metrics of quality ( such as IMPUTEv2 info score ) will be . Thorough investigation of the impact of rare variation on phenotype will thus require re-sequencing , although some success in discovering rare coding variants associated with complex human traits has been achieved through exome array genotyping [47 , 53–55] . For the time being , arrays that combine an imputation scaffold with direct interrogation of rare coding variation likely offer the most cost-effective approach to assaying variants across the frequency spectrum . In conclusion , our study has enabled discovery and fine-mapping of novel and established association signals for glycaemic and obesity-related traits , and through integration with genomic data from relevant tissues , has highlighted functional and regulatory processes through which these effects are mediated . Improved understanding of the biological basis of the quantitative human anthropometric and metabolic traits may advance our appreciation of the mechanisms underlying downstream disease endpoints , including T2D and cardiovascular diseases , ultimately leading to personalised treatment approaches , therapeutic development and public health benefits .
All human research was approved by the relevant institutional review boards , and conducted according to the Declaration of Helsinki . All participants provided written informed consent . We considered 22 population-based and case-control GWAS of European ancestry in up to ( after quality control ) : 87 , 048 individuals for BMI; 54 , 572 individuals for WHRadjBMI; 46 , 694 individuals for FG; and 24 , 245 individuals for FIadjBMI . Samples were limited to individuals of at least 18 years of age . Case-control studies were stratified by disease status , with each stratum analysed separately . Full details of study and sample characteristics are provided in S1 Table . Samples were genotyped with a variety of GWAS arrays . Sample and SNP quality control was undertaken within each study . Sample quality control included exclusions on the basis of genome-wide call rate , extreme heterozygosity , sex discordance , cryptic relatedness , and outlying ethnicity . SNP quality control included exclusions on the basis of call rate across samples and extreme deviation from Hardy-Weinberg equilibrium . Non-autosomal SNPs were excluded from imputation and association analysis . SNPs with MAF<1% were also excluded from the genotype scaffold prior to imputation . Full details of the genotyping arrays and quality control protocols employed by each study are summarised in S1 Table . Within each study , the autosomal GWAS genotype scaffold was imputed up to the 1000 Genomes Project multi-ethnic reference panel ( Phase I interim release , June 2011 ) , which was the most up to date available at the time analyses were undertaken . Imputation was performed using IMPUTEv2 [42] , minimac [39] or specialist in-house software . Poorly imputed variants ( IMPUTE info<0 . 4; minimac r^2<0 . 3 ) [43] , and those with minor allele count of less than three ( under a dosage model ) were excluded from downstream association analyses . We utilised protocols for obesity-related and glycaemic trait transformations developed by the GIANT Consortium [17 , 18] and MAGIC Investigators [19] . Full details of trait transformations , trait summary statistics and study-specific covariates are presented in S2 and S3 Tables . BMI was calculated as the ratio of weight ( kg ) to squared height ( m2 ) . BMI was inverse normal transformed separately in males and females . Association of the transformed trait with each variant passing quality control was tested in a linear regression framework under an additive model in the dosage of the minor allele after adjustment for age , age2 and study-specific covariates , separately in males and females . WHR was calculated as the ratio of waist circumference ( m ) to hip circumference ( m ) . Residuals were obtained after adjustment for age , age2 , BMI , and study-specific covariates , separately in males and females , and were subsequently inverse-rank normalised . Association of the transformed trait with each variant passing quality control was tested in a linear regression framework under an additive model in the dosage of the minor allele , separately in males and females . FG was measured in mmol/L . Individuals with a diagnosis of diabetes ( type 1 or type 2 ) , diabetes treatment , and/or FG≥7mmol/L , non-fasting state , or pregnancy were excluded . Individuals from case cohorts ( with diseases such as stroke and cardiovascular disease ) were also excluded if they had undergone hospitalization or blood transfusion in the 2–3 months before measurements were taken . Association of the untransformed trait with each variant passing quality control was tested in a linear regression framework under an additive model in the dosage of the minor allele after adjustment for age , age2 and study-specific covariates , separately in males and females . FI was measured in pmol/L with subsequent natural log transformation . Individuals with a diagnosis of diabetes ( type 1 or type 2 ) , diabetes treatment , and/or FG≥7mmol/L , non-fasting state , or pregnancy were excluded . Individuals from case cohorts ( with diseases such as stroke and cardiovascular disease ) were also excluded if they had undergone hospitalization or blood transfusion in the 2–3 months before measurements were taken . Association of the transformed trait with each variant passing quality control was tested in a linear regression framework under an additive model in the dosage of the minor allele after adjustment for age , age2 , BMI and study-specific covariates , separately in males and females . Summary statistics from association testing of variants passing quality control , separately in males and females , were corrected in each study for residual population structure through genomic control [56] where necessary ( S2 and S3 Tables ) . Subsequently , association summary statistics were combined across studies in sex-specific and sex-combined fixed-effects meta-analyses ( inverse-variance weighting ) for each trait , as implemented in GWAMA [57] . Heterogeneity in allelic effects between males and females for each trait at each variant was assessed by means of an implementation of Cochran’s Q-statistic [58] in GWAMA [57] . Variants passing quality control in fewer than 50% of the contributing studies for each trait were excluded from the meta-analysis . After filtering , the total numbers of variants reported for each trait were: 9 , 953 , 165 for BMI; 9 , 954 , 794 for WHRadjBMI; 9 , 967 , 162 for FG; and 9 , 837 , 044 for FIadjBMI . Sex-specific or sex-combined p<5x10-8 was considered genome-wide significant for each trait . Associated loci are referred to by the name ( s ) of the nearest gene ( s ) to lead SNP , unless there are more biologically plausible candidates mapping nearby . We performed approximate conditioning in established and novel glycaemic and obesity-related trait loci in GCTA [44] on the basis of association summary statistics from the sex-combined meta-analyses after variant filtering . We utilised genotype data from two reference studies to approximate LD between variants in diverse European populations , and hence correlation between parameter estimates in the GCTA-COJO joint regression model: 58BC-WTCCC ( 2 , 802 individuals from Great Britain ) ; and NFBC1966 ( 5 , 276 individuals from Lapland and the Province of Oulu in Northern Finland ) . We identified “index” variants to represent each distinct association signal achieving genome-wide significance ( p<5x10-8 ) in the GCTA-COJO joint regression model for further validation . We performed exact conditional analysis for each locus identified with multiple distinct association signals in GCTA using imputed data from all contributing studies except Rotterdam Study 1 ( 5 , 745 individuals ) . Within each study , we tested for association in the same linear regression framework utilised for unconditional analysis , separately in males and females , but included genotypes at each GCTA index SNP identified at the locus , in turn , as an additional covariate in the model . At each established glycaemic and obesity-related trait locus , we also performed conditioning on the previously reported lead SNP if it differed from that reported in our unconditional meta-analysis . Subsequently , association summary statistics for each signal were combined across studies in sex-specific and sex-combined fixed-effects meta-analyses ( inverse-variance weighting ) for each trait , as implemented in GWAMA [57] . We estimated the variance explained for each trait using genotype data from NFBC1966 ( 5 , 276 individuals ) in a multiple linear regression framework . For each trait , we considered two sets of variants: ( i ) previously reported lead SNPs for established loci; and ( ii ) new lead SNPs and index variants for multiple distinct association signals in established and novel loci . We tested for association of the trait: ( i ) with covariates only; and ( ii ) with covariates and the dosage of the minor allele at each variant . For each set of variants , the trait variance explained was given by the difference in the coefficient of determination ( r2 ) between these two regression models . For each distinct signal for each trait , we calculated the posterior probability of driving the association for the jth variant , πCj , given by πCj=Λj∑kΛk , where the summation is over all variants reported in the ( conditional ) meta-analysis across the locus . In this expression , Λj is the approximate Bayes’ factor [59] for the jth variant , given by Λj=[VjVj+ω]exp[ωβj22Vj ( Vj+ω ) ] , where βj and Vj denote the allelic effect and corresponding variance from the ( conditional ) meta-analysis for the association signal . The parameter ω denotes the prior variance in allelic effects , taken here to be 0 . 04 [59] . A 99% credible set was then constructed by: ( i ) ranking all variants in the locus according to their Bayes’ factor , Λj; and ( ii ) including ranked variants until their cumulative posterior probability exceeds 0 . 99 . We interrogated coding variants in the 99% credible set for each association signal using Ensembl and HaploReg [60] . Their likely functional consequences were predicted by SIFT [61] , PROVEAN [62] and PolyPhen2 [63] . We collected genomic annotation data from several sources . For regulatory state information , we collected sequence reads generated for six assays ( H3K4me1 , H3K4me3 , H3K27ac , H3K27me3 , H3K36me3 , and CTCF ) from 9 ENCODE cell types ( GM12878 , K562 , HepG2 , HSMM , HUVEC , NHEK , NHLF , hESC , HMEC ) [64] , pancreatic islets [65] , and adipose stem cells ( hASC t1 , t4 ) [66] . Reads were mapped to the human genome reference sequence ( hg19 ) using BWA [67] . Regulatory states for all cell types were called from the aligned reads using ChromHMM [68] , assuming 10 states . We then assigned names to the resulting state definitions as follows: active promoter ( High H3K4me3 , H3K27ac ) ; strong enhancer 1 ( H3K4me3 , H3K27ac , H3K4me1 ) ; strong enhancer 2 ( H3K27ac , H3K4me1 ) ; weak enhancer ( H3K4me1 ) ; poised promoter ( H3K27me3 , H3K4me3 , H3K4me1 ) ; repressed ( H3K27me3 ) ; low/no signal; insulator ( CTCF ) ; low/no signal; and transcription ( H3K36me3 ) . We also obtained transcription factor binding sites ( TFBS ) established using chromatin immunoprecipitation sequencing . This consisted of data on 147 proteins [64–66] . Finally , we used transcript information from GENCODEv14 [69] to define protein-coding genes , 5’ and 3’ UTR regions , and non-coding genes . For transcripts to be classified as protein-coding , the ‘protein-coding’ tag needed to be set and further filtering for either presence in the conserved coding DNA sequence ( CCDS ) database or experimentally confirmed mRNA start and end was applied . From this set of transcripts , 5’ UTR , exon , and 3’ UTR regions were defined . For non-coding genes , transcripts labelled as ‘lncRNA‘ , ‘miRNA’ , ‘snoRNA’ or ‘snRNA’ were used as non-coding genes . Overlap between the annotations described above and variants in tractable credible sets was determined using bedtools v2 . 17 . 0 . We defined seven broad functional classes from these annotation data: coding ( protein-coding transcripts ) ; ncRNA ( non-coding RNA transcripts ) ; UTR ( 3’ and 5’ UTR regions of coding transcripts ) ; enhancers ( strong and weak enhancer elements ) ; promoters ( active and poised promoter elements ) ; insulators; and TFBS ( sites pooled across all factors ) . We further used each of the cell line annotations as a distinct category . Each variant was allowed to overlap multiple annotation categories . For each broad functional class , Fisher’s exact test as implemented in R v3 . 0 . 1 ( with alternative = “greater” ) was used to compare whether the set of credible variants showed a higher fold overlap of this annotation versus all of the others independently . The six resulting p-values for each class were then combined using Fisher’s method . With 21 different functional class and trait combinations , a Bonferroni adjusted significance threshold ( p<2 . 4x10-3 ) was used .
|
Human genetic studies have demonstrated that quantitative human anthropometric and metabolic traits , including body mass index , waist-hip ratio , and plasma concentrations of glucose and insulin , are highly heritable , and are established risk factors for type 2 diabetes and cardiovascular diseases . Although many regions of the genome have been associated with these traits , the specific genes responsible have not yet been identified . By making use of advanced statistical “imputation” techniques applied to more than 87 , 000 individuals of European ancestry , and publicly available “reference panels” of more than 37 million genetic variants , we have been able to identify novel regions of the genome associated with these glycaemic and obesity-related traits and localise genes within these regions that are most likely to be causal . This improved understanding of the biological mechanisms underlying glycaemic and obesity-related traits is extremely important because it may advance drug development for downstream disease endpoints , ultimately leading to public health benefits .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
|
Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation
|
The adaptive landscape analogy has found practical use in recent years , as many have explored how their understanding can inform therapeutic strategies that subvert the evolution of drug resistance . A major barrier to applications of these concepts is a lack of detail concerning how the environment affects adaptive landscape topography , and consequently , the outcome of drug treatment . Here we combine empirical data , evolutionary theory , and computer simulations towards dissecting adaptive landscape by environment interactions for the evolution of drug resistance in two dimensions—drug concentration and drug type . We do so by studying the resistance mediated by Plasmodium falciparum dihydrofolate reductase ( DHFR ) to two related inhibitors—pyrimethamine and cycloguanil—across a breadth of drug concentrations . We first examine whether the adaptive landscapes for the two drugs are consistent with common definitions of cross-resistance . We then reconstruct all accessible pathways across the landscape , observing how their structure changes with drug environment . We offer a mechanism for non-linearity in the topography of accessible pathways by calculating of the interaction between mutation effects and drug environment , which reveals rampant patterns of epistasis . We then simulate evolution in several different drug environments to observe how these individual mutation effects ( and patterns of epistasis ) influence paths taken at evolutionary “forks in the road” that dictate adaptive dynamics in silico . In doing so , we reveal how classic metrics like the IC50 and minimal inhibitory concentration ( MIC ) are dubious proxies for understanding how evolution will occur across drug environments . We also consider how the findings reveal ambiguities in the cross-resistance concept , as subtle differences in adaptive landscape topography between otherwise equivalent drugs can drive drastically different evolutionary outcomes . Summarizing , we discuss the results with regards to their basic contribution to the study of empirical adaptive landscapes , and in terms of how they inform new models for the evolution of drug resistance .
Evolutionary biology has focused the lens through which we study drug resistance in microbes , helping to create a language to describe the evolutionary relationship between pathogens and therapeutic agents . Simultaneously , drug resistance has become a model problem to explore central concepts in evolutionary theory , including epistasis [1–4] , robustness [5] and extinction [6] . In recent years , the adaptive landscape analogy has been applied in various infectious disease contexts [1 , 7–10] , often using combinatorial approaches to identify probable trajectories for the evolution of drug resistance [3 , 11–16] . Most studies of this kind use the drug concentration that cuts the replication rate in half ( IC50 ) , the minimum inhibitory concentration ( MIC ) or related resistance metrics to predict the pathways through which resistance evolves under the assumption that the most resistant variants are preferred in the process of evolution towards maximal drug resistance . This assumption is based on an incomplete appreciation of the growth rate vs . drug concentration curves that generate the IC50 and MIC values . Specifically , the IC50 and MIC data each intrinsically restricts , in a different way , the environmental dimension over which adaptive landscapes vary , but few studies have examined this area either theoretically [17–19] or empirically [8 , 15 , 20] . Further interrogation of the environmental dimension of adaptive landscapes for drug resistance may be useful in the ongoing quest to develop rational strategies to prevent the rise and spread of drug resistance [21–27] . Such inquiry might also be relevant to addressing existing questions regarding how to most effectively treat a malaria infection [25 , 28–30] , and how widespread resistance arises in the first place [31] . As answers to these questions remain elusive , the evolutionary problem of drug resistance can benefit from new models and perspectives . In this study , we use empirical data and simulations to study the interaction between adaptive landscapes and two environmental dimensions: drug type and concentration . We do so in Plasmodium falciparum , the causative agent of the most deadly form of malaria . First , we compare the growth curves for all 16 combinatorial mutants across drug types and concentrations , and ask whether the landscapes for the two drugs display cross-resistance as commonly understood . We then reconstruct all accessible adaptive trajectories for the evolution of drug resistance across drug environments , and observe how their topography changes as a function of environment . Next we offer mechanistic insight into why this topography changes through quantifying the fitness effect of individual mutations , and patterns of epistasis , across drug environments . Finally , we simulate evolution to observe how subtle differences in the topography of these otherwise cross-resistant landscapes create surprisingly different dynamics . We discuss the results in terms of their implications for the general study of empirical adaptive landscapes , in the context of more detailed models for the evolution of drug resistance , and with regards to how they refine our understanding of the cross-resistance concept .
The study utilized data from a well-characterized system: transgenic Sacharomyces cerevesiae carrying a combinatorially complete set of resistance mutations for P . falciparum Dihyrofolate Reductase ( DFHR ) [11 , 12] . By “combinatorially complete , ” we mean all combinations of mutations at the following sites corresponding to mutations identified in field isolates of Plasmodium falciparum in various settings [32–43]: N51I , C59R , S108N , I164L . We use bit string notation to represent the 16 alleles being studied , with 0000 corresponding to the wild type ancestor , and 1 to a mutation at each site ( the 1111 allele the quadruple mutant ) . We also use asterisk notation to denote classes of alleles containing individual sites: 1*** ( N51I ) , *1** ( C59R ) , **1* ( S108N ) , ***1 ( I164L ) . Fig 1 shows the entire set of mutants for the landscape connected in all combinations between the ancestor ( 0000 ) and the quadruple mutant ( 1111 ) . The empirical data—growth measurements without drug and IC50 values for all 16 alleles in pyrimethamine ( PYR ) and cycloguanil ( CYC ) —were measured in prior studies [11 , 12] and used to develop the more detailed model of evolution presented in this study . Following the literature , we assumed that growth rate is related to drug concentration via a logistic function with two empirically-derived parameters: the drugless growth rate and the IC50 ( see S1 Table ) , both measured in previous studies using a transgenic yeast system [11 , 12] . Logistic curves were generated from an equation: g ( x ) =gdrugless1+eIC50-xc ( 1 ) Where gdrugless is the growth rate with no drug present , the IC50 the concentration of drug that inhibits growth rate by 50% , and c a fitted constant that defines the shape of the logistic curve . The final growth rates were computed by standardizing the original drugless growth rates ( in optical density ) relative to the slowest-growing viable allele , 1011 , which was given a gdrugless value of 1 . 0 ( the 0011 allele has undetectable growth in this system , and is given a growth rate of 0 ) . We determined growth rates across a window of pyrimethamine and cycloguanil concentrations between 0 and 100 , 000 uM , a range that includes blood levels of pyrimethamine measured within infected humans [44–48] . Drug concentrations are log transformed , and are represented in this study as Log [Drug] in micromolar ( uM ) concentration ( S2 Table ) . Error estimates for growth rates at each drug concentration are unavailable because the estimated growth rates are modeled from Eq 1 , using the empirically measured gdrugless and the IC50 . We do , however , have standard errors for both of these parameters , which are quite small . These estimates have been published previously[11 , 12] , and are reproduced in S1 Table . For the drugless growth rate , the standard errors range between 1% and 8% . For IC50 values , the standard errors are even lower , ranging from 0 . 2% to 3% in pyrimethamine and 0 . 4% and 4% in cycloguanil . We will further examine the role of experimental noise in the section dedicated to the fitness effects of individual mutations below . Pleiotropy is broadly defined by a single genotype’s ( an allele or mutation ) effect on two or more phenotypes . Because we are examining landscapes in two different drugs , we sought to test pleiotropy more directly by determining whether the structures of the adaptive landscapes for the two drugs were consistent with “cross-resistance” as commonly understood . We assessed this in terms of ( 1 ) the correlation between the IC50 values for the alleles in landscapes of pyrimethamine and cycloguanil , and ( 2 ) the correlation between the growth rates of the alleles in pyrimethamine and cycloguanil across a range of drug concentrations . To measure the effects of environment on distinct mutations , we utilized a novel method used to compute the change in fitness when each of a set of single mutations ( the four sites examined in this study ) was added to all possible genetic backgrounds [49] . Our version of this calculation is analogous to measures of gene by gene by environment interactions [50] , or how much the effect of a mutation depends on genetic background and environment [20 , 51] . The number of mutational backgrounds per mutation can be calculated as follows: number of variants per site ( number of total sites—1 ) = 23 = 8 possible genetic backgrounds . The effect of a mutation can be computed by taking the difference between the fitness ( W ) of an allele j and the one-step neighbor carrying mutant jε ( N51I , C59R , S108N , I164L ) : ΔWε=Wjε−Wj ( 2 ) We computed the absolute effects of individual mutations on growth rate at each of the four sites across the measured drug concentrations . In addition , we calculated a scaled effect of each mutation by dividing the absolute effect on growth rate by the growth rate of the wild type ancestor ( 0000 ) at a given drug concentration . This relative contribution is important to highlight because we would like to identify those environments where the absolute effects of a mutation are large , but which have little effect on dictating evolutionary dynamics because all mutants have high fitness . Alternatively , we would also like to identify those environments where the absolute effect of a given mutation might be small but very consequential in evolutionary dynamics because they are large relative to the ancestor ( e . g . , 0000 ) . While previous studies of adaptive landscapes have identified probable pathways in evolution [3 , 11 , 13 , 14] , none include information on dynamic properties of this evolution , how alleles rise and fall through frequency space . This is a notable omission , as only through studying dynamics can we observe how the rate of fixation and other dynamic properties depend on the environment . To test how the aforementioned changes in adaptive landscape structure affect the dynamics of evolution , we modeled a discrete ( non-overlapping ) generation , individual-based scheme using SimuPop , a forward-time simulation package [52] . Each run began with a population fixed for the 0000 ancestor , with a population size of 104 , where mutation and reproduction were probabilistic , rather than deterministic . The mutation rate was based on a normalized mutation matrix for P . falciparum as in Lozovsky et al . , and adjusted for a per-site , per generation mutation rate . Finally , these mutation rates were scaled by a factor m ( set to 103 ) , which allowed us to run much shorter simulations with a more manageable numbers of individuals , while not changing the qualitative results of simulations with much larger population sizes , as in Jiang et al ( 2013 ) . We do this by dividing the effective population size by the scaling factor , m , and then multiplying all rates by that same factor: Ne⋅μ=Nem ( μ⋅m ) ( 3 ) Where Ne is the effective population size , μ the mutation rate , and m the scaling factor . Unlike Jiang et al . ( 2013 ) , we modeled a starting population size that was identical in all simulation runs , at several drug concentrations: no drug , 1 . 0 uM , 100 uM and 10 , 000 uM . This static drug , long-term forward evolution scenario is analogous to a pathogen being treated consistently with a certain concentration of drug over a long duration . We ran 100 replicate simulations for each scenario , amounting to approximately 700 simulations: 1 no drug simulation , 3 pyrimethamine concentrations , 3 cycloguanil concentrations . Rather than simply reporting the “winning” allele or most frequently traversed trajectory , we were interested in the dynamics of evolution , and included illustrative examples of evolutionary simulations . In addition , we compared mean fixation times for alleles in the most preferred pathways across simulated environments .
Fig 1 illustrates the general structure of the landscape and individual growth rates for its 16 alleles as a function of pyrimethamine and cycloguanil concentration . Note that the growth rate of the most resistant allele in pyrimethamine as judged by IC50 ( 1111 ) is the highest only at extreme drug concentrations , meaning that it is not uniformly favored despite its superior resistance ( as measured by IC50; S1 Table ) . For cylcoguanil , the rank order of fitness values is different than for pyrimethamine , with the 0111 triple mutant having the highest growth rate across most drug concentrations . More broadly , we can observe how the rank order of growth rates varies across the range of drug concentrations and changes the topography of adaptive landscapes . We list the rank orders of alleles in Table 1 . Having demonstrated that the structure of adaptive landscape topography changes as a function of drug environment , we can address whether the 16 alleles that compose the adaptive landscapes for the two drugs demonstrate cross-resistance , that is , whether resistance phenotypes for one drug confer corresponding resistance to the other . To do this , we compared the landscapes with regards to their IC50 ( a standard measure of resistance ) and growth rates across all drug environments . Regression analysis of the IC50 values across the two drugs , observable in Fig 2A , yielded a significant correlation for IC50 ( Pearson R2 = 0 . 74 , P = 3 . 9 X 10−5 ) . In addition , the landscape showed significant or nearly significant correlations between the landscapes across drug concentrations ( Fig 2B and S1 Fig ) , indicating that the landscapes share an essential structure . This is unsurprising , as pyrimethamine and cycloguanil are related compounds with a similar molecular structure and mechanism of action [53–55] . With that said , the decreasing correlation between landscapes with increasing drug concentration ( Fig 2B and S1 Fig ) suggests that cross-resistance is a quantity that may depend on the environment . Also note that this decrease in correlation could be an artifact of the overall decline in the growth rates of alleles as drug concentrations increase . Because more alleles have a growth rate close to 0 at high drug concentrations , the resolution in rates between alleles ( necessary for a strong correlation ) also declines . We mention this to highlight that the significant ( or nearly significant ) but declining correlations observed at higher concentrations might actually be stronger than the analysis and graphs communicate . Using fitness values for P . falciparum ( computed using Eq 1 ) , we reconstructed a 3-dimensional representation of all accessible trajectories between the ancestor ( 0000 ) and quadruple mutant ( 1111 ) across drug environments ( Fig 3 ) . We define a pathway as accessible if growth rate increases with Hamming distance , as in several related studies of adaptive landscapes [3 , 11 , 14] . Note that many such pathways will reach a fitness peak prior to the quadruple mutant , at a double or triple mutant state . In this figure , we observe how the environment has gross effects on evolution , affecting both the number of accessible pathways , and their topography . In particular , note subtle differences between the pyrimethamine and cycloguanil environments: There are fewer accessible pathways in cycloguanil than pyrimethamine in the three drug concentrations observed in Fig 3A vs . 3D; Fig 3B vs . 3E; Fig 3C vs . 3F . With regards to epistasis: In the null expectation ( i . e . , trajectories without epistasis ) , the fitness would increase monotonically along each mutational pathway . That the fitness increases non-monotonically along some pathways is suggestive of magnitude epistasis , where fitness effects are not the same on all backgrounds . Sign epistasis underlies many non-accessible pathways ( not observed in Fig 3 ) where there are fitness decreases with increased Hamming distance , constraining certain trajectories and making others more accessible [6 , 15] . We explore this in detail later in our study . To explain the ruggedness in adaptive landscape topography ( Fig 3 ) and the observed patterns of cross-resistance ( Fig 2 ) , we calculated the average effect of individual mutations on reproductive fitness , across drug environments . To do so , we compared the effect of each mutation at the four candidate sites—N51I ( 1*** ) , C59R ( *1** ) , S108N ( **1* ) , and I164L ( **1* ) —in both pyrimethamine and cycloguanil , across concentrations . We carried out this comparison for both the absolute growth rate effect and a rescaled measure that calculates the effect relative to the average growth rate in a given environment ( see: Methods ) . The former informs us of how an environment dictates the total fitness effects of mutations . The latter ( scaled effects ) illuminates the picture relative to the growth rate of the wild type ancestor ( 0000 ) . This is a critical distinction , because some mutations of low absolute effect might be very consequential for evolution in certain environments . At high drug concentrations ( PYR and CYC ) , for example , all alleles have a low absolute growth rate . Nevertheless , alleles experience intense competition at these high drug concentrations because of meaningful differences in their relative growth rates . The shape of the curves in Fig 4 indicates an interaction between fitness effect and environment . The results of the formal analysis ( ANOVA ) of how the drug environment influences mutation effects is outlined in S4 Table and can be broadly summarized as follows: In pyrimethamine , the third site mutation ( **1* ) is most affected by environment ( absolute: F = 4 . 57 , df = 9 , 70 , P = 0 . 00009; scaled: F = 8 . 10 , df = 9 , 70 , P = 4 . 0 × 10−8 ) . This is especially true at high drug concentrations , as the third site mutation is present in the most resistant single mutant ( 0010 ) , double mutant ( 0110 ) , triple mutants ( 1110 and 0110 ) and the maximally resistant quadruple mutant ( 1111 ) ( Fig 1B and Table 1 ) . In cycloguanil , the situation is different , and the mutation effect findings are dominated by the 0111 allele that is substantially more resistant ( as measured by IC50 , S1 Table ) than any other in its adaptive landscape . For those reasons , all mutations that can generate 0111 have positive absolute effects for much of the breadth of drug concentrations , and especially the higher drug concentrations . The third site , **1* , has a statistically significant interaction with drug concentration ( absolute effect: P = 0 . 00081 ) with the fourth site , ***1 having a nearly significant interaction across environments ( absolute effect: P = 0 . 05 ) . These findings relate to the role each site plays in creating not only the 0111 mutant , but also the most resistant double mutant ( 0110 ) , and other mildly resistant triple mutants ( 1011 ) . To observe how adaptive landscape by environment interactions affect evolutionary dynamics , we used growth rates derived from empirical data to simulate the evolution of populations at various drug concentrations ( see: Materials and Methods for details ) . In this model , a population of 10 , 000 individuals fixed for the ancestor ( 0000 ) were exposed to single , stable concentrations of pyrimethamine or cycloguanil in several drug environments: no drug ( Fig 5A ) , low ( 1 . 0 uM ) ( Fig 5B and 5E ) , intermediate ( 100 uM ) ( Fig 5C and 5F ) and high ( 10 , 000 uM ) ( Fig 5D and 5G ) . The graphs represent illustrative examples of the most preferred pathways and typical evolutionary dynamics in each environment . We will discuss the main results of simulations in each drug environment , and provide a summary of all simulations in S6 Table ( which also includes the results of statistical tests comparing the mean fixation times along preferred pathways ) .
Upon constructing a visual representation for discrete evolutionary pathways in P . falciparum , we observed that the overall topography of the landscape is strongly dependent on environment ( Fig 3 ) . The results of simulations highlight that dynamic properties of evolution , such as the identity of the most preferred pathway and fixation time of the highest fitness allele , are both dependent on the environment ( Fig 5 and S6 Table ) . Given the degree to which environmental circumstances affect evolution in our simulations , coupled with knowledge that in vivo drug concentrations vary within patients over time [44–48] , we suggest that future characterizations of adaptive landscapes should be considered with respect to the full breadth of environments in which organisms exist . This suggestion is consistent with the more modern "fitness seascape" analogy [63] , befitting a more dynamic model of evolution .
|
The adaptive landscape analogy describes the process of evolution by examining how individual mutations in a gene or genome affect the reproductive success of an organism . In certain cases , it can offer insight into what pathways evolution is likely to take in moving between different phenotypes . The analogy has been used by evolutionary biologists to describe a number of phenomena ranging from how mutations affect hemoglobin function to how bacteria evolve resistance to antibiotics . In this study , we combine computational biology with experimental data to examine how the environment—defined as the type of drugs and their amounts—affects the structure of adaptive landscapes for drug resistance in Plasmodium falciparum ( the agent responsible for the most deadly form of malaria ) with respect to mutations in dihydrofolate reductase ( DHFR ) , an enzyme that plays an important role in drug resistance . We conclude that the environment has a profound effect on how the evolution of drug resistance occurs . In the future , these details should be integrated into models of antimicrobial therapy , as they greatly influence the dynamics of drug resistance evolution .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"organismal",
"evolution",
"medicine",
"and",
"health",
"sciences",
"pyrimethamine",
"drugs",
"microbiology",
"antimalarials",
"epistasis",
"microbial",
"evolution",
"pharmacology",
"evolutionary",
"adaptation",
"fitness",
"epistasis",
"evolutionary",
"genetics",
"heredity",
"genetics",
"biology",
"and",
"life",
"sciences",
"evolutionary",
"biology",
"drug",
"interactions",
"evolutionary",
"processes"
] |
2016
|
Adaptive Landscape by Environment Interactions Dictate Evolutionary Dynamics in Models of Drug Resistance
|
Vaccinia virus envelope protein A27 has multiple functions and is conserved in the Orthopoxvirus genus of the poxvirus family . A27 protein binds to cell surface heparan sulfate , provides an anchor for A26 protein packaging into mature virions , and is essential for egress of mature virus ( MV ) from infected cells . Here , we crystallized and determined the structure of a truncated form of A27 containing amino acids 21–84 , C71/72A ( tA27 ) at 2 . 2 Å resolution . tA27 protein uses the N-terminal region interface ( NTR ) to form an unexpected trimeric assembly as the basic unit , which contains two parallel α-helices and one unusual antiparallel α-helix; in a serpentine way , two trimers stack with each other to form a hexamer using the C-terminal region interface ( CTR ) . Recombinant tA27 protein forms oligomers in a concentration-dependent manner in vitro in gel filtration . Analytical ultracentrifugation and multi-angle light scattering revealed that tA27 dimerized in solution and that Leu47 , Leu51 , and Leu54 at the NTR and Ile68 , Asn75 , and Leu82 at the CTR are responsible for tA27 self-assembly in vitro . Finally , we constructed recombinant vaccinia viruses expressing full length mutant A27 protein defective in either NTR , CTR , or both interactions; the results demonstrated that wild type A27 dimer/trimer formation was impaired in NTR and CTR mutant viruses , resulting in small plaques that are defective in MV egress . Furthermore , the ability of A27 protein to form disulfide-linked protein complexes with A26 protein was partially or completely interrupted by NTR and CTR mutations , resulting in mature virion progeny with increased plasma membrane fusion activity upon cell entry . Together , these results demonstrate that A27 protein trimer structure is critical for MV egress and membrane fusion modulation . Because A27 is a neutralizing target , structural information will aid the development of inhibitors to block A27 self-assembly or complex formation against vaccinia virus infection .
Vaccinia virus , the prototypic member of the Orthopoxvirus genus of the family Poxviridae , contains a double-stranded DNA genome of approximately 190 kb that encodes more than 200 individual proteins [1] . It replicates and produces mature virus ( MV ) in the cytoplasm of host cells [2] . The vaccinia MV particle contains ∼20 envelope proteins , at least 16 of which participate in MV entry into cells [3] , [4] . Three proteins , H3 , D8 , and A27 , mediate MV attachment to the cell surface glycosaminoglycans ( GAGs ) ; one A26 protein binds to the extracellular matrix protein laminin [5] , [6] , [7] , [8] . A27 protein was also implicated as a viral fusion protein because a monoclonal antibody recognizing A27 protein neutralized virus entry and interfered with MV-induced membrane fusion [9] , [10] , [11] . It was proposed that the N-terminal sequences of A27 protein contain hydrophobic residues common to viral fusion peptides and that A27 protein forms parallel trimeric coiled coils common to type 1 fusion proteins [12] , [13] . Furthermore , Kochan et al . demonstrated that co-expression of vaccinia A17 and A27 proteins in mammalian and insect cells triggered cell-cell fusion [14] , suggesting that A27 protein acts directly in membrane fusion execution . However , 12 additional MV proteins ( A16 , A21 , A28 , G3 , G9 , H2 , I2 , J5 , L1 , L5 , F9 , and O3 ) were shown to form a viral entry fusion complex ( EFC ) to mediate membrane fusion , although the fusion mechanism remains unknown [15] . Given the complexity of virion structure , vaccinia virus membrane fusion has been a somewhat contentious issue , and how A27 protein is involved in membrane fusion has been a matter of some debate . With such a large number of envelope proteins , it is not surprising that vaccinia virus has a wide range of infectivity . Depending on cell types and virus strains , MV particles enter cells through either endocytosis or plasma membrane fusion [16] , [17] , [18] . Endocytosis of the vaccinia virus WR strain into HeLa cells requires the viral A25–A26 protein complex and two cell surface receptors: integrin β1 [19] and CD98 [20] . The A26 open reading frame ( ORF ) was deleted from the WR virus genome , and the resulting WRΔA26L mutant virus enters cells through plasma membrane fusion [17] , [18] . The current model states that viral A26 protein on MV acts as an acid-sensitive membrane fusion suppressor that binds to the A16 and G9 subcomponents of the EFC to restrain fusion activity at neutral pH [21] . After endocytic uptake of MV into vesicles , the acidic endocytic environment induces the dissociation of A26 protein from MV , leading to viral membrane fusion with vesicular membranes . On the other hand , vaccinia MV lacking A25–A26 suppressor proteins bypass the need for low pH and readily fuse with plasma membrane [17] . While A25 and A26 proteins are important determinants in the vaccinia virus entry process , they are not integral membrane proteins . Therefore , the assembly of A25 and A26 proteins into MV requires A27 , which forms disulfide bonds with A26 [22] , [23] . Although A27 protein also lacks a transmembrane region , it does interact with the integral membrane protein A17 [24] , providing a bridging function to anchor A25 and A26 proteins onto MV particles . Aside from the role in virus entry described above , A27 protein also facilitates enveloped virus release during the late phase of the viral life cycle . A proportion of MV progeny in infected cells are transported out of viral factories via microtubules through a A27-dependent mechanism to the trans Golgi network , where additional membranes are wrapped and extracellular enveloped virus ( EV ) is released via exocytosis [25] . Deletion of the A27L gene from the vaccinia virus genome did not affect MV production; however , defects in MV transport [25] and inability to wrap additional membranes [26] were reported . Thus , inactivation of A27 protein functions resulted in the attenuation of EV formation and a small plaque phenotype [27] , [28] . Although vaccinia A27 is multi-functional in the vaccinia virus life cycle , its structure is unavailable . We determined the crystal structure of tA27 protein , which reveals unexpected and novel coiled-coil architecture . The availability of the tA27 protein structure allows us to further investigate the oligomeric states of A27 protein and elucidate how it interacts with A26 to regulate membrane fusion during vaccinia virus infection .
Vaccinia A27 protein is composed of 110 amino acid residues containing a heparin binding domain ( HBD ) , a coiled-coil domain ( CCD ) and the A17 binding leucine zipper domain ( LZD ) ( Figure 1A ) . The HBD , amino acids ( aa ) 21–34 , includes the core sequence KKPE ( aa 26–29 ) , which is structurally flexible and essential for binding to cell surface heparan sulfate ( HS ) [5] , [29] , [30] . The CCD contains aa 43–84 and is required for self-oligomerization in vitro [29] , [31] . The CCD domain contains cysteine 71 and 72 , previously found to form disulfide bonds during A27 self-assembly and with A26 protein [23] . The LZD domain ( aa 85–110 ) is the A17 binding region and was predicted to be a leucine zipper [13] , [24] . We also expressed a truncated A27 protein ( aa 21–84 , C71/72A ) with a T7 tag at the N-terminus and a hexahistidine tag at the C-terminus , named as T7-tA27 , in bacteria with cysteine-to-alanine mutations of both amino acids 71 and 72 to prevent protein insolubility caused by nonspecific disulfide bond formation [31] . C71/72A double mutations had no effect on T7-tA27 protein oligomerization in vitro compared with wild type protein , suggesting that disulfide bonding formed between C71 and C72 in the virus-infected cells serves to stabilize A27 oligomer interactions mediated through hydrophobic interactions in the coiled-coil region [13] . The tagged recombinant T7-tA27 protein was soluble and biologically active . It forms hexamers in solution according to a size exclusion chromatography ( SEC ) study [29] , [31] , binds to cell surface HS [30] , [32] , and competes with vaccinia MV for cell attachment [30] , [31] , suggesting that the T7-tA27 protein mimics the native A27 protein structure on vaccinia MV . Hence , we further purified the tag-free A27 protein ( aa 21–84 , C71/72A , named as tA27 , Figure S1 ) for X-ray crystallographic study . The tA27 protein was crystallized in the P43212 space group , and a native dataset was collected to 2 . 2 Å ( Table S1 ) . For phasing determination of the tA27 protein , we also generated a series of tA27 mutants by introducing a methionine residue into the tA27 protein and subsequently producing selenomethionine-labeled crystals of tA27 protein; however , all attempts to produce crystals were unsuccessful . Finally , the initial phases of tA27 protein were solved using multiple-wavelength anomalous diffraction analysis of tantalum bromide cluster derivative crystals ( Table 1; Figure S2 ) . The native structure of tA27 protein was refined to an Rwork of 21 . 0% and Rfree of 27 . 2% , with all residues located in the allowed regions of a Ramachandran plot ( Table 1 ) . The electron density map is shown in Figure S3 . Monomers composed of tA27 protein fold into single α-helix structures and assemble further into a hexameric structure using a trimer ( which corresponds to the crystallographic asymmetric unit ) as a building block ( Figure 1B ) . The trimeric building block of tA27 contains unusual triple-stranded coiled-coil interactions with two parallel α-helices ( chains A and B ) and one antiparallel α-helical strand ( chain C ) using the N-terminal region interface ( NTR Figure 1B ) . The antiparallel α-helix ( chain C ) uses its C-terminal region interface ( CTR ) to form a bridge-like structure that connects two trimers into a hexameric assembly . This unique type of parallel-to-antiparallel interaction within each trimer is distinct from the classical parallel trimeric coiled-coil structure common to other viral fusion proteins , such as influenza virus HA2 ( Figure 1C; Table S1 ) , although the individual coiled-coil of tA27 and the heptad repeat of viral fusion proteins are structurally similar . Each subunit of tA27 has conformational differences , with average root mean square deviations of 2 . 71 Å ( chains A and B; 40 Cα atoms ) , 2 . 08 Å ( chains A and C; 40 Cα atoms ) , and 1 . 28 Å ( chains B and C; 40 Cα atoms ) ( Figure S4 ) . The hexameric assembly of tA27 is a structurally stable unit with the NTR and CTR providing significant driving force for protein oligomerization . This finding is supported by the Protein Interfaces , Surface , and Assemblies ( PISA ) program and our previous CD spectroscopy analyses ( Table S2 ) [29] , [33] . Previous reports also demonstrated that both full length and T7-tA27 form trimers/hexamers in vitro and that oligomerization of A27 protein is concentration-dependent [13] , [29] , [30] , [31] . The HBD of tA27 is a short peptide region of aa 21–34 [32] . Using site-directed mutagenesis and solution NMR , we previously identified four amino acids , KKPE , with a turn-like conformation that mediates specific heparin binding [30] . The HBD feature is distinct from the foot-and-mouth disease virus , which contains a shallow cavity for binding to HS on cells , and the adeno-associated virus , whose capsid proteins form a channel-like structure to interact with heparin [34] , [35] , [36] , [37] . Due to the intrinsic flexibility of HBD [29] , its electron density maps ( residues 21–44 for chains A and B; residues 21–43 for chain C ) were not visible ( Figure 1B; Figure S3 ) . Despite extensive efforts to co-crystallize tA27 proteins and soak native tA27 crystals with HS and heparin , we have not obtained the ligand-bound form of tA27 structures . It might be that the structure of the HBD is only induced upon binding to its ligand and that ligand association and dissociation are in a dynamic equilibrium . In the future , it will be worthwhile to investigate tA27 protein in complex with heparin or HS using small-angle X-ray scattering . The CCD of tA27 protein contains approximately 42 residues ( aa 43–84 ) . In the CCD region , six hydrophobic residues , including Leu47 , Leu51 , Leu54 , Ile58 , Val61 , and Phe65 , are packed in six layers at the NTR , along with Ile68 , Asn75 , and Leu82 at the CTR ( Figure 2 ) . These residues are located in close proximity with one another , and along with the hydrogen bonds of Asn75 they mediate hydrophobic interaction as the primary driving force for the molecular self-assembly of the tA27 protein ( Figure 2B; Table S2 . Specifically , Cross-Section 1 ( C-S 1 ) is composed of Ile68 ( chains A′ and B′ ) and Leu82 ( chain C ) C-S 2 contains Leu82 ( chains A′ and B′ ) and Ile68 ( chain C ) ; C-S 3 contains Leu47 ( chains A and B ) and Phe65 ( chain C ) ; C-S 4 includes Leu51 ( chains A and B ) and Val61 ( chain C ) ; C-S 5 contains Leu54 ( chains A and B ) and Ile58 ( chain C ) ; C-S 6 is composed of Ile58 ( chains A and B ) and Leu54 ( chain C ) ; C-S 7 comprises Val61 ( chains A and B ) and Leu51 ( chain C ) ; and C-S 8 consists of Phe65 ( chains A and B ) and Leu47 ( chain C ) . Such parallel-to-antiparallel multiple-strand arrangements of hydrophobic residues provide a strong structural basis to explain the previous in vitro study that demonstrated triple mutations ( TM ) of L47A , L51A , and L54A , which resulted in T7-tA27 disassembly [31] because all six hydrophobic interactions ( shown in Figure 2 ) are completely disrupted . These arrangements of hydrophobic interaction also solved a previous disagreement concerning a proposed model [13] that predicts three parallel coiled-coil structures of A27 trimer , in which three bulky face-to-face Phe65 residues would have disrupted A27 trimer assembly . In fact , it is critical to point out that the three coiled-coil arrangements in the tA27 crystal structure allow spatial stability of the A27 assembly . In addition , the Asn75 side chain OD1 of chain C forms two hydrogen bonds with the Asn75 NH2 of chains A′ and B′ . Most importantly , the tA27 crystal structures are in agreement with the biochemical properties of soluble T7-tA27 protein reported previously ( see below ) . Previous studies showed that both HBD and oligomerization of T7-tA27 protein are required for cell attachment [30] , [31] , [38] . The conclusion was based on experiments showing that a 14-mer peptide containing only the HBD exhibited minimal affinity to heparin/HS [30] and that in vitro leucine-to-alanine mutagenesis of L47 , L51 , and L54 ( triple mutations , TM ) of T7-tA27 protein generated a soluble T7-tA27-TM protein that failed to bind to heparin in vitro and to HS on cells [31] . The NMR spectra for T7-tA27 protein revealed slow molecular dynamics due to protein self-assembly [29] , and CD spectroscopy analysis showed that TM destabilized T7-tA27 protein into monomeric subunits at near-neutral pH ( ∼6 . 7 ) [31] . In addition to self-assembly through the CCD region , it is worth noting that A27 protein also associates with other viral proteins . The two cysteine 71/72 residues , although mutated to alanine in the tA27 crystal structure ( marked red in Figure 1 ) , mediate intermolecular disulfide bonds with Cys441 and Cys442 of the A26 protein [23]– . Two forms of A26A27 protein complexes ( 70-kDa and 90-kDa ) were detected on MV particles in non-reducing conditions [21] , [23] . The C-terminal LZD region of the A27 protein , although absent from the tA27 crystal structure , reportedly interacts with viral integral protein A17 to facilitate A27 anchoring onto MV particles [13] , [14] , [24] . Vaccinia A27 protein orthologs are widely conserved in Poxviridae ( Figure 3A ) . Alignment of poxviral A27 orthologs reveals that the CCD and LZD domains are more conserved than the HBD domain among parapoxvirus ( Group A ) , orthopoxvirus ( Group B ) , capripoxvirus , suipoxvirus ( Group C ) , and leporipoxvirus ( Group D ) . The different sizes of A27 ortholog proteins in each group mainly reflect deletions/insertions clustered at the N-terminal region of A27 orthologs , while the domain sizes of the CCD and LZD are relatively conserved ( Figure 3B ) . For the CCD , the hydrophobic residues contributing to the formation of the coiled-coil structure are particularly conserved in Groups B , C , and D ( Figure 3A ) . In addition , cysteine residues within the CCD of the vaccinia A27 protein are conserved in Groups A , B , and C . Threading the sequences of A27 orthologs onto the X-ray structure of vaccinia tA27 protein for structure modeling reveals that the CCD domains of A27 orthologs exhibit conserved structural folding ( Figure 3C ) , implying that these A27 orthologs evolve to form similar structural architecture in order to maintain important biological functions . A previous in vitro study demonstrated that TM of L47A , L51A , and L54A resulted in T7-tA27 disassembly [31] , which showed the important contribution of residues at the NTR to T7-tA27 protein assembly however , the importance of residues in the CTR was not investigated . Therefore , we generated a series of tA27 expression constructs to re-visit these issues . The mutation sites in tA27 protein ( Table S3 ) include tA27-WT containing C71/72A ( tA27-WT ) ; tA27-TM , L47A/L51A/L54A , at the N-terminal interface ( tA27-TM-N ) ; tA27-TM , I68A/N75A/L82A , at the C-terminal interface ( tA27-TM-C ) ; and tA27-hexa alanine mutations combining both L47A/L51A/L54A and I68A/N75A/L82A ( tA27-6A ) . Because the stretch of tA27 protein encompassing amino acids 21–84 does not contain any aromatic residues , an extra tryptophan was included at the N-terminus of each protein . All of the recombinant proteins were purified as tag-free tA27 proteins , as described in materials and methods . Each recombinant protein of 1 mg/ml and 9 . 5 mg/ml was individually injected into a Superdex 75 10/300 GL size exclusion column at pH 7 . 5 , the fractions were collected , and the molecular mass of each tA27 recombinant protein was determined using standard protein molecular mass markers . Additionally , tA27-WT and tA27-TM-N proteins were analyzed by gel filtration system equilibrated at pH 3 . 0 ( which has been reported to induce dissociation of tA27-WT protein into monomers by 2D NMR experiments [31] ) . They were subsequently analyzed using a gel filtration system equilibrated in pH 3 . 0 , and fractions were collected . Because protein markers were unsuitable as standards at low pH , the protein elution volume was used for comparison among different recombinant proteins . The SEC results are summarized in Figure 4 and Table S3 . At 1 mg/ml low concentration , all four proteins ( tA27-WT , tA27-TM-N , tA27-TM-C , and tA27-6A ) were eluted with a very close peak volume with respective apparent molecular masses of 24 . 4 , 19 . 8 , 21 . 9 , and 22 . 9 kDa , suggesting these proteins were trimers . At 9 . 5 mg/ml , tA27-WT was eluted earlier , suggesting concentration-dependent oligomerization , while the elution volumes of tA27-TM-N , tA27-TM-C , and tA27-6A remained largely unchanged . Strangely , when tA27-WT and tA27-TM-N at 1 mg/ml were eluted at pH 3 . 0 , at which both proteins should exist mostly as monomers , the elution volumes remained largely unchanged . The obvious discrepancy between the apparent molecular mass and elution volume of the monomers raised the possibility that tA27 protein , with an elongated asymmetric shape , is not suited for use in SEC for oligomer determination since most molecular standard proteins are globular in shape . Indeed , the length and diameter of the tA27 helix might disguise the protein , causing it to behave like a much larger protein in solution because its radius of gyration would be larger owing to its asymmetric shape . Next , we sought to use two independent biophysical methods , sedimentation velocity analytical ultracentrifugation ( AUC-SV ) and size exclusion chromatography with multi-angle light scattering ( SEC/MALS ) to elucidate the oligomeric state of recombinant tA27-WT in solution . Both methods can be used to determine the molecular weights of proteins without assuming that the protein of interest is compact and globular [39] . tA27-WT , tA27-TM-N , tA27-TM-C , and tA27-6A , each at 120 µM concentration , were examined in solution by AUC-SV as described in the materials and methods . The results of c ( s ) analysis for tA27-WT at pH 7 . 5 showed two different peaks ( 0 . 96S and 1 . 5S ) suggesting concentration-dependent oligomerization ( Figure 5A ) . The small peak at ∼0 . 96 S is assigned to the monomeric form , while the peak at 1 . 5S corresponds to a molecular mass of ∼16 kDa and is assigned to the dimeric form . tA27-WT at pH 3 . 0 and tA27 mutants TM-N , TM-C , and 6A at pH 7 . 5 all remained as a major peak at 0 . 88S , indicating monomeric structure . The small difference of the tA27-WT monomers sedimentation coefficient at neutral and acidic pH ( 0 . 96S vs . 0 . 88S ) may be attributed to other factors , such as an acidic environment affecting the hydrodynamic parameters or shape and integrity of the structure of tA27-WT . The SV result of tA27-WT at a low concentration ( 20 µM ) has only a broad peak in the range of 0 . 75S to 1 . 1S and lacks a significant peak around 1 . 5S ( data not shown ) , indicating that the proportions of monomer and oligomers changed depending on the total protein concentration , with the proportion of dimer apparently increasing as the concentration increased . We also employed SEC/MALS to determine the absolute molecular weights of tA27-WT moieties in solution based on the angular dependence of scattered light intensity , which is independent of the molecular shapes , thereby circumventing the issue of asymmetric molecular assembly of tA27-WT that contains disordered N-terminal fragments . At a low protein concentration ( 1 mg/ml ) , the retention volume ( ca . 12 ml ) of tA27-WT in our SEC/MALS analysis was consistent with the previously reported value using a Superdex 75 10/300 GL column; however , MALS analysis indicated that the molecular mass of this fraction is 8 . 1±0 . 3 kDa , which corresponds to monomeric tA27-WT and indicates that the sample is monodispersed ( Figure 5B ) . At a much higher protein concentration ( 9 . 5 mg/ml ) , the retention volume became smaller ( ca . 11 ml ) and the corresponding peak was highly asymmetric , indicating that tA27-WT becomes polydispersed in solution with a rapid equilibrium between different oligomeric states . Indeed , the SEC/MALS analysis indicated a continuous molecular mass distribution ranging from 13 . 4±0 . 4 kDa to 8 . 4±0 . 2 kDa for the asymmetric elution peak . The result suggested that tA27-WT monomer and dimer most likely coexisted at an equilibrium . However , we did not identify the existence of trimeric tA27-WT in solution within this protein concentration range . In conclusion , our SEC/MALS data suggested that tA27-WT protein exists primarily as a monomer in solution at low protein concentration , and becomes more dimeric as its concentration increases . While the results may seem contradictory to the crystallographic findings , it is important to note that the SEC/MALS and AUC analyses are performed in solution while protein crystallization is by default an oligomerization process , which has a very high local concentration . Although tA27-WT was detected as monomer-dimers in solutions , it is important to point out that protein behavior in solution is fundamentally different from protein crystal environments . As tA27-WT exhibits increased oligomerization propensity at high concentration , it is plausible that trimer/hexamer formation ( as observed in the crystal structure ) can occur in vivo where the local concentration is elevated . Because it was difficult to obtain the soluble form of full-length A27 to use in further experiments , we sought to use genetic approaches to address the structure/function relationship of A27 protein in vivo . We performed in vitro mutagenesis , introduced TM-N , TM-C , and 6A mutations into the full-length A27 ORF , and cloned into a plasmid pMJ601 [40] such that a viral synthetic late promoter drove A27 protein expression in the infected cells . We then used a transient transfection-infection system to investigate A27 protein self-assembly in non-reducing conditions , since the A27 trimers are stabilized in vivo by disulfide bonding [11] , [23] . We monitored the 70-kDa and 90-kDa protein complexes that are also formed between A26 and A27 through disulfide bonding [21] , [22] , [23] . 293T cells were transiently transfected with individual A27 plasmid constructs , subsequently infected with WRΔA27L at a multiplicity of infection ( MOI ) of 5 plaque-forming units ( PFU ) /cell and harvested at 24 h post-infection for SDS-PAGE analysis . In reducing conditions , comparable levels of each transfected A27 protein construct were detected in cells; the endogenous A26 protein level in WRΔA27L was similar ( Figure 6A ) . Next , we probed a non-reducing gel with A27 antibody ( Figure 6B ) . WT A27 protein formed monomer/dimer/trimer and 70-kDa and 90-kDa A26–A27 protein complexes in cells , while A27-TM-N protein mainly existed as a monomer ( with a trace amount of dimer ) and as part of the 90-kDa A26–A27 protein complex . A27-TM-C protein was present in monomer and dimer forms , and did not form any complex with A26 . Finally , A27-6A protein only existed as a monomer and did not form any complex with A26 protein ( Figure 6B ) . These results suggest that the TM-N and TM-C mutations each affect A27 protein self-assembly to a different extent , and combining all six mutations in A27-6A eliminated its ability to self-assemble . The TM-N mutation only affected formation of the 70-kDa complex , while the TM-C mutation abolished formation of both the 70-kDa and the 90-kDa A26–A27 complex . The lysates were probed with anti-A26 antibody in non-reducing conditions , and the results are consistent ( Figure 6C ) . H3 protein level in each lane was comparable as stained by anti-H3 antibody ( Figure 6C ) . We want to emphasize that the lack of formed complexes consisting of a combination of A27-TM-C or A27-6A with A26 protein cannot be interpreted as a simple result of insufficient A26 protein level in cells ( Figure 6A and C ) . Each of the A26-containing bands that appeared as doublets were observed before and caused by the incomplete formation of intramolecular disulfide bonds between C43 and C342 in the A26 protein [23] . Encouraged by the transient assay results , we constructed recombinant vaccinia viruses expressing A27 mutant proteins ( Figure 7AΔ ) . Removal of the A27 gene from wild type vaccinia virus caused a defect of MV egress resulting in a small plaque phenotype on BSC40 cells , as observed for WRA27L [27] , [28]Δ . Therefore , we generated a BSC40-A27 cell line on which even WRA27L viruses grew as large plaques because A27-WT protein is constitutively expressed in trans ( Figure 7B ) . A dual expression cassette containing a full-length A27 ORF encoding either A27-WT ( to construct a revertant virus , A27R ) , A27-TM-N , A27-TM-C , or A27-6A mutant protein , and a marker lacZ gene was inserted into the J2R ( tkΔ ) locus of WRA27L virus ( Figure 7A ) . The four resulting recombinant viruses were isolated as large blue plaques by X-gal staining of BSC40-A27 cells ( Figure 7C ) . Although all of the recombinant viruses expressed similar levels of A27 protein in BSC40 cells ( Figure 8A ) , only the WR-A27R virus formed large plaques and WR-A27-TM-N , WR-A27-TM-C , and WR-A27-6A formed small blue plaques on BSC40 cells ( Figure 7C ) . Since A27 protein is necessary to mediate MV egress leading to EV formation and large plaques , the results suggest that mutations in A27-TM-N , A27-TM-C , and A27-6A knocked out A27 protein function to mediate MV transport . It is worth mentioning that only A27 ( not A26 ) is involved in MV egress , as deletion of the A26L ORF did not alter the large plaque phenotype on BSC cells [8] . Consistent with the small plaque phenotype , BSC40 cells infected with WR-A27-TM-N , WR-A27-TM-C , and WR-A27-6A produced less actin-containing EV [25] , [26] in immunofluorescence analysis ( data not shown ) . Taken together , we concluded that residues at both N- and C-terminal trimer interfaces of A27 protein are important for mediating MV egress in infected cells . After obtaining all of the recombinant viruses , BSC40 cells were infected with wild type WR , WRΔA27L , WR-A27R , WR-A27-TM-N , WR-A27-TM-C , and WR-A27-6A at an MOI of 5 PFU/cell , and subsequently harvested at 24 h post-infection for SDS-PAGE analysis in reducing ( +2ME Figure 8A ) or non-reducing ( −2ME ) conditions with anti-A27 ( Figure 8B ) , anti-A26 and anti-H3 ( Figure 8C ) antibodies . Similar to what was observed in the transient expression experiment , A27-WT protein in cells infected with WR and WR-A27R revertant viruses self-assembles into A27 monomer/dimer/trimer ( Figure 8B ) . Some A27 dimer existed as doublets , and the reason is not clear . A27L-TM-N and A27-TM-C proteins self-assemble into monomer/dimer although the proportions of monomer and dimer appeared to be different for each protein . Most interestingly , A27-6A protein was only present as a monomer in cells . Taken together , the A27 immunoblot results suggest that both NTR and CTR interfaces contribute to A27 trimerization and simultaneous interruption of NTR and CTR interfaces dissociates A27 protein into monomers in the infected cells , supporting the A27 crystal structure . We used anti-A26 antibody , to monitor A26 protein level in cells in reducing conditions ( Figure 8A ) . The A26 protein level was very sensitive to the deletion and mutation of A27 protein , not because of nonspecific interruption of A26 transcription or translation , but because WR-A27R revertant virus expresses a high level of A27-WT protein at the same tk locus as these A27 mutant viruses . In the non-reducing condition ( Figure 8C; ) , only WR and WR-A27R contained abundant A26 , which formed 70-kDa and 90-kDa complexes in cells . While WR-A27-TM-N , WR-A27-TM-C , and WR-A27-6A all contained less A26 protein , WR-A27-TM-N remained partly able to form the 90-kDa A26-A27 complex WR-A27-TM-C and WR-A27-6A formed neither the 70-kDa nor the 90-kDa complex ( Figure 8C ) . Combining the results of transient assays ( Figure 6 ) and recombinant viruses ( Figure 8C ) , we concluded that the A27 trimer structure is critical for A26 interaction and protein stabilization . Next , we produced MV particles from the infected cell cultures , purified them using CsCl centrifugation , and subjected them to SDS-PAGE and immunoblot analysis in the reducing ( +2ME ) condition ( Figure 8D ) . WR and WR-A27R MV particles contained abundant A26 and A27 proteins . WR-A27-TM-N contained reduced but detectable amount of A26 and A27 , while WR-A27-TM-C and WR-A27-6A contained little A26 or A27 . All of the purified MV particles contained similar amounts of H3 protein ( Figure 8F ) . When MV particles were analyzed in the non-reducing condition ( −2ME ) with anti-A27 ( Figure 8E ) , anti-A26 and anti-H3 antibodies ( Figure 8F ) , the conclusion was consistent with what was observed in the cell lysates after transient expression ( Figure 6 ) and the virus-infected cell lysates ( Figure 8B and C ) . Together , the results demonstrate that interruption of A27 self-assembly resulted in partial or complete loss of A26 and A27 protein packaging into MV particles . Our previous study results suggested that vaccinia A26 protein on MV controls the pathway specificity of viral entry into HeLa cells [17] . Wild type WR strain MV particles contain A26 protein and enter HeLa cells through an endocytosis pathway however , WRΔA26L MV particles enter cells through plasma membrane fusion , resulting in robust cell-cell fusion at neutral pH [17] , [41] . Using purified vaccinia MV particles from WR-A27-TM-N , WR-A27-TM-C , and WR-A27-6A infections , we investigated whether these MV particles trigger plasma membrane fusion at neutral pH . L cells expressing either green fluorescent protein ( GFP ) or red fluorescent protein ( RFP ) were mixed at a 1∶1 ratio and subsequently infected with each purified MV at an MOI of 50 PFU/cell . Cell fusion was monitored at 1–2 h post-infection , as previously described [17] . WR virus infection did not trigger cell-cell fusion and GFP- and RFP-expressing cells were well separated , similar to mock-infected cells ( Figure 9A ) . On the other hand , WRΔA27L virus proceeded readily with plasma membrane fusion , resulting in gigantic fused cells that co-expressed GFP and RFP . As expected , WR-A27R virus behaved like WR virus and did not trigger cell-cell fusion . WR-A27-TM-N infection exhibited an intermediate phenotype , with multi-nucleate fused cells of smaller sizes than those observed with WRΔA27L . Finally , WR-A27-TM-C and WR-A27-6A triggered gigantic cell-cell fusion similar to WRΔA27L . Quantitative cell-cell fusion data are presented in Figure 9B . This finding is somewhat anticipated because WR-A27-TM-C and WR-A27-6A MV particles contained neither A26 nor A27 proteins . Taken together , the above results revealed that A27 self-assembly structure is critical for A26 protein incorporation into MV particles to suppress plasma membrane fusion at neutral pH .
Vaccinia A27 protein plays multiple roles in the vaccinia virus life cycle , including binding to HS , membrane fusion regulation , and the mediation of MV transport to form EV . In previous studies , we characterized the secondary structure of soluble T7-tA27 using CD and NMR spectroscopy , and we were able to predict the secondary structure of the T7-tA27 monomer [29] . Nonetheless , previously the data only allowed us to propose a “molecular model” of A27 protein , which is not a real 3D NMR structure . In contrast , the current crystal structure has revealed that tA27 protein interaction relies on hydrophobic interactions of the CCD , including L47 , L51 , and L54 at the NTR interface and I68 , N75 , and L82 at the CTR interface . Structure modeling of the CCD domain of A27 orthologs demonstrates conservation of structural folding , implying that the CCD domain evolved to form similar trimers in order to conserve functions . Although we used the terms NTR and CTR interface to differentiate these two contact sites in the crystal structure , both sites remain conserved in the full-length A27 protein . The crystal structure is consistent with previous in vitro studies of recombinant T7-tA27 protein , which demonstrated that the rigid hydrophobic CCD is essential for protein assembly in solution [31] . However , given the fact that tA27 assembles into a rod-like coiled-coil trimer in the crystalline state , the conventional molecular weight estimation employed in previous studies using SEC analysis ( which assumes that the target proteins adopt compact and globular folds ) might be erroneous [31] . In the present study , we employ two independent biophysical methods ( AUC-SV and SEC/MALS ) to determine the molecular weight of proteins without assuming that the protein of interest is compact and globular [39] . Both measurements showed that tA27 is monomeric at a low protein concentration , and a dimeric population emerges when the protein concentration is increased . Thus , the discrepancy between our current data and the previously reported data [31] may be largely attributed to the non-globular structure of tA27 , which contains a highly disordered N-terminal part and a highly elongated C-terminal helix that render the molecular weight estimation by conventional SEC unsuitable . It is not uncommon for the retention volume of a loosely structured protein or an intrinsically disordered protein to deviate from its expected molecular weight , leading to an over-estimation of the molecular weight in spite of the use of protein standards to calibrate the SEC analysis [42] . While the AUC-SV and SEC/MALS results may seem to contradict the crystallographic findings , it is important to note that the SEC/MALS and AUC measurements were performed in solution , while protein crystallization is by default an oligomerization process , which has a very high local concentration . As tA27 exhibits increased oligomerization propensity at high protein concentrations , it is plausible that the formation of a trimer/hexamer as seen in the crystal structure occurs in vivo where the local concentration is elevated . Indeed , our immunoblot analysis identified the presence of dimeric and trimeric A27 in vivo together with the formation of A26-A27 complexes . The recombinant viruses provided strong evidence supporting the hypothesis that these residues residing on the NTR and CTR interfaces are involved in A27 dimer/trimer formation in vivo . Furthermore , the ability of A27 to self-assemble appeared to be coupled with its adaptor function for anchoring A26 proteins on MV and A26 stability . While the wild type MV contained A26-A27 protein complexes of 70-kDa and 90-kDa on MV; the TM-N mutant MV only contained the 90-kDa form , and the TM-C and 6A mutant MV particles lacked both . Consequently , these mutant MV particles , with little or no A26 fusion suppressor on the cell surface , triggered plasma membrane fusion at neutral pH . In fact , the endocytic route of the wild type WR MV into HeLa cells was sensitive to bafilomycin ( an inhibitor of endosome acidification ) ; the entry of WRΔA27L and all three A27 mutants ( WR-A27-TM-N , WR-A27-TM-C , and WR-A27-6A ) became resistant to bafilomycin ( data not shown ) . In the literature , the role of A27 protein in membrane fusion has been puzzling . Earlier studies provided evidence to support the hypothesis that A27 is the viral fusion protein [10] , [11] , [12] , [14] , [43] . Other studies argued against it [15] , [25] , [26] , [44] , particularly considering that A27 protein is inessential for vaccinia MV infectivity in contrast , a viral EFC containing 12 viral components is essential for virus-mediated membrane fusion [15] , [44] . Indeed , the tA27 crystal structure differs from a typical viral fusion protein , as previously proposed [13]–; . Moreover , using these A27 contact interface mutant viruses , we confirmed that the assembly of A27 protein affects formation of the A26A27 protein complex , without which the A26 protein became destabilized the resulting MV particles inherited such changes , and in the next round the virus entry pathway is altered accordingly . Together , our results support that A27 protein is not the viral fusion protein , and instead regulates viral fusion indirectly– by acting as an adaptor to control A26 protein incorporation into MV particles . The current tA27 structure study reinforced our idea that A26A27 protein complex assembly acts as the molecular basis of membrane fusion regulation . Finally , since the GAG binding activity of A27 protein also depends on oligomer formation [29] , future crystallization of the tA27 protein structure in complex with the ligand or other viral proteins will be of importance to help dissect the A27 functions during vaccinia virus entry .
N-Ethylmaleimide ( NEM ) was purchased from Sigma , Inc . BSC40 , BSC40-A27 , HeLa , 293T , and L cells were cultured in Dulbecco's modified Eagle's medium ( DMEM ) supplemented with 10% fetal bovine serum ( Invitrogen ) . The Western Reserve ( WR ) strain of vaccinia virus was used as described previously [8] . Purification of vaccinia virus MV was performed through a 36% sucrose cushion followed by CsCl gradient centrifugation as described previously [45] , [46] . Anti-A26 , anti-A27 , anti-H3 and anti-D8 antibodies were described previously [6] , [17] , [23] . The plasmid construct that expressed recombinant tA27 protein ( aa 21–84 ) containing C71A/C72A has been described previously [29] , [30] , [31] . To obtain recombinant tA27 protein for crystallization , the A27L insert encoding A27 ( aa 21–84 ) -C71/72A ( tA27 ) protein was amplified by PCR and subcloned into pET-32 Xa/LIC ( Novagen ) to obtain pET-32-tA27 . One additional His6 tag was subsequently inserted to increase the binding affinity with the Ni-NTA column for affinity chromatography . To obtain recombinant tA27 protein for in vitro biophysical analysis , a factor Xa cutting site followed by three extra nucleotides ( TGG , encoding tryptophan ) was inserted immediately before the tA27 ( aa 21–84 ) -C71A/C72A ORF and subcloned into pET-32 EK/LIC ( Novagen ) to obtain pET-32-tA27-WT . Plasmids containing tA27-TM-N , tA27-TM-C , and tA27-6A mutations ( Table S3 ) were subsequently generated using two-step PCR and the sequences were confirmed by DNA sequencing . pET-32-tA27 DNA construct was expressed in the E . coli BL21 ( DE3 ) ( Novagen ) strain and purified based on the same procedures described below . Bacterial cultures were grown in LB medium containing 100 µg/ml carbenicillin at 30°C and induced with 0 . 5 mM isopropyl-β-thiogalactopyranoside ( IPTG ) at 15°C overnight until the A600 reached 0 . 8 . Bacteria pellets were harvested by centrifugation at 6 , 000 g for 15 min at 4°C and resuspended in extraction buffer [50 mM Tris , pH 8 . 0 , 20 mM imidazole , 0 . 15 M NaCl , 10% ( w/v ) glycerol , 10 mM MgCl2 , 2 ng/ml of benzonase ( Novagen ) , and EDTA-free protease inhibitor cocktail ( Roche ) ] . The pellets were lysed in Cell Disruption Solutions ( Constant Systems ) and centrifuged at 30 , 000 g for 45 min at 4°C to remove insoluble debris . Proteins were purified using Ni-NTA affinity chromatography ( GE Healthcare ) and eluted with a linear gradient to 100% ( v/v ) elution buffer 25 mM Tris , pH 7 . 5 , [0 . 25 M imidazole , 0 . 15 M NaCl , 10 ( w/v ) glycerol[ . The eluted tA27 sample was dialyzed twice against 5 liters of buffer 25 mM Tris , pH 7 . 5 and 0 . 15 M NaCl and then subjected to Factor Xa digestion to remove the histidine-tagged fusion protein . The untagged tA27 was purified using another Ni-NTA affinity chromatography column ( GE Healthcare ) followed by gel filtration ( HiLoad 16/600 Superdex 75 , GE Healthcare ) in 25 mM Tris , pH 7 . 5 and 0 . 15 M NaCl . SDS-PAGE confirmed the purity of tA27 above 99% ( Figure S1 ) . Recombinant tA27-WT and mutant proteins including tA27-TM-N , tA27-TM-C , and tA27-6A were purified using the same procedures . tA27 was crystallized in hanging drops using the vapor diffusion method at 20°C for 1 to 2 weeks by mixing 2 µl of protein solution ( 5 mg/ml ) with 2 µl of reservoir solution [0 . 1 M phosphate-citrate , pH 4 . 5 , 0 . 2 M NaCl , and 45–50% ( w/v ) polyethylene glycol 200 and equilibrating with 0 . 5 ml reservoir solution . The tantalum bromide derivative crystals were obtained by soaking native tA27 crystals for 6 h in the mother liquor supplemented with 1 mM tantalum bromide cluster ]Ta6Br12]+2[ . Crystals were directly mounted from mother liquor and immediately flash-cooled to 100 K in a stream of cold nitrogen . Diffraction data for native tA27 were collected at Taiwan Contract BL12B2 station at Spring-8 ( Hyogo , Japan ) , and the tantalum bromide cluster derivative data were collected at BL13B1 of the National Synchrotron Radiation Research Center ( Hsinchu , Taiwan ) using the two-wavelength multiple-wavelength anomalous diffraction method at the tantalum edge . Diffraction data were processed and scaled using the HKL2000 package [47] . Five percent of the randomly selected diffraction data were used to calculate Rfree [48] . Statistics are shown in Table 1 . The SHELXD program [49] identified one cluster site per one tA27 trimer in an asymmetric unit using a two-wavelength MAD experiment ( Figure S2 ) . The cluster locations and phases were refined and calculated using SHARP [50] . Density modification and phase extension performed with DM [51] yielded an initial electron density map for automatic model building in Buccaneer [52]Å . This preliminary model served as a search template for using the molecular replacement method to determine the higher resolution structure of native tA27 at 2 . 2 with PHASER [53] . Manual checking and building were performed in Coot [54] , and refinement was done using REFMAC [55] , with non-crystallographic symmetry restraints fixed for chains A and B and translation libration screw refinement . Structure analysis and stereochemical quality were performed with MolProbity [56] . The crystallographic statistics are listed in Table 1 . The superimpositions of the individual chains were performed using Least Square Fit in Coot [54]; . High-quality images of the molecular structures were created with PyMOL ( DeLano , 2002 http://www . pymol . org/ ) . The oligomerization of wild type and mutant tA27 proteins in solution was analyzed on a size exclusion column ( Superdex75 10/300 GL High Performance , GE Healthcare ) in 25 mM Tris , pH 7 . 5 and 0 . 15 M NaCl or pH 3 . 0 at a flow rate of 0 . 2 ml/min . The injection volume was 100 µl , containing 1 mg/ml or 9 . 5 mg/ml protein solution . The molecular weight of wild type and mutant tA27 proteins at pH 7 . 5 were calculated by comparing with those of protein molecular mass standards , including aprotinin ( 6 . 5 kDa ) , ribonuclease A ( 13 . 7 kDa ) , carbonic anhydrase ( 29 kDa ) , ovalbumin ( 43 kDa ) , conalbumin ( 75 kDa ) , and ferritin ( 440 kDa ) . Sedimentation velocity was performed using an XL-A analytical ultracentrifuge ( Beckman Coulter , Fullerton , CA ) with absorption optics , using an AnTi60 rotor with cells containing quartz windows . Samples and control reference buffer ( approximately 400 µL ) were added to double-sector centerpieces radial absorbance data at 280 nm was acquired at 3-min intervals and a rotor speed of 60 , 000 rpm at 20°C . Wild type and mutant tA27 protein samples ( 120 µM ) were in buffer containing 25 mM Tris , pH 7 . 5 , 0 . 15 M NaCl or pH 3 . 0 . The buffer density and viscosity were calculated using SEDNTERP [57] , and the data were analyzed using SEDFIT software [58] . Absolute molecular weights were determined with static light scattering using a Wyatt Dawn Heleos II multi-angle light scattering ( MALS ) detector ( Wyatt Technology ) coupled to an AKTA Purifier UPC10 FPLC protein purification system with a Superdex 75 10/300 GL High Performance size exclusion column ( GE Healthcare ) . tA27 protein ( at concentrations of 1 mg/ml and 9 . 5 mg/ml ) was applied to the size exclusion column in a buffer containing 25 mM Tris , pH 7 . 5 , 0 . 15 M NaCl , and 0 . 02% NaN3 , at a flow rate of 0 . 5 ml/min . A BSA sample ( 3 . 2 mg/ml ) was used as a reference to calibrate the system . The absolute molecular weights of individual peaks in the size exclusion chromatogram were determined using the static light scattering ( SLS ) data in conjunction with the refractive index measurements ( Wyatt Optilab rEX , connected downstream of the LS detector ) . A standard value of refractive index ( dn/d = cη = °0 . 185 ml/g ) was used for proteins , and the buffer viscosity 1 . 0226 cP at 25C was calculated using SEDNTERP . The value of the reference refractive index , 1 . 3452 RIU , was taken directly from the measurement of the Wyatt Optilab rEX with buffer alone passing through the reference cell . To express vaccinia WR full-length A27 protein in BSC40 cells , the WR A27L ORF was codon-optimized for eukaryotic expression ( Gene Script , Inc . ) and cloned into the plasmid pLKO-AS3 . 1-EGFP3′ ( National RNAi Core , Academia Sinica , Taiwan ) . 293T cells were transfected with pLKO-AS3 . 1-EGFP3′-A27 , pCMV-ΔR8 . 91 , and pMD . G ( National RNAi Core , Academia Sinica , Taiwan ) , and the supernatant was collected at 48 h post-infection and subsequently applied to BSC40 cells for lentiviral transduction . These transduced cells expressing EGFP were collected using a FACS Vantage SE/DiVa . The sorted EGFP-positive cells were grown and re-sorted to enrich the population of high-EGFP-expressing cells . The process was repeated three times , and the cells were used in plaque assays . To construct a plasmid for generating the WRΔA27L virus , A26L and A28-A29L ORF sequences were amplified out of viral genomic DNA using PCR and cloned into pBluescript vector as flanking sequences . A luc-gpt cassette was subsequently inserted between the A26L and A28-A29L sequences , resulting in a plasmid pA26L/luc-gpt/A28L-A29L-KS ( − ) . The plasmid was transfected into 293T cells that were subsequently infected with vaccinia virus ( WR ) , and the lysates were harvested at 3 days post infection for isolation of WRΔA27L virus in 1% agar containing 25 µg/ml mycophenolic acid , 250 µg/ml xanthine , and 15 µg/ml hypoxanthine as described previously [59] . In vitro mutagenesis was performed using the QuikChange site-directed mutagenesis kit ( Stratagene ) to obtain mutant A27L ORFs encoding the full-length A27-TM-N , A27-TM-C and A27-6A . The wild type A27L and the mutant A27-TM-N , A27-TM-C , and A27-6A ORFs were cloned into pMJ601 to produce pMJ601-A27R , pMJ601-A27-TM-N , WR-A27-TM-C , and WR-A27L-6A , respectively , all of which were confirmed by DNA sequencing . The resulting plasmids were transfected individually transfected into 293T cells that were subsequently infected with WRΔA27L , and cultured for 2 to 3 days , and harvested for recombinant virus isolation by with three rounds of plaque purification in 1% agar with 150 µg/ml of X-Gal ( 5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside ) . Cell fusion assays were performed as previously described [17] . In brief , L cells expressing GFP or RFP were mixed at a 1∶1 ratio and seeded in 96-well plates ( 4×104/well ) . The next day , cells were pretreated with 40 µg/ml cordycepin ( Sigma ) for 60 min and subsequently infected with CsCl-purified wild-type ( wt ) WR , WRΔA27L , WR-A27R , WR-A27-TM-N , WR-A27-TM-C , and WR-A27-6A viruses at an MOI of 50 PFU/cell in duplicate . After infection at 37°C for 30 min , cells were incubated at 37°C in the presence of ( 40 µg/ml ) cordycepin and photographed at 2 h post-infection using a Zeiss Axiovert fluorescence microscope . The percentage of cells containing both GFP and RFP fluorescence was quantified as cell-cell fusion using Axio Vision Rel . 4 . 8 software . Freshly confluent cells were infected with WR , WRAΔ27L , WR-A27R , WR-A27-TM-N , WR-A27-TM-C , and WR-A27-6A viruses at 37°C for 1 h and subsequently cultured in growth medium containing 1% agar . At 2 days post-infection , the cells were fixed and stained with either 300 µg/ml X-gal or 1% crystal violet/20% EtOH ( Sigma ) . To avoid artificial reduction and disulfide bond rearrangement during cell rupture , cell lysates were prepared in the presence of an alkylating agent , NEM , using methods described previously [23] . Briefly , cells were infected with various viruses at an MOI of 5 PFU/cell for 1 h at 37°C and harvested at 24 h post-infection . For non-reducing gel analysis , cells were placed on ice and washed twice with freshly prepared cold phosphate-buffered saline ( PBS ) containing 20 mM NEM ( Sigma ) and subsequently mixed with an equal volume of SDS-polyacrylamide gel electrophoresis ( PAGE ) loading buffer ( 150 mM Tris-HCl , pH 6 . 8; 20% glycerol; 4% SDS; 0 . 04% bromophenol blue ) , boiled for 10 min , and loaded on NuPAGE 4% to 12% Bis-Tris denaturing gels ( Invitrogen ) . For reducing gel analysis , SDS-PAGE loading buffer was supplemented with 5% 2-mercaptoethanol and cell lysates were separated on 10% and 15% SDS-PAGE gels . Proteins were subsequently transferred onto nitrocellulose membranes for immunoblot analysis with anti-A26 ( 1∶1 , 000 ) , anti-A27 ( 1∶5 , 000 ) , anti-H3 ( 1∶1 , 000 ) and anti-D8 ( 1∶5 , 000 ) antibodies . Coordinates and structural factors of tA27 have been deposited in the Protein Data Bank with 3VOP accession number .
|
Mature vaccinia virus has more than 20 envelope proteins , including the A27 protein , which has multiple functions in the virus life cycle . During virus entry , A27 mediates the attachment of mature vaccinia virus to cell surface heparan sulfate . A27 also tethers a viral fusion suppressor protein , A26 , to mature virions . During virion morphogenesis , A27 mediates mature virus transport in infected cells . We used X-ray crystallography to determine the structure of tA27 protein , which forms a novel hexamer consisting of four parallel strands and two anti-parallel strands . Hexamerization depends on the coiled-coiled domain from L47 to L82 within each tA27 strand , and mutational analysis revealed that amino acid residues within the coiled-coiled domain are critical for tA27 self-assembly in vitro . We extended the importance of tA27 self-assembly into an in vivo system in which A27 protein dimer/trimer formation through the coiled-coiled domain is crucial to its biological activity , and revealed how A27 regulates virus-induced membrane fusion through its ability to form complexes with A26 protein . Since A27 is a critical target of neutralizing antibodies against pathogenic poxvirus infection in humans , our findings provide a structural basis for the development of anti-pox drugs .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"viral",
"attachment",
"viral",
"vaccines",
"viral",
"envelope",
"viral",
"entry",
"viral",
"transmission",
"and",
"infection",
"virology",
"emerging",
"viral",
"diseases",
"microbial",
"pathogens",
"biology",
"microbiology",
"viral",
"structure",
"pathogenesis"
] |
2013
|
Crystal Structure of Vaccinia Viral A27 Protein Reveals a Novel Structure Critical for Its Function and Complex Formation with A26 Protein
|
In this paper we propose a model of visually guided route navigation in ants that captures the known properties of real behaviour whilst retaining mechanistic simplicity and thus biological plausibility . For an ant , the coupling of movement and viewing direction means that a familiar view specifies a familiar direction of movement . Since the views experienced along a habitual route will be more familiar , route navigation can be re-cast as a search for familiar views . This search can be performed with a simple scanning routine , a behaviour that ants have been observed to perform . We test this proposed route navigation strategy in simulation , by learning a series of routes through visually cluttered environments consisting of objects that are only distinguishable as silhouettes against the sky . In the first instance we determine view familiarity by exhaustive comparison with the set of views experienced during training . In further experiments we train an artificial neural network to perform familiarity discrimination using the training views . Our results indicate that , not only is the approach successful , but also that the routes that are learnt show many of the characteristics of the routes of desert ants . As such , we believe the model represents the only detailed and complete model of insect route guidance to date . What is more , the model provides a general demonstration that visually guided routes can be produced with parsimonious mechanisms that do not specify when or what to learn , nor separate routes into sequences of waypoints .
Individual ant foragers show remarkable navigational ability , shuttling long distances between profitable foraging areas and their nest . Despite low resolution vision and the availability of odometric information , many ant species preferentially guide their foraging routes using learnt visual information [2] , [29]–[31] . The robust extraction and learning of the visual information required for route guidance is a product of the interactions between innate behaviours and learning [12] , [32] . We highlight these interplays by sketching out the career of an individual forager . Upon first leaving the nest , a new forager performs a series of short learning walks where a carefully orchestrated series of loops and turns allow her to inspect the visual surroundings from close to the nest entrance [33]–[35] . The knowledge gained during these special manoeuvres means she will be able to use visual information to pin-point the nest entrance after future foraging trips . When she finally leaves the vicinity of the nest she is safely connected to it because of her path integration ( PI ) system [12] , [36] . In order to perform path integration , odometry and compass information are continuously combined such that at all times during a foraging journey the ant has the direction and distance information required to take an approximately direct path home . However , PI is subject to cumulative error and cannot take account of passive displacements , such as by a gust of wind . To mitigate these risks and ensure robust navigation , ants therefore learn the visual information required to guide routes between the nest and their foraging grounds [Reviews: [12] , [37]] . During the early stages of learning the ants are reliant on their PI system for homing . However , as they become more experienced they come to rely more and more on visual information for route guidance [31] . The use of PI also provides consistent route shapes thereby facilitating and simplifying the learning of appropriate visual information [32] , [38] . Extensive behavioural experiments over many years have led to a knowledge base of properties and behavioural signatures of visually guided navigation in ants that can be summarised as follows: Computational models of visual navigation in insects followed experimental findings where ants [42] and bees [16] had been shown to guide their return to a goal-location by matching retinotopic information as remembered from the goal . With their seminal snapshot model , Cartwright and Collett [16] showed that within a certain catchment area [46] subsequent search for a goal location can be driven by a comparison of the current view of the world and a view stored at that goal . This has inspired roboticists and biologists to develop homing models [19]–[26] where a single retinotopic view is used to get back to a location . Snapshot style models represent elegant , but abstract , sensori-motor strategies for navigation yet there are two major directions where such models need developing . Firstly , although snapshot models are very useful for understanding the information that is available in a visual scene [21] , [47] , to fully understand visual navigation we must consider the constraints imposed by a particular motor system and means of locomotion . Secondly , we need to understand how visual knowledge can be applied to the guidance of longer distance journeys and not just to the pin-pointing of a single goal location . A significant component to any view-based homing algorithm is the sensori-motor interaction . The original snapshot model was developed following extensive experiments with bees . In the final stages of locating an inconspicuous goal , bees and wasps are able to fix the orientation of their body axis , perhaps using compass information , and then translate in any direction [48]–[50] . Inspired by this , the original snapshot model relies on stored views and current views being aligned to an external frame of reference before a matching procedure is used to determine a homing direction [16] . This represents a significant challenge for ants , and also for bees and wasps when flying rapidly over longer distances , where translation is predominantly in the direction of the body axis . In the context of our proposed model , however , the tight coupling of sensation and action is used to simplify the problem of learning a route . For an ant with fixed eyes and a relatively immobile head a given view implicitly defines a direction of movement and therefore an action to take . This suggests the following approach: Given the success of snapshot-type models in place-homing , it is natural to assume that navigation over larger scales , that is , along routes , could be achieved by internalizing a series of stored views linked together as a sequence . Route behaviour in this framework would entail homing from one stored view to the next in a fixed sequence . While it has been shown that the catchment areas of individual snapshots can be quite large [21]–[47] , [53] , attempts to model route navigation using linked view-based homing have shown it to be a nontrivial problem which requires the agent to both robustly determine at which point a waypoint should be set during route construction and when a waypoint has been reached during navigation [19] , [27] , [28] . Essentially , for robust route navigation using a sequence of snapshots , an agent needs place recognition to determine where along the route it is [54] . Here we propose a different model that develops and refines ideas that have been recently put forward as an alternative to such a scheme [55] . Instead of defining routes in terms of discrete waypoints all views experienced during training are used to learn a holistic route representation . We test our proposed route navigation strategy in simulation , by learning a series of routes through visually cluttered environments consisting of objects that are only distinguishable as silhouettes against the sky . This represents a challenging task due to the paucity of information and the potential for visual aliasing , whereby two locations appear similar enough so as to be indistinguishable . Our results indicate that , not only is the approach successful , but also that the routes that are learnt show many of the features that characterise the routes of desert ants .
Our navigation algorithm consists of two phases . The ant first traverses the route using a combination of PI and obstacle avoidance ( as specified in the Materials and Methods ) during which the views used to learn the route are experienced . Subsequently , the ant navigates by visually scanning the world and moving in the direction which is deemed most familiar . In the later experiments , the route is learnt by a neural network and the familiarity of each view is the output of the trained network . However , to show the utility of the proposed scanning routine , without the added complication of learning a familiarity metric , we first explored the performance of a system with perfect memory . This was implemented by storing views experienced every 4 cm along a training route and using these to determine view familiarity directly . Following Zeil et al . [21] we calculate the sum squared difference in pixel intensities between rotated versions of the current view and each stored view . The minimum across all stored images and all viewing directions experienced during a scan of the world from the current location is deemed the most familiar view for that location and a 10 cm step is taken in the viewing direction associated with this minimum . Figure 1 shows that by storing the views along a training path and using these to drive a subsequent recapitulation of the route , robust behaviour is achievable . We used our algorithm to learn three routes through an environment containing both small and large objects randomly distributed across the environment . Three subsequent navigation paths were attempted for each route . Of the nine paths , all but one successfully return to the nest location , with the one failure caused by the simulated ant being drawn out of the stable route corridor by the presence of a tussock that dominates the visual field and causes visual aliasing . Despite the noise added to the movements during the recapitulation , paths are idiosyncratic though inexact . Within a corridor centred on the original route both a good match and a sensible heading are recovered that will , in general , drive the simulated ant towards the goal ( Figures 1B–D ) . Outside of this route corridor the best match becomes poorer and , particularly within areas containing a high degree of visual clutter ( i . e . within a group of tussocks ) the proposed direction of movement less reliably points towards the goal . This is seen most clearly in panel C where , very close to tussocks , a significant proportion of the homeward directions determined by the algorithm ( white arrows in Figure 1B–D ) point away from the goal location . Often these erroneous signals direct movements back into the route corridor , although this is clearly a matter of chance . The routes that are generated show a distinct polarity meaning that they can only be traversed in a single direction as is evidenced by the coherence of the homeward directions ( arrows in Figure 1B–D ) . Importantly , the actions that result from following this strategy are not tied to a coordinate system and are therefore completely independent from the PI system that provided the initial scaffold for learning . In addition , the resulting routes are not dependent on a chained sequence of actions; appropriate actions are taken at any location along the route corridor independent of how that location was reached . One potential problem with this navigational strategy is that if the simulated ant overshoots the goal it will , in general , carry on heading in the same direction and move further and further away from the goal location ( Figure 2A ) . This is because there are no training views that point back towards the nest once it has been passed . This problem can be mitigated by including an exploratory learning walk during the training phase , a behaviour seen in many species of ants [33]–[35] . These initial paths take the form of a series of loops centred on the nest as can be seen in Figure 2B which shows the learning walk of a Melophorus bagoti worker taken from a paper by Muser et al . [33] . Essentially , this process means that in the region around the nest there will always be some views stored which are oriented back towards the nest . To explore the possible effects of these initial short learning walks , the views experienced along them were added to the set of inbound views used for route learning . Figure 2 shows the end section of a route navigated after training with and without a learning walk . In these tests the simulation was not stopped when the simulated ant reached the nest location , analogous to blocking the nest entrance in a behavioural experiment . With the addition of a learning walk ( Figure 2B ) , as the simulated ant passes the nest , rather than the best match being from the training route and oriented upwards ( as in Figure 2A ) , the best match comes from the learning walk . The simulated ant is drawn into the loop of the learning walk it first encounters , leading to the looped paths seen in Figure 2B . Close to the nest , the density of points from the learning walk increases and there are multiple views from nearby locations oriented in a variety of directions . The best match at subsequent points will then likely be from different learning walk loops and so the ant stops following a single loop and enters more of a search-type path around the nest . Thus , our algorithm demonstrates both route following and nest search with the same mechanism . Here we have shown that by storing and using panoramic views as they were experienced and aligned during training , we can achieve visually guided route navigation through a scanning routine and without recourse to a compass . The model is of particular interest since the resulting paths show remarkable similarities to many of the features that we observe in the routes of ants . Specifically , independence from the PI system that is assumed to scaffold the original learning; distinct polarity of routes; formation of a route corridor; and procedural rules that can be accessed out of sequence . By including a learning walk we can also get visually driven search for the nest location from the same mechanism . This algorithm demonstrates the efficacy of using a simple scanning behaviour as a strategy for seeking familiar views . However , the algorithm relies on the unrealistic assumption of a perfect memory of views experienced along the training route . We next investigate a more realistic encoding of the visual information required for navigation by training an artificial neural network using the views experienced along a return journey and a learning walk . Having shown that the proposed scanning routine can produce ant-like paths , we next addressed the problem of learning a familiarity metric to use in place of a perfect memory system . Instead of storing all of the views experienced on a training route , the views were used to train a two-layered artificial neural network to perform familiarity discrimination using an Infomax learning rule [59] . Each view was presented to the network in the order in which it was collected and then discarded . This means that the memory load does not scale with the length of route but remains constant . Once trained , the network takes a panoramic image as input and outputs a familiarity measure indicating the likelihood of the view from that location and orientation being part of the learnt route . The trained network was then used in conjunction with the scanning routine to drive route navigation by presenting the rotated views to the network , and choosing the most familiar direction as the direction in which to navigate . The only difference in the behavioural routine was that the scanning range was reduced from to a slightly more realistic scan centred on the direction of travel from the previous timestep . In a first experiment using this approach we employed the Infomax system to learn the same three training paths as in the previous experiment using a perfect memory . As Figure 3 shows , in this instance all returns were completed successfully . We do not believe this indicates that the approach is more robust than the perfect memory system but simply that the noise added to the system did not happen to nudge the agent into an area of the environment where visual aliasing would occur . In other ways the results of this experiment are very similar to the results obtained using a perfect memory . As these routes were learned using a single exposure to each of the training views , we are thus able to fulfil another of the desiderata for ant-like visual mediated route navigation: that routes can be learnt rapidly , in this case following a single trial . To show that learning was not environment specific we conducted further simulations . Environments with varying densities of tussocks were randomly generated and a simple algorithm that performed path integration with obstacle avoidance was used to generate paths through them . In all of the environments we provided a distant horizon consisting of bush-like and tree-like objects as would be present in the natural environment of Melophorus bagoti [33] . In these experiments we also included a simplified learning walk at the start of training to prevent the simulated ant overshooting the goal . We first examined a low tussock density environment compared to the environment used previously . Performance was good , although the lack of nearby objects resulted in less consistent paths ( Figure 4 ) . Example views taken from the training route ( Figure 4 , right ) show how the panorama of distant objects provide a stable frame of reference throughout the route . Despite the sparse visual information in this environment , the distant objects help to keep the return paths heading in the right direction . The effect that the structure of the learning walk has on the return paths can be clearly seen near the goal location . As the simulated ant nears the goal it gets drawn into a series of left and right sweeps that reflect the left and right inbound loops of the learning walk and are analogous to an ant's search for its often inconspicuous nest entrance . We next used an environment with a more dense set of tussocks ( Figure 5 ) . In this more densely tussocked world the distant panorama is no longer visible at all points along the route . This clearly makes route learning more difficult as is evidenced by the failures in three of the four runs . Because noise is added to the simulated ant's heading during route recapitulation the simulated ant may stray into previously unexperienced parts of the environment which , even a short distance away from the learnt route , can look very different in this cluttered world . Two attempts fail early when noise added to the heading leads the simulated ant to go to the left of a small tussock taking it into a part of the world with which it is not familiar . The other two returns do reasonably well . They do show some circling of tussocks , driven by training views where the path goes very close to a tussock and dominates the visual field , however both paths make it very close to the nest . This is a challenging environment in which to navigate and was picked to be at the limit of the algorithm's learning power following just one training run; other runs using a similar density of tussocks were more successful . Performance also improved if we removed or reduced the noise that was added to the direction of movement at each timestep . Of course , ordinarily ants would incorporate knowledge from several foraging trips during which time their performance becomes more stable and robust . We investigate this in the next section . Performance of our algorithm was often quite reliable following a single training run but there were still failures ( Figure 5 ) . Ants , however , do not just use a single training run but will continue to develop their knowledge of the surroundings during multiple runs . We therefore investigated the effect on performance if multiple subtly different training routes were combined . The path integration algorithm that we used allowed the generation of multiple paths that were similar but not identical . The views collected along a number of paths were used to train the network . The learning scheme did not need to be altered as each view collected was simply presented to the network in the order that it was experienced . Performance is shown for a twelve metre route in one of the more challenging environments ( tussock density 0 . 75 ) following 1 , 2 , 4 and 8 training runs ( Figure 6 ) . Using multiple training runs can be seen to aid robustness , and after 8 training runs ( Figure 6 , far right ) the recapitulated routes are efficient and consistent , even in this high tussock density environment . With repeated training runs the network will be exposed to a more comprehensive set of views from the route than with a single training run . It should be noted that using , say , four training runs is not the same as sampling the views four times as often during a single training run . In the latter case , sets of four consecutive points are not independent of each other . Using multiple runs however , views from similar locations are coupled only through the environment and thus variation in the views reflects the variation that will be experienced during navigation . For instance , if the distribution of objects in the world means that the training routes are canalised down a narrow corridor , it is likely that the navigated route will also go down a narrow path and so it does not matter that the training views from each run are similar . However , if the route corridor is broader , or even allows multiple paths , then multiple training routes allow a wider set of views that might be experienced when navigating , to be captured . Multiple runs therefore allow a broader , more robust , route corridor to be learnt . It has been shown that Melophorus bagoti are able to learn and maintain more than one route memory when forced to learn distinct return paths to their nest from a series of different feeders [6] . In the experiments performed by Sommer et al . [6] , seven training runs along a first route were followed by a control run to test whether the ant had learnt the route . This training schedule was repeated for a further two routes that each led back to the same location - the nest . Finally , the ants were tested on the first two routes to see if they had retained the original route memories . Here we attempt to replicate this experiment using our route learning algorithm to learn three 10 m routes performed in an environment with a tussock density of 0 . 75 . To do this we train a network using the first route . The network is then tested before we continue to train the network using views from the second learning route . The network is then again tested before the final training session using views from the third route , before finally being tested on all three routes . The performance can be seen in Figure 7 . The network is able to learn and navigate multiple routes without forgetting the earlier ones . It is interesting to note that when the third route is recapitulated following learning , the paths tend to get drawn back onto the previously learnt route 2 , representing a possible confabulation of these two memories within the network . The individual route memories are not held separately and the return paths for route 3 are drawn back to route 2 as , at that point in the world , views from routes 2 and 3 are similar . This is not wrong per se , as the important thing is that the routes lead safely back to the nest . Also this property of routes can be seen in the original paper [6] .
Our own experience tells us that the human capacity for visual recognition is remarkable and clearly outstrips our capacity for recall . For instance , our ability to decide whether we have met somebody before , runs to many more people than those we can explicitly recall specific facts about . Theoretical investigations of abstract neural network models back up this intuition , with familiarity discrimination or recognition models having far greater capacity than associative models with the same number of processing units and weights [60] . Given the limited neural resources available to an ant and the need for rapid learning it makes sense to develop a navigational strategy that relies on recognition , as building either a cognitive map or employing some other form of associative learning are both harder tasks . The fact that , in our experiments , sensible behaviour can be generated following a single traversal of a route indicates that a form of recognition memory may be sufficient for route navigation in the real world . In fact we would expect that in many ways the problem would be easier for an ant operating in the real world where there would be more information available to disambiguate different views and thereby reduce visual aliasing . The current model presupposes that the only information available to guide behaviour is provided by the high contrast silhouettes of objects against the sky . While we know that ants are able to use skylines to orient themselves [7] , any additional visual information , for example colour , texture or celestial cues , information from other modalities [57] , [61] , [62] , or internal motivational cues , would only help to reduce aliasing and improve reliability . Whether insects have the appropriate brain architecture for storing visual information in this way is not known , though the mushroom bodies would be the obvious candidate neural structure . These higher brain centres , that are enlarged and elaborated in central place foraging insects , have been implicated in a number of cognitive functions including olfactory processing and associative learning [63]–[65] , attention [66] , sensory integration , sensory filtering [67] , [68] and spatial learning [69] , [70] . Farris and Schulmeister [71] present compelling evidence that large mushroom bodies receiving visual input are associated with a behavioural ecology that relies heavily on spatial learning . Furthermore , recent research by Stieb et al . [72] implicates the mushroom bodies in the behavioural transition from working inside the nest to foraging outside . In light of our model it would be interesting to evaluate the potential of the mushroom bodies for familiarity discrimination or recognition memory . The pseudocolour plots in Figure 3 indicate how familiarity could provide another source of information for making routes more robust . If an agent was able to follow a combination of the gradient of the familiarity and the heading of the most familiar direction this would have the effect of drawing the recapitulated paths back onto the habitual route . While this gradient is apparent in the plots that are obtained by sampling from a dense grid of points , it is less obvious how an ant might extract this information , since it would be necessary to sample at least three non-collinear points whilst maintaining the most familiar heading . For a flying insect this would be much less of a problem . The familiarity gradient alone will only serve to draw paths back onto the route and will therefore not produce route following behaviour . However , preliminary results indicate that performance is more robust when the direction indicated by the most familiar view is combined with the familiarity gradient . We have shown how a familiarity metric could in principle be used to guide successful route navigation; the proposed motor program is however not realistic . Although ants have been observed performing scanning behaviours such as we have used , in general they proceed in a far more purposeful manner when on or near their habitual routes . One issue that we need to address therefore is how familiarity of views could be used in a way that is more consistent with the fine-grained movements that ants actually perform . In order to do this we will need to simulate an environment in which behavioural experiments have been conducted and record in fine detail the movements of ants during their foraging career . In our second set of experiments we train a network with the views experienced during a learning walk and along a route . There is no requirement for specific views to be selected and following training the network provides a holistic representation of visual information rather than a set of discrete views . The network in fact holds a holistic representation of all the visual information needed for the agent to get to a particular goal , as shown by our replication of multiple route learning . We have previously shown that other neural network models are also able to holistically encode this information [55] , [73] . However the particular elegance of the Infomax procedure is that each view is presented to the network once and then discarded . The consensus view amongst biologists is that ants do not hold spatial information in a unitary cognitive map [12] , [74]–[76] . Indeed experiments have shown that the memories required to get to one goal ( e . g . the nest ) are insulated from the memories required to get to a second goal ( e . g . a regular feeding site ) [4] , [77] . Indeed , if food-bound and nest-ward routes do not overlap then ants captured as they try to get to their nest are effectively lost if they are placed on their familiar food-bound route [4] . Our model could account for this if the motivational state of the animal formed part of the input to the familiarity network . In this way , views would appear familiar only within the correct motivational context . One of the key properties of this model is that route guidance and place search come from the same mechanism . This comes from incorporating the views experienced during a learning walk into the overall task . Learning walks ( and flights in bees and wasps ) are a form of active vision where the insect shapes its own perception in a way that is beneficial for learning . This principle is demonstrated by our design of an artificial learning walk . If the views on the outbound sections of the learning walk are made to be more variable than those on the inbound sections , then the inbound views will be learned preferentially . A simple way to achieve this is to have curved outbound routes and straight inbound routes ( see the Materials and Methods ) , a learning walk scheme that performed well . We imagine that when we have an understanding of how real learning walks are structured by the environment , performance will be improved and search paths will more closely resemble those that have been observed in ants . Another more complex way to modulate learning would be to turn-off learning when not heading towards the nest . This would require some sort of input from the PI system and interestingly , recent detailed descriptions of learning walks in Ocymyrmex [34] highlight that PI is likely to be used to ensure ants look at the nest at discrete points during their learning walks . However , these learning walks are still compatible with either behavioural or cognitive modulation of learning . The use of PI might only be used to structure the learning walks and allow the ant to accurately face its nest thereby facilitating behavioural modulation of learning [52] . We have presented a parsimonious model of visual navigation that uses the interaction of sensori-motor constraints with a holistic route memory , to drive visual navigation . The model captures many of the observed properties of ant navigation and importantly visual navigation is independent of odometric or compass information . Additionally , in the model one does not need to specify when or what to learn , nor separate routes into sequences of waypoints , thus the model is a proof of concept that navigation in complex visual environments can be achieved without those processes . Our principal goal in this research project is to understand the likely and viable mechanisms underpinning insect navigation . Therefore our next step will be to evaluate the model using fine-grained recordings of ants learning and performing routes in their natural habitat .
To create the environments used in our experiments , a distant panorama of trees and bushes was generated and uniformly distributed densities of tussock-like objects were created over a central region . While the placement of the tussocks was performed by sampling from a uniform distribution , environments that did not contain many tussocks in the vicinity of the training paths were rejected . In some of the experiments additional 3D objects such as large trees and a building were added within the central region . The environment is intended to produce panoramic views that are typical of the natural environment of the Australian desert ant Melophorus bagoti ( See [7] , [8] , [37] , [78] for example images of this environment ) . Figure 8 shows an overview of a typical environment together with a series of views along a route . Notice how variable the views are and also how insignificant the large tree and the building ( the solid black objects in Figure 8B ) can be from the perspective of an ant . This is easiest to see in Figures 8D and E taken from the middle section of the route where the house , which is NE of the ant ( i . e . just over half way along the image; notice the triangular roof in the high-resolution image ) blends in to a tussock . The simulated environment , programmed in MATLAB , consists of objects formed from flat black triangular patches as described below and rendered at a high resolution ( Figure 8D ) , prior to being re-sampled at the low resolution of the simulated visual system ( Figure 8C , E ) . This allows for subtle changes to be registered in response to small movements as would be the case for an ant with a low resolution visual system acting in the real world . This means the resultant view is composed of grey-scale values when a pixel is neither completely covered by sky nor completely covered by an object . In our simulated environment nearby objects are rendered in three dimensions whereas objects at a distance greater than 20 m from the route are flat but oriented so as to be maximally visible . Once an environment consisting of triangular patches has been created , a panoramic view from any position within the environment can be generated as follows . We first change the origin of the world to coincide with the position of the simulated ant by subtracting the current x , y and z coordinates of the ant from the set of vertices , X , Y and Z that define the triangular patches . The set of vertices [X , Y , Z] are then converted into spherical coordinates [] that represent the azimuth , elevation and radial distance . The radial information is discarded and the patches re-plotted in 2D giving the required binary panoramic view ( Figure 8D ) which is stored as a high-resolution [size ] binary matrix . The final step is to reduce the resolution of this image to , which represents the approximate sampling resolution of the compound eyes of Melophorus bagoti workers [78] . The binary matrix is resized to [] using the imresize function in MATLAB and the average value of each [] block is then used as the value of the corresponding pixel in the low resolution representation . This averaging results in values in the range [0 , 1] , with values between the two extremes indicating the fraction of sky and objects covered by a pixel in the original high resolution image [Figure 8E] . The routes shown in Figure 8 and used in the first sets of experiments ( reported in Figures 1 and 3 ) are return paths taken from a paper by Muser et al . [33] that describes the foraging ecology of Melophorus bagoti . While we have no knowledge of the real environment from which these paths were recorded we assume that the overall straightness of the paths is somewhat typical and that they therefore represent a reasonable example of the sort of paths that these ants must learn . In subsequent experiments , paths were generated iteratively starting from the end point of the outbound route using a combination of path integration and obstacle avoidance . Path integration was approximated by centring a Gaussian distribution with a standard deviation of on the correct homeward direction and sampling from this distribution . Obstacle avoidance was incorporated into the path generation scheme by modulating the Gaussian distribution used for path integration by multiplying it by the proportion of sky visible in each direction , ( effectively the inverse of the height of the skyline; Figure 11A , B ) , raised to the power of 4 , ( Figure 11C ) . The resulting modulated Gaussian ( Figure 11E ) was renormalized and sampled from to determine a movement direction and a 4 cm step was taken in this direction . Training images are collected after every step . The obstacle avoidance modulation has the effect of biasing movements towards lower portions of the horizon while preventing completely movements towards objects that fill the entire visual field in the vertical direction . Due to the sampling involved in this process , individual paths between two locations will vary slightly allowing the collection of subtly different sets of images describing a route . A return path was considered complete when the distance to the nest was less than 4 cm . Where learning walks were added to the training routes , we sampled views from pre-specified paths around the nest . Ants generally walk slower during their learning walks and so samples were taken every 2 cm along the paths as opposed to the 4 cm sampling that was used for generating the route data . These views are added to the start of the set of route views used to train the network in the order that they appear , beginning at the nest . The learning walk in Figure 2B is taken from [33] but see also [34] for another route shape that could have similar properties . The artificial learning walks were generated using a circular path with a radius of 0 . 5 m for the outbound section and a straight path for the inbound section ( Figure 12 ) . This is inspired by data from the learning flights of bumblebees whose early learning flights contain many loops with an inward portion oriented directly at the nest [A . Philippides , personal observation] . In the experiments that we report using a perfect memory system , route recapitulation is performed using a complete scan of the environment ( in steps of ) at each timestep . Normally distributed noise with a standard deviation of is added to the preferred direction of movement and a 10 cm step is made in this direction ( Figure 13 ) . In the experiments that we report using the Infomax model we employ a slightly more realistic scanning routine during route recapitulation and instead of performing full scans we limit the scans to the frontal in steps of relative to the current heading . We did this to make the scans more similar to those that real ants produce which are rarely as large as . This had a negligible effect on performance except that it made it impossible to follow a path that had any turns greater than as were present in the Muser et al . learning walk in Figure 2B . As before , normally distributed noise with a standard deviation of is added to the preferred direction of movement and a 10 cm step is made in this direction . However , when generating the pseudocolor plots in Figures 4 and 5 , we did not have a current heading and so performed a full scan to generate an assumed movement direction . For the perfect memory system each of the views experienced along a training path was stored . we then calculated a familiarity metric as minus the minimum of the sum squared difference in pixel values between the current view and each of the stored views , . ( 1 ) The maximum familiarity score across all rotated versions of the current view will be obtained for the most similar stored view and the direction from which this maximum was attained determines the next movement to make . In this setting , if the simulated ant does not stray from the training path then it is guaranteed to choose the correct direction to move at each timestep . This is because the most similar view will always be the one that was stored at that location while facing in the direction required to recapitulate the route . In order to perform familiarity discrimination we chose to use a neural network model that was specifically designed to perform this task [59] . The architecture consists of an input layer and a novelty layer with activation functions ( Figure 14 ) . The number of input units is equal to the dimensionality of the input which in our case is , the number of pixels in a down-sampled view of the world . The number of novelty units is arbitrary and here we follow [59] and use the same number of novelty units as inputs . We found that using as few as 200 novelty units can work well in many instances . We did not explore this aspect of the problem in any detail since we were more interested in the behavioural consequences of a familiarity driven approach . The network is fully connected by feedforward connections . Weights are initialised randomly from a uniform distribution in the range and then normalised so that the mean of the weights feeding into each novelty unit is 0 and the standard deviation is 1 . The network is trained using the Infomax principle [79] adjusting the weights so as to maximise the information that the novelty units provide about the input , by following the gradient of the mutual information . The core update equation ( 4 ) in our learning scheme performs gradient ascent using the natural gradient [80] of the mutual information over the weights [81] ( use of the natural gradient avoids the computationally expensive calculation of the inverse of the entire weight matrix ) . Since two novelty units that are correlated carry the same information , adjusting weights to maximise information will tend to decorrelate the activities of the novelty units and the algorithm can thus be used to extract independent components from the training data [81] . We choose to use this approach mainly because it only requires a single pass through the data . This means that each view is experienced just once and then discarded . While with a limited amount of data the algorithm is unlikely to converge to a particularly good set of independent components , it is enough that the components that are extracted provide a more suitable decomposition of the training data than of an arbitrary input . During learning the activation of each of the novelty units is computed as: ( 2 ) where is the value of the input and is the number of input units . The output of the novelty units is then given by: ( 3 ) The weights are adjusted using the following learning rule: ( 4 ) where is the learning rate and is set as 0 . 01 for this paper . Finally , the response of the network to the presentation of an unseen N-dimensional input is computed as ( 5 ) where denotes the absolute value . The network response could be viewed as an output layer but as it is a function of the activations of the novelty units , we follow [59] and do not represent it with another layer ( Figure 14 ) . As noted above , in this paper we set and the network is trained with each training view presented just once to the network in the order in which it is experienced in training . In [59] the authors use together with a threshold that must be determined empirically to determine whether the input is novel or familiar . For our purposes it is not necessary to determine a threshold as we only need to choose the most familiar input from a limited number of possibilities i . e . the views experienced during a single scan of the environment . The difference between the way an image difference function and a neural network trained using an Infomax principle represent familiarity will be subtle . In essence , the difference is manifest in the way the information is stored . For image differences , each stored view defines a single point in an n-dimensional space , with n equal to the dimension of the images ( n = 90×17 = 1530 ) and the image difference function gives the squared Euclidean distance of an input image from one of these stored points . This requires all of the views to be stored and so memory load increases as more views are experienced . The Infomax approach instead decomposes each view into a fixed number of components ( determined by the number of hidden units in the network ) which remains constant , independent of the number of views experienced . The Infomax measure is more abstract and reflects whether a test input is well described in terms of the learned components that the hidden units represent . By decomposing the input in this way it is possible compress redundant data resulting in more efficient memory storage .
|
The interest in insect navigation from diverse disciplines such as psychology and engineering is to a large extent because performance is achieved with such limited brain power . Desert ants are particularly impressive navigators , able to rapidly learn long , visually guided foraging routes . Their elegant behaviours provide inspiration to biomimetic engineers and for psychologists demonstrate the minimal mechanistic requirements for complex spatial behaviours . In this spirit , we have developed a parsimonious model of route navigation that captures many of the known properties of ants routes . Our model uses a neural network trained with the visual scenes experienced along a route to assess the familiarity of any view . Subsequent route navigation involves a simple behavioural routine , in which the simulated ant scans the world and moves in the most familiar direction , as determined by the network . The algorithm exhibits both place-search and route navigation using the same mechanism . Crucially , in our model it is not necessary to specify when or what to learn , nor separate routes into sequences of waypoints; thereby providing proof of concept that route navigation can be achieved without these elements . As such , we believe it represents the only detailed and complete model of insect route guidance to date .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"animal",
"models",
"computer",
"science",
"model",
"organisms",
"computer",
"modeling",
"biology",
"computational",
"biology"
] |
2012
|
A Model of Ant Route Navigation Driven by Scene Familiarity
|
Several myopathies are associated with defects in autophagic and lysosomal degradation of glycogen , but it remains unclear how glycogen is targeted to the lysosome and what significance this process has for muscle cells . We have established a Drosophila melanogaster model to study glycogen autophagy in skeletal muscles , using chloroquine ( CQ ) to simulate a vacuolar myopathy that is completely dependent on the core autophagy genes . We show that autophagy is required for the most efficient degradation of glycogen in response to starvation . Furthermore , we show that CQ-induced myopathy can be improved by reduction of either autophagy or glycogen synthesis , the latter possibly due to a direct role of Glycogen Synthase in regulating autophagy through its interaction with Atg8 .
Autophagy describes the sequestration of a cell's own cytoplasm and organelles into a closed double-membrane bound vesicle [1] . The completed vesicle , called the autophagosome , fuses with the lysosome , where its inner membrane and contents are degraded by hydrolases . The resulting degradation products are transported back to the cytoplasm where they can be reused for protein synthesis and ATP production . A major role of autophagy is therefore to liberate amino acids , fatty acids , and glucose that can be used to maintain cellular functions during stress and starvation . In mice , autophagy increases in most organs under starvation conditions , with muscles showing a particularly clear response [2] . Interestingly , glycogen-rich fast-twitch fibers induce autophagy much more robustly than oxidative slow-twitch fibers , suggesting a link between glucose metabolism and autophagy regulation . Several myopathies are associated with accumulation of autophagic and lysosomal vesicles containing glycogen , but for most of them it remains unclear how glycogen metabolism connects to the pathology of the diseases [3] , [4] . Among these are the hereditary primary lysosomal myopathies Pompe disease and Danon disease , infantile autophagic vacuolar myopathy , and the drug-induced vacuolar myopathies caused by treatment with chloroquine ( CQ ) or hydroxychloroquine [4] . The best characterized of these is the lysosomal storage disorder , Pompe disease , also known as glycogen storage disease type II . Pompe disease is caused by a mutation in the gene encoding acid a-glucosidase ( GAA ) , an enzyme that localizes to the lysosome , and hydrolyzes glycogen to glucose [5]–[7] . Deficiencies of GAA in both humans and in mouse models lead to accumulation of lysosomes swollen with undegraded glycogen , as well as a secondary defect in the fusion between autophagosomes and lysosomes [8]–[10] . The resulting accumulation of autophagosomes and functional block of autophagy damages the muscle tissue and interferes with the efficacy of enzyme replacement therapy [11] , [12] . The list of disorders classified as autophagic vacuolar myopaties ( AVMs ) is growing , although none but Danon and Pompe disease have been mapped to a causative gene [13] . More common than the myopathies described above , drug-induced myopathy may occur in as many as 12% of patients receiving antimalarial treatment with CQ [14] . CQ and its closely related analog hydroxychloroquine are 4-aminoquinoline compounds widely used to treat malaria , rheumatoid arthritis , and lupus erythematosus [15]–[17] . The drugs are highly lysosomotropic , causing an increase in lysosomal pH and inhibiting the fusion between autophagosomes and lysosomes [18] , [19] . Thus , much like Pompe and Danon diseases , CQ myopathy may result from a blockage of autophagic flux indirectly caused by a lysosomal defect . Glycogen is a major component of the vacuoles in CQ myopathy patient biopsies , and a massive accumulation of glycogen filled autophagosomes was reported in denervated muscles of CQ-treated rats [20]–[22] . In addition to the glycogen-filled autophagosomes and lysosomes that appear during myopathies , mouse and rat neonates exhibit a dramatic autophagic sequestration of glycogen granules in the liver as well as in skeletal and cardiac muscles [23]–[25] . Lysosomal degradation of the large stores of glycogen in fetal tissues may be important for the survival of the animal during the starvation that occurs in the first few hours of life [26] . However , it remains unclear how the lysosomal degradation of glycogen during this period is regulated and whether it is substantially different from the lysosomal degradation of glycogen observed in skeletal muscle myopathy . Indeed , the mechanism of transport of glycogen to the lysosome is poorly understood in both cases . The process of lysosomal glycogen degradation is sometimes referred to as glycogen autophagy ( or glycophagy ) . Studies in yeast have identified 35 ATG ( autophagy-related ) genes , many of which are conserved in higher organisms [27] , [28] . In all eukaryotes , autophagy is induced via the autophagy-related gene 1 ( Atg1 ) complex . Autophagosomal membrane nucleation involves a complex containing Vps34 ( the class III PI3K ) . Expansion of the autophagosome membrane requires two distinct sets of ubiquitin-like protein conjugation systems , Atg8 and Atg5–Atg12 . Fusion with the lysosome requires the endocytic Rab proteins , HOPS complex , SNARE machinery , and the LAMP-1/LAMP-2 lysosomal membrane proteins [29] . The only in vivo genetic analysis of the role of these systems during glycogen transport to the lysosome was performed in the mouse model of Pompe disease , where glycogen accumulation in the lysosome was diminished in Atg7-deficient GAA KO muscles [12] , [30] , [31] . The consequence of genetic suppression of autophagy in muscles has not been reported for the other vacuolar myopathies and CQ myopathy , nor has the prevalence of glycogen autophagy been examined in neonatal autophagy gene mutants . Thus , it is not known to what extent the classic autophagic pathway is involved in glycogen autophagy , nor what effect autophagy suppression would have on the myopathy phenotypes . A high level of conservation with higher organisms makes the D . melanogaster muscle an attractive system to study cellular processes , such as autophagy , that are involved in human disease [32] , [33] . Here , we establish an in vivo model of glycogen autophagy in the D . melanogaster larval skeletal musculature , using CQ to simulate an autophagic myopathy that is completely dependent on the core autophagy genes . In this system , glycogen autophagy is triggered by nutrient deprivation , and is required for maximum rates of glycogen degradation in the muscle . Knockdown of D . melanogaster Glycogen synthase ( GlyS ) , which is highly expressed in muscle , effectively blocks the formation of enlarged CQ-induced autophagosomes . This may be due to a direct function of GlyS , which localizes to autophagosomes and is able to form a complex with Atg8 in response to starvation . Formation of this complex is inhibited by mutations of either the GlyS putative LC3-interacting region ( LIR ) or an arginine predicted to be involved in glucose-6-phosphate binding .
Our first goal was to establish an in vivo system to analyze the effects of CQ treatment on the larval skeletal musculature . Using a GFP-tagged version of the conserved lipid-conjugated ubiquitin-like protein Atg8 , which localizes to autophagosomes in yeast , flies , and mammals [34] , we assayed its quality as a marker of autophagy in dissected larval muscles . GFP–Atg8 expressed with Dmef2–Gal4 had no effect on animal viability or on gross muscle morphology ( unpublished data ) , and larvae fed on a standard diet ( see Text S1 ) showed very few GFP–Atg8-labeled vesicle structures ( Figure 1B ) . In contrast , in larvae starved on low nutrient food for 6 h , GFP–Atg8 localized to small punctae surrounding the nuclei and between the myofibrils ( Figure 1C ) , consistent with observations in mice that autophagy increases in most organs under starvation conditions , with muscles showing a particularly clear response [2] . Prolonged treatment with CQ in mammals is associated with the onset of vacuolar myopathy , likely due to a defective autophagy–lysosome system . We treated third instar larvae with the drug to better visualize autophagic flux , and to more closely model the defects observed in AVMs such as CQ myopathy and Pompe and Danon diseases . In larvae treated with CQ and starved on low-nutrient food for 6 h , GFP–Atg8-labeled vesicles were much larger and more numerous than those in the nontreated muscle , but were similarly distributed around the nucleus and between myofibrils ( Figure 1D ) . One of the effects of CQ treatment in mammalian cells , and also of Pompe disease , is a defect in the fusion between autophagosomes and lysosomes . This causes a functional defect in autophagy and an accumulation of autophagosomes and lysosomes . To determine whether this was the case in larvae treated with CQ , we coexpressed GFP–Atg8 and the lysosomal membrane marker , HRP–Lamp1 ( Figure 1E–H ) . In well-fed larvae , neither GFP–Atg8 nor HRP–Lamp1 localized to vesicles ( Figure 1E ) . Starvation on low-nutrient food for 6 h induced the formation of small vesicles , some of which were labeled with both GFP–Atg8 and HRP–Lamp1 , indicating that these are likely autolysosomes ( Figure 1F , H ) . However , in larvae treated with CQ and starved , despite the accumulation of large and numerous GFP-labeled vesicles , we detected few vesicles that were co-labeled with HRP–Lamp1 , indicating that CQ treatment effectively blocked fusion of autophagosomes and lysosomes ( Figure 1G–H ) . Next , we tested whether the formation of the GFP–Atg8 punctae observed in both untreated and CQ-treated larvae were dependent on a functional autophagy pathway . This is especially important given that Atg8 tends to be incorporated into intracellular protein aggregates , independent of autophagy . The association with aggregates includes endogenous Atg8 as well as ectopically expressed Atg8–GFP fusion protein . Thus , an Atg8 or Atg8–GFP positive punctae can represent either an aggregate or a bone fide autophagosome . Consistent with the latter interpretation , knockdown of Atg1 was able to completely suppress the formation of GFP–Atg8 punctae in the muscles of both untreated ( Figure 1I ) and CQ-treated ( Figure 1J ) larvae starved on low-nutrient food for 6 h . Knockdown efficiency of the Atg1 RNAi was confirmed by RT-PCR ( Figure S3 ) . Similarly , animals bearing a null allele of Atg1 were likewise unable to form GFP–Atg8 punctae ( Figure 1K–L ) . We quantitated the suppression effect in the CQ-treated animals by measuring the total autophagic area per muscle cell , and tested 10 of the conserved core autophagy pathway genes , which all significantly inhibited the formation of GFP–Atg8 punctae ( Figure 1M , Figure S1 ) . The accumulation of vesicles in muscles of CQ-treated larvae was strikingly similar to the phenotype observed in mammalian AVMs . We therefore set out to determine whether the larvae exhibited symptoms of myopathy . Electron microscopy ( EM ) on sectioned muscles from third instar starved larvae revealed that treatment with CQ did indeed cause disruption of the sarcomere structure compared with an untreated control ( Figure 1N–O ) . This appears to occur through displacement of the sarcomere by enlarged vesicles in the intermyofibril spaces , similar to what has been reported for cases of CQ myopathy and Pompe disease in humans [35] , [36] . To determine whether CQ treatment disrupted muscle function , we performed two tests of larval locomotion , the larval crawling assay ( Figure 1P ) and the larval righting assay ( Figure 1Q ) . In both tests , CQ-treated larvae performed significantly worse than wild-type controls , but only in starved animals , suggesting that the accumulation of vesicles upon starvation may be responsible for a decline in muscle function . In mammals glycogen is synthesized and stored in the muscle and liver . We analyzed glycogen storage in larvae using both the histological periodic acid-Schiff ( PAS ) stain ( Figure 2A ) as well as a monoclonal glycogen antibody ( Figure 2B ) . Consistent with previous reports , we detected abundant glycogen stores in the third instar larval muscles , but not in the larval fat body , the tissue most closely analogous to the vertebrate liver and adipose tissue [37] . In addition , larvae treated with CQ and starved on low-nutrient food for 6 h showed a high degree of colocalization between GFP–Atg8 and glycogen ( Figure 2C–E ) . To ensure that the observed colocalization was indeed glycogen autophagy , we performed EM on sectioned muscles . Figure 2F–G demonstrates the massive build-up of vesicles in the starved and CQ-treated muscle . Consistent with results obtained by confocal microscopy , the vesicles accumulated near the nucleus and between the filaments of the myofibrils . Many of the vesicles were double-membraned , containing electron-dense glycogen granules . The same structures were also observed in non-CQ-treated muscles ( Figure 2H ) , indicating that glycogen autophagy occurs normally in muscles , but with less frequency . Interestingly , although the larval skeletal muscle is filled with mitochondria ( Figure 2F ) , we never observed any vesicles containing these organelles , nor did we ever observe colocalization between GFP–Atg8 and mitochondrial markers in the muscle ( Figure S2 ) . This is in contrast with several reports of autophagic vesicles containing mitochondria in other D . melanogaster tissues [38]–[40] . Thus , the larval muscle is the major site of glycogen storage in the larva , and muscle glycogen is the primary substrate of autophagic degradation . Although the degradation of glycogen by the lysosome was discovered in the 1960s , little is known about its regulation [7] , [24] . In particular , it is not clear whether the induction of autophagy in the muscle and the localization of glycogen in the autophagosomes are subject to regulation by nutrient availability and the Tor pathway . Thus , we performed a time course of glycogen autophagy at 0–8 h of starvation in CQ-treated larvae ( Figure 3A–D ) . Animals were first fed for 18 h on high-nutrient food +CQ , then transferred to low-nutrient starvation food +CQ , and then dissected and stained following each time point . At 0 h of starvation , there was abundant glycogen in the muscle but few GFP–Atg8 punctae , indicating that the rich diet was able to completely suppress autophagy even in the presence of CQ ( Figure 3A ) . At 2–3 h of starvation , autophagosomes began to appear in the muscle , although glycogen remained detectable at a high level ( Figure 3B ) . By 6–8 h much of the glycogen had been degraded , and what remained was now mostly localized to the GFP–Atg8 vesicles ( Figure 3C–D ) . Altogether , these data indicate that muscle glycogen stores are depleted by starvation and that the induction of autophagy and the localization of glycogen within the autophagosomes are regulated by nutrient intake . To more accurately quantitate these effects , we performed a starvation time course experiment on larvae with or without CQ treatment ( Figure 3E ) . Following starvation we collected the carcasses , and measured the glycogen content by enzymatic assay ( see Materials and Methods ) . Glycogen levels diminished over time in both untreated and CQ-treated larvae . However , the latter group showed a significantly reduced rate of glycogen loss , and heightened levels of glycogen persisted even after 24 h of starvation . This may represent glycogen that remains trapped , undegraded , in the autophagosomes and lysosomes of CQ-treated larvae . The Tor kinase pathway links cellular nutritional status to metabolism , growth , and autophagy [41] . Tor activity is inhibited by the Tsc1/Tsc2 complex , which in turn inhibits the small G-protein Rheb , and GTP-bound Rheb binds to and activates Tor [42] . As TOR signaling represses the formation of autophagosomes by inhibition of Atg1 , a function conserved from yeast to mammals [43] , we tested whether the induction of autophagy in muscle is subject to TOR regulation by overexpressing Rheb with the Dmef2–Gal4 driver . Strikingly , the localization of GFP–Atg8 to autophagosomes in starved/CQ-treated muscles was completely blocked by Rheb overexpression compared to controls ( Figure 3F–G ) . Similar to Rheb overexpression , knockdown of Tsc1 and gigas ( Tsc2 ) dramatically inhibited autophagy ( Figure 3H–I ) . Therefore , glycogen autophagy in the larval muscle is linked to nutrient levels via the TOR pathway . One of the critical unanswered questions related to glycogen and autophagy is how the lysosomal degradation of glycogen relates to the enzymatic degradation of glycogen via the action of glycogen phosphorylase . D . melanogaster has a single gene encoding glycogen phosphorylase , GlyP , which has a high degree of sequence homology to the mammalian enzymes . To test whether GlyP is required for glycogen autophagy , RNAi targeting GlyP was expressed along with GFP–Atg8 using Dmef2–Gal4 . This reduced the GlyP expression level in the muscle by more than 90% ( Figure S3 ) . Third instar larvae starved on low-nutrient food for 6 h and treated with CQ exhibited the same colocalization of GFP–Atg8 and glycogen ( Figure 4A–B ) as was observed in control Dmef2–Gal4 , UAS–GFP–Atg8 muscles ( Figure 2C–D ) . Next , we assayed the ability of muscles deficient in GlyP to break down glycogen . Prior to dissection , larvae were fed for 24 h on high-nutrient food . Muscles from GlyP knockdown larvae given this diet exhibited large deposits of glycogen throughout the skeletal muscle cells ( Figure 4C ) . Following the rich food diet , larvae were transferred to low-nutrient starvation food for 24 h prior to dissection . Surprisingly , after the starvation period , glycogen was almost completely undetectable in muscles from the GlyP knockdown larvae ( Figure 4D ) . Likewise , knockdown of Atg1 had no effect on the ability of muscle cells to break down glycogen in this context ( Figure 4E ) , suggesting that during starvation both glycogenolysis and autophagy are sufficient to break down glycogen such that neither is absolutely required . To test this hypothesis we knocked down both GlyP and Atg1 simultaneously . Despite 24 hrs of starvation , these larvae maintained high levels of glycogen in their muscles , indicating that glycogen breakdown requires either a functioning autophagy or glycogenolysis system ( Figure 4F ) . In order to quantitate these effects , we performed a starvation time course experiment on larvae expressing RNAs targeting white , Atg1 , GlyP , or Atg1+GlyP ( Figure 4G ) . Consistent with the immunofluorescence results above , we found that after 24 h starvation , only Atg1+GlyP RNAi significantly inhibited glycogen degradation . However , at earlier time points ( 4–12 h starvation ) , individual targeting of Atg1 or GlyP also reduced glycogen degradation , suggesting that each contributes to the maximal rate of degradation during this period . The polymerization of glucose molecules into a glycogen chain is catalyzed by glycogen synthase , the rate-limiting enzyme of glycogenesis . D . melanogaster has a single glycogen synthase ortholog ( CG6904 ) , which we refer to as GlyS . Consistent with its proposed role in glycogen synthesis , muscles expressing RNAi targeting GlyS showed a dramatic decrease in PAS staining and antiglycogen immunostaining compared to controls ( Figure 5A–D ) . We tested four UAS-GlyS RNAi constructs and each caused over 60% gene knockdown in the larval muscle when expressed by Dmef2-Gal4 ( Figure S3 ) . To determine whether GlyS levels affected autophagosome formation in the muscle irrespective of whether the vesicles contain glycogen or not , we examined GFP–Atg8 localization in starved and CQ-treated control versus GlyS knockdown muscles . Strikingly , we observed dramatically reduced GFP–Atg8 vesicle localization in the latter ( Figure 5E–H ) . To analyze this more closely , we quantitated the autophagic area , vesicle number , and vesicle size in CQ-treated control versus GlyS knockdown muscles , and found that almost all of the effects of GlyS knockdown are due to reduced vesicle size , not number . Thus GlyS is required for the formation of the bloated autophagic vesicles formed during CQ treatment . Next , we tested whether the effect of GlyS on autophagy was limited to muscle cells or extended to the D . melanogaster liver analog , the fat body . Using PAS staining , we have shown that the muscle is the major site of glycogen storage in the larva ( Figure 2A–B ) , however the fat body does contain some relatively low level of glycogen ( Figure 2A–B , Figure 5A ) . We examined the expression of the GlyS gene using a transposon insertion ( MI01490 ) that directs GFP expression under control of the endogenous GlyS regulatory elements . MI01490 larvae had strong GFP expression in the third instar skeletal muscle ( Figure 5L ) , but undetectable levels in the same stage fat body ( Figure 5M ) , consistent with the much higher glycogen levels in the muscle . Knockdown of GlyS using the fat-body–specific Gal4 driver , Cg–Gal4 , caused no appreciable effect on the accumulation of GFP–Atg8-labeled autophagosomes ( Figure 5N–O ) of larvae treated with CQ and starved on low-nutrient food for 6 h . Thus , the effect of GlyS on autophagy is tissue specific and not a general property of all cells . Given the importance of GlyS to autophagosome formation , we wondered whether GlyS levels might ameliorate the myopathy observed in larvae treated with CQ ( Figure 1N–O ) . EM of Dmef2–Gal4/UAS–GlyS RNAi larval skeletal muscle , starved and treated with CQ ( Figure 5P ) , showed a much improved sarcomere structure compared to control larvae ( Figure 1N ) , with much less distortion of the intermyofibrillar spaces . We also examined the effect of GlyS knockdown and Atg1 knockdown on larval locomotion in the crawling assay ( Figure 5Q ) . Larvae were fed , starved 3 h , or starved 6 h , with or without CQ . In fed larvae there was little difference between the white , GlyS , or Atg1 knockdown , irrespective of CQ treatment . At 3 h starvation , both GlyS and Atg1 knockdown significantly improved the crawling time of CQ-treated larvae . This remained true at 6 h of starvation , however the effect was less pronounced . Indeed , Atg1 knockdown , and especially GlyS knockdown , began to negatively affect locomotor function at 6 h starvation even without CQ treatment . In the case of GlyS , this may be due to its essential function in making glycogen rather than a direct effect on autophagosome formation . Interestingly , the human glycogen synthase muscle isoform was identified by mass spectroscopy as a potential interactor of GABARAPL1 , one of the human orthologs of Atg8 [44] , suggesting a possible mechanistic link between the autophagy machinery and glycogen . By binding ATG8 family members , some proteins can act as receptors that link their cargo to the nascent autophagosome membrane via an LC3-interacting region ( LIR ) with a consensus sequence of W/F-X-X-L/I/V , preceded by acidic residues . [45] . We identified three putative LIR motifs conserved between D . melanogaster GlyS and its mammalian orthologs: VAHFHE ( residues 187–192 ) , EFQNL ( residues 303–307 ) , and DWRTL ( residues 608–612 ) . We focused on the last of these as it contains both tryptophan and leucine , the canonical residues at their respective positions . To determine whether GlyS and Atg8 can interact in the larval muscle , we therefore generated a wild-type GlyS overexpression construct as well as overexpression constructs with a mutation at the critical tryptophan of the LIR ( W609A ) . Additionally , to analyze whether the activation state of GlyS might be important for its role in autophagy , we generated additional constructs with mutations in the G-6-P binding region ( R593A ) and at the first GSK3B phosphorylation site ( S651A ) ( Figure 6A ) [46] . These UAS–Venus-tagged forms of D . melanogaster GlyS and GlyS mutants were expressed specifically in muscle with Dmef2–Gal4/UAS–Flag–Atg8 . UAS–Venus–GlyS ( WT or mutants ) ;Dmef2–Gal4/UAS–Flag–Atg8 larvae were fed on high-nutrient food or starved on low-nutrient food for 6 h and then lysed and immunoprecipitated with anti-GFP nanobodies . Flag–Atg8 did not Co-IP with Venus–GlyS in the fed animals , but starvation consistently caused the proteins to Co-IP ( Figure 6B ) . The S651A mutant GlyS , which is predicted to be resistant to suppression by GSK3B phosphorylation [47] , [48] , was likewise able to interact with Flag–Atg8 under starvation conditions ( Figure 6B ) . Neither the W609A mutant nor R593A mutant were able to Co-IP Flag–Atg8 in either nutritional state ( Figure 6B ) . We next overexpressed the tagged forms of GlyS in the larval muscle , and assayed for their localization with respect to Flag–Atg8 ( Figure 6C–F ) . Consistent with the results of the Co-IP experiments , we observed colocalization of both Venus–GlyS ( Figure 6C ) and Venus–GlyS ( S651A ) ( Figure 6E ) with Flag–Atg8 in muscles from animals starved and treated with CQ . In contrast , Venus–GlyS ( R593A ) ( Figure 6D ) and Venus–GlyS ( W609A ) ( Figure 6F ) were found throughout the cytoplasm and did not colocalize with autophagosomes in muscles from starved and CQ-treated animals . Taken together these results indicate that GlyS and Atg8 can interact in D . melanogaster muscles , and that this interaction is regulated by nutritional status . Furthermore , the failure of the W609A mutant to interact or colocalize with Atg8 raises the possibility that the GlyS–Atg8 interaction might occur directly via the GlyS LIR .
Using the muscle-specific Dmef2–Gal4 driver and GFP–Atg8 to label autophagosomes , we found that simply adding CQ to the food induced a dramatic increase in the size of autophagosomes , causing a dramatic distortion of the sarcomere as enlarged autophagosomes filled the intermyofibrillar spaces , causing them to bulge ( Figure 1 ) . This phenotype is strikingly similar to published reports of CQ-induced myopathy and Pompe disease in humans [35] , [36] . Second , we assayed the locomotor function of treated larvae and found that CQ dramatically reduced the animals' crawling ability ( Figure 1O–P ) . We cannot rule out that the effects of the drug on the nervous system could have played a role in this phenotype . However , the fact that we were later able to suppress the locomotor defects through muscle-specific genetic knockdowns indicates that at least some of this phenotype was due to the myopathy . Using our CQ-induced myopathy model we set out to carefully examine the substrate of the accumulated autophagosomes . We thus identified glycogen as a major substrate of autophagy in the larval muscle by immunofluorescence and electron microscopy in both CQ-treated and untreated larvae ( Figure 2 ) . To further analyze the nature of the cargo during CQ-induced autophagy , we tested several other potential substrates for their presence in CQ-induced autophagosomes ( Figure S2 ) . We never detected the presence of mitochondria in the autophagosomes , indicating that autophagy was , to some extent , selective , as the cytoplasm of the larval muscle is rich in mitochondria . Further , we never observed autophagosomes containing the sarcomeric protein filamin , one of the previously reported substrates of autophagy in D . melanogaster adult flight muscle [49] . The flight muscle , a highly oxidative type of muscle , has also been used to model the function of the autophagy pathway during aging , where it seems to target ubiquitylated protein aggregates for degradation [50] . We found that a small number of autophagosomes stained positive for polyubiquitin in the larval muscle , but much fewer than those containing glycogen . Ubiquitin labeling is therefore unlikely to play a major role in the targeting of glycogen to the autophagosome . Taken together , these observations highlight the fundamental difference between autophagy in the highly glycolytic larval muscle and the oxidative flight muscle . This phenomenon extends to mammals , where large accumulations of autophagosomes are seen in glycolytic type II muscle fibers , but not in oxidative type I fibers , in the mouse model of Pompe disease [51] . Our data raise the possibility that differences in the autophagic substrate might underlie this phenotype . The only previous analyses of the Atg genes and glycogen autophagy was in the mouse Pompe disease model , and was limited to mutations in two genes , Atg7 and Atg5 , which surprisingly had different effects on the amount of lysosmal glycogen [12] , [30] , [31] . Using transgenic RNAi lines , we were able to target components from each of the major Atg protein complexes ( Figure 1I–K ) [27] , [28] , [32] . Atg1 , the D . melanogaster ortholog of mammalian Ulk1/2 kinase , and CG1347/Atg17 , the ortholog of the Ulk-interacting protein RB1CC1/FIP200 , function together and form a complex that is essential for autophagosome formation . The Atg12 complex ( Atg5 , Atg12 , Atg16 ) localizes to the phagophore and is important for vesicle elongation . The E2-like enzyme , Atg3 , is required for the conjugation of Atg8 to phosphatidylethanolamine ( PE ) . Atg6 is required for the induction of autophagy as part of the class III phosphatidylinositol 3-kinase complex . Cycling of Atg9 between the PAS and peripheral sites is essential for autophagosome formation , and depends on the Atg9 interacting proteins , Atg2 and Atg18 . Knockdown of any of these genes completely blocked autophagy in the D . melanogaster muscle , even in larvae starved and treated with CQ , indicating that the core conserved autophagy machinery is absolutely required for glycogen autophagy . One of the critical unanswered questions related to glycogen and autophagy is how the lysosomal degradation of glycogen relates to the enzymatic degradation of glycogen via glycogenolysis . In the latter , glycogen phosphorylase catalyzes the rate-limiting cleavage of glucose monomers from the end of a glycogen branch . Mutations in the muscle isoform of mammalian glycogen phosphorylase ( PYGM ) cause glycogen storage disease type V ( also known as McArdle's Disease ) , while mutations in the liver isoform ( PYGL ) cause glycogen storage disease type VI ( also known as Hers' disease ) [52] , [53] . By knocking down the D . melanogaster glycogen phosphorylase gene , GlyP , and Atg1 , we showed that neither glycogenolysis nor autophagy were required for glycogen breakdown over the course of 24 h of starvation ( Figure 4 ) . Muscles deficient in both systems , however , were unable to degrade glycogen , indicating that glycogenolysis and autophagy are the only two routes of glycogen degradation available to the muscles , and that transport of glycogen to the lysosome for degradation requires a functioning autophagy system . When we more closely examined the effects of individually knocking down Atg1 or GlyP , we found that during the first 12 h of starvation the mutant muscles maintained higher levels of glycogen than wild-type controls . A similar effect was also observed in CQ-treated muscles , which have a functional block in autophagy ( Figure 3 ) . CQ treatment consistently caused an increase in glycogen levels over controls ( Figure 3E ) . This effect was observed even after 24 h of starvation , which we suspect is due to the persistence of large numbers of autophagosomes filled with glycogen , protected from both glycogenolysis and the lysosome . We conclude that although autophagy and enzymatic glycogen breakdown can compensate for each other over the long term , in the first 12 h after starvation both systems are required for the maximum efficiency of glycogen breakdown . Although these results suggest that glycogen autophagy in the D . melanogaster muscle targets the same stores of glycogen as GlyP in response to starvation , it is possible that in some cases , autophagy specifically targets mis-branched glycogen . Lending support to this idea , it was recently shown in mouse models of Lafora disease ( progressive myoclonus epilepsy ) that defective autophagy accompanied the formation of Lafora bodies , a poorly branched , excessively phosphorylated form of insoluble glycogen [54]–[57] . This finding could have important implications for treatment of Lafora disease as well as for Andersen disease ( glycogen branching enzyme deficiency or GSD type IV ) and Tarui disease ( GSD type VII ) , which are also associated with the accumulation of polyglucosan aggregates . The recent proteomic analysis of the autophagy interaction network in human cells by Behrends et al . ( 2010 ) [44] identified the muscle form of glycogen synthase as an Atg8-binding protein . This raises the possibility that GlyS could itself act as an adaptor between glycogen and the autophagy machinery . Consistent with this , we found that tagged forms of D . melanogaster GlyS and Atg8 colocalized in vivo in the muscle during starvation and CQ-induced autophagy ( Figure 6 ) . Furthermore , co-immunoprecipitation experiments indicated that the two proteins only form a complex in starved muscles ( Figure 6B ) . As the binding of Gys1 to Atg8 is dependent on the nutritional state of the animal , it is possible that posttranslational modifications such as the inhibiting phosphorylation of GSK3 or the activating binding of Glucose-6-phosphate might regulate this interaction . Consistent with this view , we found that mutation of R593A inhibited the interaction with Atg8 ( Figure 6B , D ) . In yeast the analogous mutation ( R581A ) generates an enzyme with a lower level of activity than wild type [58] , [59] . Along with the fact that the S651A mutation failed to disrupt the GlyS–Atg8 interaction , this suggests that active GlyS is better able to interact with the autophagosome . Unfortunately , as GlyS is required for the synthesis of glycogen , we could not test its function as a cargo receptor by simple knockdown of the gene . We were , however , able to analyze the ability of the W609A mutant to interact with and localize with Atg8 . This mutation disrupts one of the putative LIR motifs conserved between D . melanogaster GlyS and its mammalian orthologs . We found that unlike WT GlyS , W609A mutants failed to interact with Atg8 upon starvation , supporting the notion that the DWRTL sequence is a bone fide LIR motif . In yeast glycogen synthase , the residue corresponding to W609 lies in a loop region between two alpha helices , facing inward toward the center of the tetrameric protein [59] . This is a relatively highly ordered region of the protein , such that mutation of W609 could disrupt the function of the protein , inhibiting its activity . We have not ruled out this possibility , but we note that there were no obvious differences in antiglycogen immunostains from WT GlyS or W609A muscles ( Figure S4 ) , suggesting that glycogen synthesis was not impaired by overexpression of the mutant . Nonetheless , we found that GlyS has a general role in promoting autophagy in muscles from CQ-treated animals ( Figure 5 ) . The effect of GlyS knockdown on autophagosome size could simply be due to the absence of glycogen substrate in these muscles . Alternatively , this finding might indicate that physical interaction between the autophagic machinery and GlyS/glycogen is required for the formation of enlarged autophagosomes , or that the absence of GlyS alters a signaling pathway leading to suppression of autophagosome growth . Recently , it was reported that the AMPK complex , which is known to promote autophagy and phosphorylate GlyS , binds to and is activated by glycogen [60] , [61] . A change in AMPK activity and/or localization might therefore play a role in the decreased autophagosome size observed in the GlyS knockdown muscle . Interestingly , we observed that much of the Venus–GlyS protein appears to localize to the surface of the Atg8 punctae induced by CQ treatment ( Figure 6C , E ) . It is possible that GlyS residing on the outer surface of the autolysosome could itself sense glucose released via lysosomal glycogen degradation . This would provide a means for feedback from glycogen autophagy to the metabolic and signaling functions of GlyS . In conclusion , this study represents an advance in our understanding of the role of autophagy in glycogen metabolism in skeletal muscle . The relevance of these processes to animal health , and our investigation of the interaction between GlyS and autophagy , suggests that the D . melanogaster model can identify important participants in glycogen autophagy and related myopathies .
Details on fly strains can be found in Text S1 . Unless otherwise noted , fly crosses and larvae were maintained in vials containing “standard” cornmeal/soy flour/yeast fly food ( see Text S1 for further details ) . For starvation experiments third instar larvae were individually selected and no more than 20 per experiment were transferred to “low-nutrient food” composed of 0 . 3× standard food . Likewise , for the rich-food diet , no more than 20 larvae were transferred to “high-nutrient food” composed of standard food supplemented with 100 g/L sucrose and 50 g/L yeast . For each genotype , at least four larvae from at least two independent vials were analyzed . For drug treatments , chloroquine diphosphate salt ( Sigma ) was added to the food at 2 . 5 mg/ml . Assays were based on previously published methods [62] . For the crawling assay , larvae were positioned at one end of a furrow on the surface of a sylgard plate , with a yeast ball at the far end . The time to crawl 3 cm was measured three times , with the final successful trial used as data for analysis . Trials in which the larva crawled over the edge of the lane were considered unsuccessful , and the larvae were reset at the starting point . For the righting assay , a larva was placed on a sylgard plate , allowed to acclimate for 10 s , then turned upside down . The time it took for the animal to turn back onto its ventral surface was recorded . For both locomotor assays , 10 individual larvae were tested , and p values calculated using Student's t test . For whole-mount immunostaining of fly tissues , third instar larval body wall muscles were dissected according to [63] or third instar fat body were dissected and fixed for 15 min in PBS with 4% formaldehyde . After washing in PBT , samples were incubated overnight with appropriate primary and secondary antibodies . Image analysis was done with ImageJ . Transmission electron microscopy was performed using a technique similar to [64] . See Text S1 for further details and list of antibodies used . Third instar larvae were fed on standard food , then immersed in 4% formaldehyde in PBS for 1 hr at room temperature , pierced on their posterier end to allow fixative to permeate the tissues , and transferred to 4°C overnight . The tissue was dehydrated in an ethanol series followed by xylene , then embedded in paraplast , and sectioned at 5 µm . Samples were deparrafinized and rehydrated , then stained with PAS , and counterstained with Acidified Harris Hematoxylin following the manufacturer's protocol ( Polysciences ) . Images were collected with an Axiophot 2 compound microscope . Larvae were put on a rich-food diet for 24 h prior to the experiment , then switched to the starvation diet , with or without CQ , for the indicated time . We adapted a protocol used previously for measuring glycogen content in D . melanogaster larvae [65] . For each measurement 20 larvae were homogenized on ice in 100 µl PBS , then heat treated at 70°C for 5 min . Homogenate was then diluted 1∶10 in PBS , centrifuged at 12 , 000 rpm for 3 min , the supernatant was collected , and the glucose analyzed by absorbance at 340 nm using the Glucose ( HK ) Assay Kit ( Sigma ) . To calculate glycogen levels , untreated samples were compared to samples supplemented with 1 µ/mL amyloglucosidase ( Sigma ) , which degrades the glycogen to glucose . Glycogen levels were then normalized to protein levels in the corresponding homogenate , calculated by Bradford assay , and the ratios for each genotype or treatment were compared using Student's t test . Glycogen Synthase cDNA LD46952 was cloned into Gateway entry vectors according to the pENTR Directional TOPO Cloning system ( Invitrogen ) , then cloned into destination vectors derived from the Drosophila Gateway Collection , and obtained from the Drosophila Genomics Resource Center ( pTVW = N-terminal Venus tag under the control of the UASt promoter ) . Point mutations were introduced using the QuikChange II site-directed mutagenesis kit ( Stratagene ) . Atg8a cDNA LD05816 was cloned into pTFW = N-terminal 3xFlag tag under the control of the UASt promoter , as above . Integration into the genome was performed using standard methods . See Text S1 for further details . Prior to dissection larvae were fed in standard food , then eight larvae of each genotype were transferred to new vials containing either standard food or low-nutrient food . Third instar larvae were dissected to obtain a clean carcass with only muscles remaining attached . Dissections were collected in lysis buffer ( 25 mM Tris–HCl [pH 7 . 5] , 150 mM NaCl , 5 mM EDTA , 1% [v/v] NP-40 , 5% [v/v] glycerol , 1× EDTA-free protease and phosphatase inhibitor cocktail [Thermo Scientific] ) . After homogenization , debris was removed by centrifuging once at 1 , 200× g for 5 min and once at 1 , 3000× g for 5 min . Extracts were cleared by incubation with agarose resin ( 50 µl of packed beads per IP; Thermo Scientific ) for 1 h at 4°C , followed by centrifugation at 13 , 000× g for 15 min . Immunocomplexes were formed by incubation for 2 h at 4°C using anti-GFP nanobodies coupled to agarose beads ( 10 µl of packed beads per IP; ChromoTek ) . All washes were in lysis buffer . Western blot was performed using standard protocols . Antibodies used are: mouse anti-GFP ( 1∶1 , 000 Abcam ab1218 ) , mouse anti-Tubulin ( 1∶5 , 000 Sigma-Aldrich T6199 ) , and rabbit anti-Flag ( 1∶2 , 000 Sigma ) .
|
Lysosomes are organelles that work as a disposal system for the cell . It is known that lysosomes can degrade glycogen and that defects in this function trigger the accumulation of vesicles containing glycogen in animals that lead to vacuolar myopathies—diseases that result in muscle weakness . However , it remains unclear how and why glycogen is degraded through this system , and what significance it has for the pathology of such diseases . Here , we addressed these questions by establishing a fruitfly model system to study glycogen autophagy in skeletal muscles . By feeding the flies chloroquine ( CQ ) , we induce a vacuolar myopathy associated with massive accumulation of glycogen-filled vesicles , and assay the role of autophagy and glycogen metabolic enzymes in this process . We show that CQ-induced glycogen autophagy is completely dependent on the core conserved autophagy genes and that this autophagy is triggered by nutrient deprivation in a Tor-dependent manner . Interestingly , while glycogen autophagy and enzymatic glycogen breakdown can compensate for each other , concurrent inhibition of both systems blocks glycogen breakdown . Finally , we show that CQ-induced myopathy can be improved by reduction of either autophagy or glycogen synthesis , the latter possibly due to a direct role of glycogen synthase—the main enzyme involved in converting glucose to glycogen—in regulating autophagy through its interaction with the autophagosome .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
Role of Autophagy in Glycogen Breakdown and Its Relevance to Chloroquine Myopathy
|
Phytopathogens secrete effector proteins to manipulate their hosts for effective colonization . Hemibiotrophic fungi must maintain host viability during initial biotrophic growth and elicit host death for subsequent necrotrophic growth . To identify effectors mediating these opposing processes , we deeply sequenced the transcriptome of Colletotrichum higginsianum infecting Arabidopsis . Most effector genes are host-induced and expressed in consecutive waves associated with pathogenic transitions , indicating distinct effector suites are deployed at each stage . Using fluorescent protein tagging and transmission electron microscopy-immunogold labelling , we found effectors localised to stage-specific compartments at the host-pathogen interface . In particular , we show effectors are focally secreted from appressorial penetration pores before host invasion , revealing new levels of functional complexity for this fungal organ . Furthermore , we demonstrate that antagonistic effectors either induce or suppress plant cell death . Based on these results we conclude that hemibiotrophy in Colletotrichum is orchestrated through the coordinated expression of antagonistic effectors supporting either cell viability or cell death .
To penetrate the cuticle and cell wall of their hosts , most plant pathogenic fungi differentiate specialized infection structures called appressoria . Appressoria have been long-recognized as providing tight adhesion to host surfaces ( Latin appressus , pressed closely against ) [1] . The appressoria of Colletotrichum and Magnaporthe species display a complex physiology and morphology , adapted for efficient host cell entry . Key features are ( a ) a melanized cell wall acting as a semipermeable barrier to osmolytes , ( b ) glycerol accumulation for generating turgor and ( c ) an extracellular matrix to anchor the cell and counter-balance downward mechanical forces applied during penetration [2] . The appressoria of Colletotrichum and Magnaporthe are highly polarized cells with an upper domed region and a basal region containing the penetration pore , from which a needle-like penetration hypha emerges to puncture the epidermal cell wall [3] , [4] . Differentiation of the pore involves deposition of a new wall layer , termed the ‘pore wall overlay’ , which is continuous with the penetration hypha cell wall [5] . Hydrolytic enzymes secreted by penetration hyphae may act synergistically with mechanical pressure during host penetration [2] . However , whether appressoria actively manipulate the attacked cell in preparation for invasion is currently unknown . Host manipulation and re-programming are hallmarks of biotrophic plant pathogens , which depend on living host cells . In addition to overcoming preformed barriers , these pathogens must defeat immune responses elicited by recognition of conserved microbe-associated molecular patterns ( MAMPS , e . g . chitin ) , including local deposition of chemical and physical barriers at pathogen entry sites [6]–[8] . Since MAMPs fulfill important functions in pathogens and cannot be modified or jettisoned without fitness cost , biotrophic pathogens secrete effector proteins as molecular weapons to evade or suppress plant immunity . The evolution of secreted effector proteins by pathogens led plants in turn to evolve resistance proteins that recognize these effectors , thereby providing effector-triggered immunity , often leading to host cell death and pathogen arrest . In turn , pathogens deploy effectors to interfere with these processes , resulting in a molecular arms-race between plant and pathogen in which both opponents try to overcome each others innovations , leaving only temporary winners [9] . Pathogen effectors often carry the marks of this rapid co-evolution , showing extreme sequence diversification . Effectors typically have no similarity to known proteins and have a restricted phylogenetic distribution [10] . Colletotrichum species are notorious plant pathogens , most of which have a ‘hemibiotrophic’ lifestyle that combines an initial , symptomless biotrophic phase with a later necrotrophic phase associated with severe symptoms . In contrast to biotrophs , Colletotrichum species can be cultured axenically and are accessible to genetic manipulation [11] . C . higginsianum has a wide host range , including many cruciferous crops and the model plant Arabidopsis thaliana . Phylogenetically , C . higginsianum belongs to the C . destructivum species group , which is characterized by ‘localized biotrophy’ , where intracellular biotrophic hyphae are restricted to the first-infected epidermal cell [12] . Filamentous necrotrophic hyphae later develop from the bulbous biotrophic hyphae and spread into the surrounding tissue , producing macerated , water-soaked lesions [13] . Little is known about Colletotrichum effectors: Stephenson and co-workers [14] reported CgDN3 , a putative secreted protein of C . gloeosporioides which was implicated in suppressing host resistance responses . Furthermore , C . lindemuthianum and C . higginsianum possess CIH1 [15] , [16] , an effector containing tandem chitin-binding lysin motifs which may function in chitin sequestration and camouflage [10] . We assume that the appressoria , penetration hyphae and biotrophic hyphae of C . higginsianum secrete effector proteins before , during and after penetration to evade host defenses and maintain host viability during the biotrophic phase , and to induce cell death at the switch to necrotrophy . In the present study , we aimed to investigate the role of secreted effector proteins in mediating hemibiotrophy and their delivery at fungal-plant interfaces . Based on deep transcriptome sequencing and computational mining of ESTs from precise infection stages , we derived a large inventory of in planta-expressed effector candidates for this pathogen . Tagged effectors were found to localize to previously undescribed interfacial compartments . In particular , we demonstrate that effectors are focally secreted from appressorial penetration pores before host invasion . Furthermore , we present evidence that the coordinated expression and secretion of antagonistic biotrophy effectors and toxin effectors contribute to fungal virulence and the regulation of hemibiotrophy in C . higginsianum .
As a first step towards the discovery of secreted C . higginsianum effector proteins , we generated ESTs by sequencing the fungal transcriptome associated with different cell types and infection stages . These included plant-penetrating appressoria , mature biotrophic hyphae isolated from Arabidopsis leaves by fluorescence-activated cell sorting and the late necrotrophic phase . Sequencing techniques , strategies used to maximize gene discovery as well as EST assembly statistics are summarized in Text S1 . Biocomputational screening yielded 327 EST contigs predicted to encode solubly secreted , extracellular proteins . We defined C . higginsianum effector candidates ( ChECs ) as secreted proteins lacking homologs outside the genus Colletotrichum or resembling ( presumed ) effectors from other plant pathogenic fungi . Applying these criteria , 198 contigs encoding ChECs were identified , of which 102 were depleted in ESTs derived from the late necrotrophic phase ( Table S1 ) . Thus , these genes appear to be preferentially expressed during infection stages relevant to the establishment and maintenance of biotrophy , namely appressoria and biotrophic hyphae , and we refer to them as biotrophy-associated ChECs hereafter . Most of these were small in size ( average 67 , median 56 amino acids ) and lacked recognizable protein domains . A motif search revealed no motifs were shared by non-paralogous ChECs . Only 30% of the biotrophy-associated ChECs had a detectable homolog in the closely-related species C . graminicola , suggesting most ChECs are higginsianum-specific ‘orphan’ genes . In contrast , among an equal number of similar-sized genes randomly selected from the genome , 59% had C . graminicola homologs , indicating that biotrophy-associated ChEC genes are subject to greater diversification than other genes . Consistent with this , a survey of 21 different Colletotrichum species and isolates by Southern analysis showed that ChEC1 and ChEC2 were strongly conserved within the C . destructivum species group , and ChEC3 was only detectable in C . higginsianum isolates ( Figure S1A ) . ChEC3 and its paralog ChEC3a are similar to the C . gloeosporioides effector CgDN3 [14] , and lack homologs in C . graminicola . ChEC3 , ChEC3a and CgDN3 are small proteins ( 47 to 56 amino acids after signal peptide cleavage ) , and have only 17 residues in common . Despite that , their exon-intron structure and predicted secondary structure are conserved ( Figure S1B ) . Sequencing ChEC3 and ChEC3a loci from 20 different C . higginsianum isolates revealed that ChEC3 is monoallelic and ChEC3a has an additional allele with only one ( nonsynonymous ) nucleotide polymorphism . Thus , both genes show interspecies diversification ( or absence ) but intraspecies conservation . Five other ChECs displayed sequence similarity to effectors previously identified from other fungi . ChEC5 harbours a cerato-platanin domain and shares 79% identical amino acids with M . oryzae MSP1 [17] . Both , ChEC90 and ChEC90a contain LysM domains and are homologous to C . lindemuthianum CIH1 [15] 39% and 50% amino acid identity , respectively ) . ChEC36 shares 40% identical amino acids with Fusarium oxysporum f . sp . lycopersici SECRETED IN XYLEM 6 ( SIX6 ) [18] while ChEC88 resembles the biotrophy-associated secreted protein BAS3 of M . oryzae [19] ( 49% amino acid identity ) . Remarkably , a survey of the top 30 contigs containing the highest numbers of ESTs from plant-penetrating appressoria , revealed that no fewer than 18 ( 60% ) encode secreted proteins , of which 12 were biotrophy-associated ChECs ( Table S2 ) . In addition to ChEC3 and its paralog ChEC3a , these included ChEC4 and ChEC9 , both predicted to contain nuclear localization signals . The functionality of these signals was experimentally verified by transient expression in planta ( Figure S2 ) , raising the possiblity that ChEC4 and ChEC9 are translocated into the host nucleus for transcriptional reprogramming . Interestingly , ChEC7 and ChEC10 had transcripts containing remnants of retrotransposons within their UTRs , which resembled CgT1 , a non-LTR LINE-like element previously identified in C . gloeosporioides [20] and Ccret2 from C . cereale [21] , respectively ( [22]; Table S2 ) . Taken together , secreted proteins , including ChECs , predominate among the most highly expressed genes in appressoria during early host invasion . In addition to ChECs that may support the biotrophic lifestyle , we identified putative secreted toxin effectors , including ChToxB , a homolog of the host-selective toxin ToxB from Pyrenophora tritici-repentis [23] and homologs of Necrosis- and Ethylene-inducing Peptide1-Like Proteins ( referred to as NLPs hereafter ) [24] . The six NLP homologs identified in the C . higginsianum genome show sequence variation in the NLP consensus motif and have contrasting expression profiles and necrosis-inducing activities . For example , ChNLP1 is expressed specifically at the switch to necrotrophy and is a potent cell death inducer when expressed transiently in Nicotiana benthamiana , whereas ChNLP3 is expressed in appressoria before penetration and lacks necrosis-inducing activity ( Figure 1 ) . The sampling of biological materials used for EST generation was designed to maximize the discovery of genes expressed at several biotrophy-relevant stages ( from unpenetrated appressoria through to very mature biotrophic hyphae ) and did not allow dissection of gene expression dynamics associated with developmental transitions ( e . g . pre-/post-invasive growth and the switch from biotrophy to necrotrophy ) . To profile the expression of selected ChECs and putative toxins during infection in more detail , we used qRT-PCR . We sampled RNA from the following developmental stages: unpenetrated appressoria in planta , penetrated appressoria with nascent biotrophic hyphae , the switch from biotrophy to necrotrophy ( Figure S3 ) as well as late necrotrophy . To represent in vitro cell types , dormant spores , saprotrophic mycelium and mature appressoria formed on an artificial , non-penetratable substratum were included . For expression profiling , we prioritized ChECs that resembled previously identified effectors ( see above ) and/or displayed high expression levels in biotrophy-relevant stages as determined by their EST read counts . Expression analysis of 17 selected genes revealed that four successive waves of effector gene expression occur during pathogenesis ( Figure 2 ) : The first wave of ChEC genes is induced in unpenetrated appressoria in planta , exemplified by ChEC7 and ChEC9 . Similarly , ChEC3 , ChEC3a , ChEC4 , ChEC6 and ChEC36 are also induced in unpenetrated appressoria in planta , but their expression continues into the early biotrophic phase ( wave two ) . Of these , ChEC6 had the highest relative expression level of all ChECs tested , suggesting an important role in early pathogenesis . In contrast , ChEC13 , ChEC34 , ChEC51 , ChEC56 , ChEC88 and ChEC89 are specifically induced during penetration and establishment of biotrophic hyphae ( wave three ) . The last wave of effector genes , exemplified by ChNLP1 and ChToxB , is induced only during the switch to necrotrophy , suggesting that their toxic products contribute to terminating the biotrophic phase for subsequent necrotrophic growth . In contrast to the previous examples , ChEC5 and ChEC91 were preferentially induced in saprotrophic mycelium . Thus , nearly all ChECs tested were confirmed to be biotrophy-associated . Moreover , for seven ChEC genes showing induction in unpenetrated appressoria in planta ( ChEC7 , 9 , 3 , 3a , 4 , 6 , 36 ) , pre-formed transcripts were not detectable in dormant spores and only ChEC7 was induced in mature appressoria in vitro , indicating all other genes are truly plant-induced . To localize ChECs during pathogenesis , proteins were expressed in C . higginsianum as C-terminal fusions with fluorescent proteins under control of their native upstream regulatory sequences . At least three independent transformants were analyzed per gene and verified to show the same localization pattern . Live-cell confocal laser scanning microscopy detected fluorescence for all of the seven ChECs tested ( see below ) , confirming the computational ORF predictions . For high-resolution localization of selected protein fusions we also used transmission electron microscopy-immunocytochemistry with an mRFP-specific antibody to label ultrathin sections . When fungal transformants expressing ChEC36:mRFP ( a wave 2 effector ) were inoculated onto Arabidopsis seedlings , we detected a strongly fluorescent spot at the basal penetration pore in 72% of the inspected appressoria ( n = 101 ) ( Figure 3A–D ) . In some cases , the pore was in addition encircled by a weakly fluorescent ring ( Figure 3E , F ) . Among pore-labelled appressoria , 11% showed in addition labelling of discrete intracellular structures in the fungal cytoplasm ( Figure 3E , G ) . Transmission electron microscopy immunogold labelling revealed that these structures resembled vacuolar inclusion bodies ( Figure S4A ) . Biotrophic hyphae showed no labelling ( Figure 3H , I; Figure S4B ) , suggesting ChEC36:mRFP is exclusively secreted before and during penetration . Transmission electron microscopy of in planta appressoria revealed that penetration pores ( ∼200 nm diameter ) are surrounded by an additional wall layer continuous with the penetration hypha wall ( Figure 3J ) , referred to as the ‘pore wall overlay’ hereafter , which contains β-1 , 3-glucan ( Figure S5 ) . Based on serial sectioning of 24 appressoria , immunogold labelling confirmed that ChEC36:mRFP specifically localized to the penetration pore in 13 appressoria ( 54% ) and decorated the pore wall overlay in seven appressoria ( 29% ) ( Figure 3K , Figure S4C , D ) . No labelling was observed on the inner or outer surface of the appressorial wall , suggesting that appressoria secrete ChECs in a highly polarized manner towards the pore . Membrane contrast was low in these samples because they were not fixed with osmium tetroxide and were embedded in acrylic resin in order to provide optimal antigen preservation . Nevertheless , in favourably-orientated sections , the immunogold labelling of pores and pore wall overlays appeared external to the fungal plasma membrane , consistent with ChEC36 being an extracellular protein ( Figure S4E ) . No immunogold labelling was observed in or beneath wild-type appressoria ( n = 11 ) , indicating specific epitope recognition ( Figure S4F ) . All appressoria with labelled pores had not produced a visible penetration hypha , as verified by serial sectioning . Despite that , small pads of host cell wall material were already deposited beneath 77% of inspected unpenetrated appressoria ( n = 35 ) , suggesting host cells respond to the pathogen prior to any visible ingression or structural damage ( Figure 3K; Figure S4C , F; Figure S5H ) . The inner , first-formed layer of these host cell wall deposits did not contain detectable callose but subsequently became encrusted with a callose layer ( Figure S5H ) . Appressoria in situ can be acetone-fixed and completely detached from the plant surface by cellulose acetate stripping , as determined by scanning electron microscopy ( Figure 3L–N ) . This allowed us to dissect whether fluorescent protein-tagged ChECs are released from appressoria into the plant epidermis . When the cellulose acetate-stripped leaf surface was inspected with confocal microscopy , sites of successful penetration were characterized by brightly fluorescent spots from which the fluorescence signal appeared to diffuse laterally a short distance ( ∼2 µm ) , resulting in a small halo of mRFP fluorescence ( Figure 3O ) . Similar to ChEC36:mRFP , 72% ( n = 101 ) of intact appressoria expressing ChEC6:mRFP ( another wave 2 effector ) also showed pore-localized fluorescence , with haloes visible at lower focal planes , consistent with local delivery of ChEC6 into the plant apoplast ( Figure 3P , Q ) . Taken together , it appears that ChECs expressed pre-penetration are focally secreted to , and from , an extremely localized zone of direct contact between host and pathogen , delimited by the appressorial penetration pore . Inoculation of transformants expressing CHEC89:GFP or CHEC89:mRFP ( a wave 3 effector ) onto Arabidopsis seedlings showed fluorescent labelling on the surface of 91% ( n = 89 ) biotrophic hyphae . In an independent quantification of labelled hyphae , 61% ( n = 239 ) showed punctate accumulation of fluorescence in discrete foci randomly scattered over the hyphal surface ( Figure 4A , B; Figure S6A–C ) . Fully-expanded , mature biotrophic hyphae also showed strong surface labelling , which accumulated in hyphal concavities ( Figure 4C , D ) . After retraction of the plant protoplast by plasmolysis , a fluorescent signal was detectable in the enlarged apoplastic space , suggesting that the CHEC89:mRFP fusion protein is freely diffusible and not linked to the fungal cell wall ( Figure 4E , Figure S7 ) . In support of this , we could detect fluorescence in anticlinal plant cell walls near infection sites , especially where two or more neighboring appressoria had invaded the same epidermal cell ( Figure 4B; Figure S6C ) . This suggests the interface between the host plasma membrane and biotrophic hyphae is continuous with the bulk apoplast , allowing limited diffusion away from the penetration site . Spectral scanning confirmed an mRFP-specific fluorescence emission , ruling out local autofluorescence of the plant cell wall . Secondary hyphae emerging from the apices of biotrophic hyphae lacked detectable labelling , indicating CHEC89:mRFP secretion is specific to biotrophic hyphae ( Figure 4F ) . Similar to CHEC89:mRFP , localization to the surface of biotrophic hyphae was also observed for the wave 2 effector ChEC3:mRFP ( 92% , n = 98 ) and the wave 3 effectors CHEC13:mRFP ( 86% , n = 114 ) and CHEC34:mRFP ( 89% , n = 102 ) ( Figure 5A , B; Figure S6D–I ) . However , the localization patterns of these ChECs were not identical: Thus , in independent quantification experiments , many hyphae expressing ChEC34:mRFP ( 75% , n = 140 ) showed an accumulation of fluorescence in discrete foci . In contrast , the proportion of hyphae showing punctate labelling was lower for transformants expressing ChEC3:mRFP ( 13% , n = 117 ) and ChEC13:mRFP ( 13% , n = 109 ) which both displayed a more uniform labelling on the surface of most hyphae . Epidermal cells infected by biotrophic hyphae expressing ChEC3:mRFP also showed weak fluorescence in the apoplastic space enlarged by plasmolysis ( Figure S6I ) . Only CHEC13:mRFP was detectable in ‘pseudo biotrophic hyphae’ formed after penetration of an artificial , penetratable substratum , suggesting the expression of all other CHEC genes tested is plant-induced and not linked to appressorial penetration per se ( Figure S8 ) . In contrast to ChEC3:mRFP and CHEC13:mRFP , CHEC34:mRFP also localized to the plant cell wall . This signal was confined to cell walls adjoining penetration sites but spread longer distances ( >25 µm ) than CHEC89:mRFP ( Figure 5C , D ) . Using transmission electron microscopy to view the biotrophic interface at higher resolution , we found that the host plasma membrane made direct contact with the cell walls of biotrophic hyphae , except in small regions where discrete pads of electron-opaque material protruded from the hyphal surface ( Figure 5E , F ) . To investigate whether these interfacial bodies correspond to the punctate fluorescence observed by confocal microscopy , we used immunogold labelling to detect CHEC34:mRFP . All interfacial bodies we examined were intensely labelled in transformant ( n = 11 ) , but not wild-type biotrophic hyphae ( n = 8 ) ( Figure S9 ) , suggesting they are foci of effector accumulation ( Figure 5G ) . In other hyphae where interfacial bodies were not visible , gold labelling more uniformly decorated the plant-fungal interface ( Figure 5H ) . Neither the fungal cell wall nor plant cytoplasm were labelled in these samples . Taken together , our findings are consistent with Colletotrichum biotrophic hyphae having a role in effector delivery . Targeted mutagenesis of a gene provides unambiguous genetic evidence for its contribution to fungal virulence . However , targeted replacement of pathogen effector genes frequently does not result in reproducible infection phenotypes , possibly due to functional redundancy between effectors [19] , [22] . Thus , assigning virulence-related functions to ChECs remains a challenging task . However , direct expression of ChECs in plant cells allows their biological activity to be investigated in isolation from other fungal effectors . To test whether ChECs can suppress plant cell death , we transiently co-expressed them in N . benthamiana leaves together with cell death-inducing proteins . In brief , agrobacteria containing a vector for ChEC expression were mixed with those for cell death inducer expression and infiltrated into one half of a leaf . A control mixture , in which ChEC-carrying agrobacteria were replaced by those enabling YFP expression , was infiltrated into the other half , allowing pair-wise comparisons in the same leaf ( Figure 6A ) . Infiltration sites co-expressing YFP and ChNLP1 showed severe confluent necrosis six to eight days after infiltration . To quantify cell death suppression activity , we determined the proportion of ChEC-expressing sites showing reduced necrosis ( Figure 6B ) or no reduction in necrosis ( Figure 6C ) . We tested four wave 2 effectors and three wave 3 effectors , including ChEC3 , ChEC3a and ChEC5 due to their similarity to effectors required for pathogenicity in other fungi . Co-expression of ChEC3 , ChEC3a , ChEC5 , ChEC6 and CHEC34 without their signal peptides reduced necrosis in 70 to 90% of the inspected infiltration site pairs ( Figure 6D ) . In contrast , co-expression of a C . higginsianum chitinase without its signal peptide as negative control protein [16] resulted in significantly fewer sites showing reduced necrosis ( P<0 . 02 , Student's t-test ) . Western blot analysis using epitope-tagged ChNLP1 confirmed that co-expression of ChECs has no impact on ChNLP protein level per se ( Figure S10 ) . Thus , the observed necrosis reduction reflects ChEC activity rather than failure of ChNLP1 expression . ChEC13 , ChEC36 and ChEC89 lacked statistically significant cell death-suppressing activity . To evaluate whether the presence of the fungal signal peptide affects activity , we re-tested three of the most active suppressors of ChNLP1-induced necrosis including their signal peptides . Co-expression of ChEC3 , ChEC3a or ChEC5 with their signal peptides resulted in significantly fewer sites showing reduced necrosis relative to the chitinase control with signal peptide ( P<0 . 01 ) . Thus , data obtained from ChEC constructs with and without signal peptide were not significantly different ( P>0 . 3 , Figure 6D ) . To evaluate the specificity of the cell death suppressing activity , we re-tested the same three ChECs for their ability to interfere with necrosis induced by Phytophthora infestans INF1 , an elicitor requiring different plant signalling components [25] . ChEC3 , ChEC3a or ChEC5 failed to suppress INF1-induced necrosis , whereas co-expression of Avr3aKI , a well-described suppressor of INF1-induced cell death [26] , resulted in significant necrosis reduction in our assay ( Figure 6D ) . Thus , ChEC3 , ChEC3a and ChEC5 specifically interfere with ChNLP1-induced necrosis , but not INF1-induced necrosis . Preliminary experiments showed that challenge of ChEC-expressing sites with the adapted tobacco pathogens C . orbiculare and C . destructivum did not result in enhanced fungal growth , possibly as a result of immune responses triggered by the agroinfiltration procedure . To further investigate the virulence function of ChECs , we tested the ability of two suppressors and two non-suppressors of ChNLP1-induced cell death to promote the multiplication of plant pathogenic bacteria . For this we used Pseudomonas syringae pv . tomato carrying the ‘effector detector vector’ to deliver ChECs via the bacterial type III secretion system into the cytoplasm of Arabidopsis cells [27] . Out of the four proteins tested , ChEC3 and CHEC89 , but not ChEC6 and ChEC36 , significantly enhanced bacterial virulence compared to a YFP control , presumably by suppressing host defense responses ( Figure 6E ) .
The role of secreted effector proteins during infection by hemibiotrophic plant pathogens is poorly understood . The present study provides a comprehensive inventory of in planta-expressed effector candidates for the hemibiotrophic fungus Colletotrichum higginsianum . We found most biotrophy-associated ChEC genes were dramatically upregulated exclusively in planta , which suggests these proteins play an important role during host infection . Consecutive waves of effector gene expression were associated with key developmental transitions , indicating that distinct suites of effectors are deployed at each infection stage . For ChECs upregulated pre-invasion ( waves 1 and 2 ) , we demonstrated focal secretion to and from appressorial penetration pores . Appressoria have been long-recognized as structures enabling turgor-driven penetration of host surface barriers [2] . We now add another level of functional complexity to this highly elaborated infection structure that has not been reported previously , namely the local release of effector proteins at a nanoscale interface formed between host and pathogen , defined by the basal penetration pore . In contrast to secreted proteins that are targeted to the inner appressorial wall where they may play structural roles [28] , ChECs were specifically secreted to the penetration pore , reflecting the strong basipetal polarity associated with switch from radial ( isometric ) expansion to focused tip growth of the emerging penetration hypha [4] . ChEC6:mRFP and ChEC36:mRFP fusions were expressed and targeted to this pore before any ingression into the host cell had occurred . Given that in vitro and in planta appressoria are morphologically indistinguishable [29] and that these effectors were not expressed by in vitro appressoria , this suggests that host-derived signals , rather than developmental cues , induce ChEC expression . Moreover , these signals must be sensed before penetration hyphae emerge , presumably via the wall-less pore region . Unlike all other ChECs expressed as mRFP fusions , only CHEC13:mRFP was detectable during penetration of cellophane , suggesting CHEC13 expression is developmentally linked to penetration hypha formation . Similarly , expression of the M . oryzae avirulence gene ACE1 , involved in the synthesis of a secondary metabolite effector , is linked to the emergence of penetration hyphae [30] . Why are ChECs expressed and secreted at such an early stage of pathogenesis ? Our ultrastructural analyses revealed host-derived cell wall material is deposited beneath appressoria before any visible penetration or structural damage to the cuticle/cell wall , indicating Arabidopsis perceives and responds to C . higginsianum appressoria before fungal entry . We propose that the fungus deploys early-expressed effectors to counteract pre-invasion host defenses and to prepare the host cell for colonization . In support of this idea , it was previously demonstrated for C . lindemuthianum that appressorium maturation , but not penetration , was sufficient to induce bean defense responses [31] . Similarly , C . lindemuthianum mutants lacking the transcription factor Ste12 required for appressorial penetration induced a hypersensitive response and defense gene expression in non-host plants , again indicating that pathogen perception is independent of penetration [32] . Arabidopsis resistance to C . higginsianum conferred by the resistance genes RRS1 and RPS4 acts very early , before formation of biotrophic hyphae [33] . This suggests wave one or two effectors ( or their activities ) may be recognized by these resistance proteins . Shimada and co-workers [34] proposed that C . higginsianum is able to suppress callose wall depositions in attacked Arabidopsis cells . Consistent with this , we barely detected callose within the first-formed host cell wall deposits . However , these initial deposits were subsequently encrusted with a layer of β-1 , 3 glucan . Intriguingly , we also observed specific labelling of the host cuticle-cell wall interface directly beneath appressoria ( Figure S5 G , H ) . Further work is required to determine whether this β-1 , 3 glucan is of plant or fungal origin . The observed accumulation of wave three effectors in interfacial bodies on the surface of biotrophic hyphae is reminiscent of the M . oryzae biotrophic interfacial complex ( BIC ) , in which fluorescent protein-tagged effectors also accumulate [19] . However , when biotrophic hyphae of M . oryzae occupy the first invaded epidermal cell , only a single BIC of approx . 1–2 µm diameter is present , whereas C . higginsinum hyphae are decorated with numerous interfacial bodies of ∼500 nm diameter . Khang and co-workers [35] reported that BIC-localization was correlated with effector translocation into the rice cytoplasm . However , we could not detect uptake of any ChEC:mRFP fusion protein into the host cytoplasm . Nevertheless , the ability of ChECs to enhance bacterial growth upon delivery into the plant cytoplasm and/or to suppress ChNLP1-induced cell death upon direct expression in the plant cytoplasm ( i . e . without their signal peptide ) raises the possibility that these effectors are translocated into host cells . It is possible that the amount of translocated ChEC:mRFP fusion protein is below the detection limits of confocal microscopy and immunogold labelling . Alternatively , the mRFP tag ( 28 kDa ) could interfere with effector translocation , although tags as large as 50 kDa were successfully used to trace M . oryzae effector translocation into invaded rice cells and their neighbors [35] . The use of antibodies raised against native ChEC proteins or peptides for immunolabelling may circumvent this potential problem . Colletotrichum homologs of NLPs have not been reported previously . ChNLP1 was an effective cell death inducer and was strongly and specifically upregulated at the switch from biotrophy to necrotrophy , consistent with a role in terminating the biotrophic phase of pathogenesis . The strong upregulation of ChNLP3 and ChNLP5 early during host penetration and biotrophy is intriguing and shows that expression of NLP-homologs is not detrimental to biotrophy per se . Consistent with this , the genome of the biotrophic oomycete Hyaloperonospora arabidopsidis contains several NLP-like genes without necrosis-inducing activity [36] . ChNLP3 and ChNLP5 lack three out of four highly conserved amino acid residues required for full NLP activity [37] , and as expected , ChNLP3 did not provoke cell death in N . benthamiana in our assay , suggesting these proteins have adopted new functions . We hypothesize that during initial host penetration and the intracellular biotrophic phase , Colletotrichum likely induces host cell damage and the release of damage-associated molecular patterns ( DAMPs ) and needs to secrete effectors that maintain host cell viability . In support of this , we found ChECs that suppressed cell death induced by ChNLP1 , which is likely to cause disintegration of the plant plasma membrane . This suppression activity was specific for NLP1-induced cell death , since these effectors did not suppress INF1-induced necrosis . It was previously demonstrated that distinct signalling pathways mediate NLP- and INF1-induced cell death in N . benthamiana [25] . It is conceivable that in our co-expression assay in planta , C . higginsianum effectors interfere with ChNLP1-specific signalling components and thereby prevent amplification of a cell death signal or its spreading from cell to cell . Plant responses evoked by NLPs share some characteristics with MAMP-triggered immunity [38] , [39] . The broad taxonomic distribution of NLPs in fungi , oomycetes and bacteria , and the relatively high sequence conservation of NLPs is also consistent with the classical concept of MAMPs [24] . It was suggested previously that plant cells recognize NLP action but not the protein itself and that NLP-mediated membrane disruption may release endogenous damage-associated molecular patterns ( DAMPs ) [37] . In view of their reciprocal expression pattern during infection , cell death-suppressing ChECs may not interfere with ChNLP1 itself or its cytolytic activity but rather with responses to DAMPs , or to other factors inducing cell death through similar pathways , that are released or triggered during biotrophic invasion . Consistent with a role in the suppression of MAMP/DAMP-triggered immunity , ChEC3 also supported the multiplication of plant pathogenic bacteria . ChEC3 and its paralog ChEC3a resemble CgDN3 from C . gloeosporioides , which is phylogenetically distant from C . higginsianum [12] . Similar to ChEC3 and ChEC3a , CgDN3 was found to be expressed during the early biotrophic phase of C . gloeosporioides [14] . These authors found that a fungal mutant lacking CgDN3 was non-pathogenic and elicited a cell death response in attacked cells , and they proposed a role for CgDN3 in interfering with plant defense . Here we provide experimental evidence that this effector family functions in host cell death suppression . ChEC5 contains a cerato-platanin domain ( CPD ) . CPD proteins have varied and sometimes contrasting activities , depending on the fungal pathogen and host species . This diversified protein family is prevalent in the genomes of ascomycete plant pathogens , including necrotrophs and obligate biotrophs as well as ectomycorrhizal fungi [40]–[42] , suggesting an important role during plant colonization . Depending on the pathogen's lifestyle , certain members may have co-opted functions to suppress or elicit host cell death , and thus can be regarded as ‘core’ effectors deployed by many plant-associated fungi . Jeong and co-workers [17] reported the failure of appressorial penetration and early abortion of pathogenesis in M . oryzae mutants lacking MSP1 , a homolog of ChEC5 , suggesting this CPD protein is involved in establishing a biotrophic interaction with host cells . Similar to ChEC5 , MSP1 expression was not differentially regulated in planta [43] . Our study provides the first evidence that a CPD protein acts as a cell death suppressor , further expanding the range of biological activities of this protein family . Although many effectors of filamentous pathogens interfere with INF1-induced cell death and cell death resulting from effector-triggered immunity [26] , [44] , [45] , only one effector suppressing NLP-induced cell death has been reported previously , namely P . infestans SNE1 [46] . Remarkably , SNE1 showed this effect when directly expressed in the plant cytosol , without its signal peptide . SNE1 carries the motif RXLX at its N-terminus , which resembles a variant of the oomycete host translocation motif RXLR , and was shown to mediate effector uptake [47] , [48] . Similarly , ChEC3 , ChEC3a , ChEC5 , ChEC6 and CHEC34 also exert cell death-suppressing activity without their signal peptides . Despite the lack of any RXLR-like or other shared amino acid motif in these proteins , this finding suggests they act intracellularly after translocation into host cells . Thus , the necrosis-suppressing effect of full-length ChECs could result from their re-entry after being secreted by the plant cell . In conclusion , the extreme stage-specificity and reciprocal expression patterns of cell death inducers and suppressors raise the possibility that Colletotrichum utilizes the same type of programmed cell death at the onset of necrotrophic growth that it must previously suppress during biotrophy . This would imply that effector-targeted components of the signalling cascade required for NLP-induced cell death become compatibility factors for the later necrotrophic stage of infection . In addition , the focal release of effectors is revealed as a new function for the appressoria of plant pathogenic fungi . The small contact zone delimited by the appressorial penetration pore can be regarded as a highly localized battleground to which both opponents target their weapons , even before the outer host barriers are breached . An intriguing finding was the tight regulation and plant-responsiveness of most effector genes . Future goals will be to decipher the nature of the plant signal ( s ) inducing effector gene expression and how they are sensed by the pathogen . A better understanding of host perception by phytopathogenic fungi is likely to provide novel strategies for the control of many economically important crop diseases through chemical intervention or plant breeding .
C . higginsianum isolate IMI349063A was used for EST generation and as background strain for fungal transformations . The abaxial surface of detached Arabidopsis leaves was inoculated and incubated as described previously [16] . Epidermal peels were prepared by adhering the adaxial surface with double-sided tape and quickly removing the epidermis using tweezers . Pieces of remaining mesophyll were excised quickly and the peels ( ∼15 mm2 per leaf ) were flash-frozen in liquid nitrogen . The following infection stages were sampled using this technique: 5 hpi ( germlings; RT-PCR ) , 22 hpi ( unpenetrated appressoria in planta; RT-PCR , qRT-PCR and EST generation ) , 40 hpi ( penetrated appressoria in planta with nascent biotrophic hyphae; RT-PCR , qRT-PCR and EST generation ) and mock-inoculated leaves ( RT-PCR and EST generation ) . Microscopic spot-checks of infected material ensured the absence of biotrophic hyphae in samples representing unpenetrated appressoria in planta and the absence of necrotrophic hyphae in samples representing penetrated appressoria with nascent biotrophic hyphae . Sectors including the first appearing pin-point water-soaked lesions ( ∼60 hpi ) were sampled for the switch between biotrophy and necrotrophy ( Figure S3 ) . Macerated leaves at 72 hpi represented late necrotrophy . Saprotrophic mycelium and conidia were produced as described previously [13] . In vitro appressoria and germlings formed on an unpenetratable surface were obtained by incubating spores on polystyrene [29] . Formation of ‘pseudo biotrophic hyphae’ within cellophane membranes was achieved using autoclaved dialysis tubing ( Visking , Roth ) . Details about library preparation , sequencing techniques , strategies to maximize gene discovery as well as EST assembly statistics can be found in Text S1 . ORFs were predicted from EST contigs with the Fusarium matrix of BESTORF ( Molquest package , Softberry ) . Solubly secreted proteins were identified following published guidelines [49] . Secreted proteins ( and their corresponding contigs ) for which no significant ( <1e-5 ) BLASTX , BLASTN or TBLASTX [50] match could be obtained in GenBank's protein , nucleotide and EST databases , respectively , were defined as ChECs , supplemented with proteins resembling previously described fungal effectors . TBLASTN identified orthologs in the C . graminicola genome ( http://www . broadinstitute . org/annotation/fungi/ ) . ChEC-encoding contigs depleted in ESTs from the late necrotrophic phase ( <15% ) were defined as ‘biotrophy-associated’ . The inventory of biotrophy-associated ChECs was manually curated to remove ( i ) incomplete ORFs with missing C-termini present in the genome showing homology to known proteins ( ii ) ORFs <20 residues and ( iii ) artifactual ORFs with monotonous sequences . For further analysis , we prioritized ChECs that resembled previously identified effectors and/or displayed high expression levels in biotrophy-relevant stages as determined by their EST read counts in our non-normalized libraries ( see Text S1 for details on library preparation ) Three biological replicates were obtained for each sampled fungal stage . cDNA was synthesized from 1 µg total RNA using the iScript cDNA synthesis kit ( Bio-Rad ) in a volume of 20 µL . Two µL of cDNA ( 5 ng/µL ) were amplified in 1X iQ SYBR Green Supermix ( Bio-Rad ) with 1 . 6 µM primers using the iQ5 Real-time PCR detection system ( Bio-Rad ) . Specific primers ( see Text S1 ) amplified fragments ranging from 106 to 329 bp with efficiencies ranging from 97 and 123% . GeNorm ( http://medgen . ugent . be/wjvdesomp/genorm/ ) was used to assess expression stability of five commonly used reference genes of which α-tubulin and actin were most stable ( stability value 0 . 047 and 0 . 051 , respectively ) and used to normalize gene expression [51] . To localize ChECs by fluorescent protein-tagging , genes including at least 1 . 5 kb or the entire upstream intergenic region were amplified from DNA , followed by TOPO cloning ( Invitrogen ) , sequence verification and shuttling into pFPL-R , a binary destination vector providing C-terminal translational fusions to mRFP . This vector was created and kindly provided by Dr . M . Farman ( Univ . of Kentucky , Lexington , KY ) . Fungal transformation was carried out as described by Huser and co-workers [52] . A detailed description of the cloning and the agroinfiltration procedure used for transient expression in N . benthamiana is described in Text S1 . The ‘effector detector vector’-based bacterial multiplication assay was performed according to Sohn et al . [27] . For cellulose acetate-stripping , 5% ( w/v ) cellulose acetate ( Sigma-Aldrich ) in acetone was brushed on the inoculated leaf . After complete acetone evaporation , the cellulose acetate coating was stripped off . Confocal images were obtained with Leica TCS SP2 or Zeiss LSM 700 confocal scanning microscopes . Excitation for imaging GFP fluorescence used the 488 nm laser line and emission was detected at 492–550 nm . For imaging mRFP , excitation was at 563 ( Leica ) or 555 nm ( Zeiss ) and emission was detected at 566–620 ( Leica ) or 557–600 nm ( Zeiss ) . To discriminate mRFP emission from autofluorescence , we used spectral imaging in the lambda mode of a Zeiss LSM 510 microscope . Using the Meta detector and 545 nm excitation line , image stacks with 558–648 nm emission were recorded . To separate mixed fluorescent signals and resolve the spatial distribution of mRFP fluorescence , linear unmixing was employed using the mRFP emission spectrum and several autofluorescence spectra as references . Cellulose acetate replicas and the stripped leaf surface were imaged with a Zeiss Supra 40VP scanning electron microscope at 10 kV . Stripped leaf surfaces were fixed using a cryo-preparation device ( Emitech Technologies , Ringmer ) . Specimens were frozen in liquid nitrogen slush and sputter-coated with palladium after sublimation of surface ice ( Polaron Sputter Coater SC 7600 , Quorum Technologies ) . Samples for ultrastructural observation were processed according to [13] . For immunogold labelling , infected plant material was fixed in 4% ( w/v ) p-formaldehyde and 0 . 5% ( v/v ) glutaraldehyde in 0 . 05 M sodium cacodylate buffer , pH 6 . 9 , for 2 h . After progressive low-temperature dehydration in a graded water-ethanol series [53] , samples were embedded in LR White resin ( Plano GmbH , Wetzlar , Germany ) . For immunogold labelling , we used procedures described previously [54] . Rabbit polyclonal anti-mRFP antibody ( R10367 , Molecular Probes ) and mouse monoclonal anti-ß-1 , 3-glucan antibody ( Biosupplies Australia Pty . , Parkville , Australia ) were both applied at dilutions of 1 in 500 . Goat anti-rabbit and goat anti-mouse IgG antibodies conjugated with 5 or 10 nm colloidal gold particles ( British Biocell International , Cardiff , UK ) were used as secondary antibodies . ChEC3 ( HE651156 ) , ChEC3a ( HE651158 ) , ChEC4 ( HE651159 ) , ChEC5 ( HE651160 ) , ChEC6 ( HE651161 ) , ChEC7 ( HE651162 ) , ChEC9 ( HE651164 ) , ChEC13 ( HE651168 ) , ChEC34 ( HE651193 ) , ChEC36 ( HE651195 ) , ChEC51 ( HE651213 ) , ChEC56 ( HE651219 ) , ChEC88 ( HE651251 ) , ChEC89 ( HE651252 ) , ChToxB ( HE651256 ) , ChNLP1 ( HE651257 ) , ChNLP3 ( HE651259 ) , ChNLP5 ( HE651261 ) . Accession numbers for the entirety of ChECs can be retrieved from Table S1 . ESTs were submitted to the EBI Sequence Read Archive under the accession number ERP001241 ( http://www . ebi . ac . uk/ena/data/view/ERP001241 ) .
|
Many fungal plant pathogens undergo a series of developmental and morphological transitions required for successful host invasion . For example , Colletotrichum higginsianum , a pathogen of cruciferous plants , employs a two-stage infection strategy called ‘hemibiotrophy’: after specialized penetration organs ( appressoria ) breach the host cuticle and cell wall , the fungus initially produces bulbous primary hyphae inside living epidermal cells ( ‘biotrophy’ ) , before entering a destructive phase in which host tissues are killed and macerated by filamentous secondary hyphae ( ‘necrotrophy’ ) . Here we investigated the role of secreted effector proteins in mediating hemibiotrophy and their delivery at fungal-plant interfaces . We found expression of many effector genes is plant-induced and distinct sets of effectors are deployed in successive waves by particular fungal cell-types . Early-expressed effector proteins are focally secreted from appressorial penetration pores and may function to suppress early plant defense responses , which we found to be activated before fungal entry . We also show that later-expressed effectors accumulate in structures formed at the interface between primary hyphae and living host cells , implicating these hyphae in effector delivery . Our findings indicate new functions for fungal infection structures and suggest a model for how this fungus switches from biotrophy to necrotrophy .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"plant",
"science",
"plant",
"biology",
"plant",
"pathology",
"biology"
] |
2012
|
Sequential Delivery of Host-Induced Virulence Effectors by Appressoria and Intracellular Hyphae of the Phytopathogen Colletotrichum higginsianum
|
The synaptonemal complex ( SC ) links two meiotic prophase chromosomal events: homolog pairing and crossover recombination . SC formation involves the multimeric assembly of coiled-coil proteins ( Zip1 in budding yeast ) at the interface of aligned homologous chromosomes . However , SC assembly is indifferent to homology and thus is normally regulated such that it occurs only subsequent to homology recognition . Assembled SC structurally interfaces with and influences the level and distribution of interhomolog crossover recombination events . Despite its involvement in dynamic chromosome behaviors such as homolog pairing and recombination , the extent to which SC , once installed , acts as an irreversible tether or maintains the capacity to remodel is not clear . Experiments presented here reveal insight into the dynamics of the full-length SC in budding yeast meiotic cells . We demonstrate that Zip1 continually incorporates into previously assembled synaptonemal complex during meiotic prophase . Moreover , post-synapsis Zip1 incorporation is sufficient to rescue the sporulation defect triggered by SCs built with a mutant version of Zip1 , Zip1-4LA . Post-synapsis Zip1 incorporation occurs initially with a non-uniform spatial distribution , predominantly associated with Zip3 , a component of the synapsis initiation complex that is presumed to mark a subset of crossover sites . A non-uniform dynamic architecture of the SC is observed independently of ( i ) synapsis initiation components , ( ii ) the Pch2 and Pph3 proteins that have been linked to Zip1 regulation , and ( iii ) the presence of a homolog . Finally , the rate of SC assembly and SC central region size increase in proportion to Zip1 copy number; this and other observations suggest that Zip1 does not exit the SC structure to the same extent that it enters . Our observations suggest that , after full-length assembly , SC central region exhibits little global turnover but maintains differential assembly dynamics at sites whose distribution is patterned by a recombination landscape .
The critical events that ensure a precise reduction in chromosome ploidy at the first meiotic division occur during meiotic prophase [1] , [2] . Chromosomes are typically unpaired as nuclei enter meiosis , but by late prophase have established connections with their homologous partners , which ultimately allow such partners to disjoin specifically from one another at the first meiotic division ( and segregate to separate daughter nuclei ) . Thus , a key accomplishment of meiotic prophase is the formation of stable partnerships between homologous chromosomes . The process that drives homolog pairing can be divided into two major steps: initiation and reinforcement . The molecular mechanism that mediates initial pairing between partner chromosomes is still unclear , but must involve the capacity to recognize homology and productively link this recognition to reinforcement of a paired association between two chromosomes . For meiotic nuclei in most organisms , homolog associations are stabilized for the long term via a crossover recombination event . DNA double-strand breaks ( DSBs ) are deliberately induced and undergo regulated repair during meiotic prophase; the fraction of double-strand breaks that are repaired to a crossover outcome involving the homolog's ( nonsister ) chromatid ensure that homologous partner chromosomes are linked , so long as sister cohesion remains intact . A physical and functional link between homology recognition and stable maintenance of chromosomal partnerships is the synaptonemal complex ( SC ) . SC formation ( synapsis ) involves the multimeric assembly of coiled-coil containing proteins; the coiled-coil containing proteins that establish the SC central region interact with themselves and with chromosome axis proteins associated with each homolog in order to ultimately generate an elaborate protein lattice at the interface of lengthwise-aligned chromosomes [2] , [3] . SC links initial homolog pairing with pairing maintenance by virtue of the fact that SC assembly is normally regulated such that it occurs only subsequent to homology recognition between chromosomes , whereas a fully assembled SC may be required for a normal number and distribution of crossover recombination events ( which will in turn ensure the persistence of homologous associations after SC disassembly and until chromosome segregation on the meiosis I spindle ) [2]–[8] . Zip1 is a primary structural component of the SC central region in budding yeast [9]–[11] . Like most SC central region proteins identified to date , Zip1 contains an extensive coiled-coil domain flanked by globular ends , and is predicted to form dimers . The structure of SC central region proteins resembles that of intermediate filament subunits and suggests a capacity to self-assemble [3] , [12] . Elegant immuno-electron microscopy experiments in budding yeast demonstrated that Zip1 subunits interact with one another near their amino termini , and interact with chromosome cores at their carboxyl terminal ends [9] . Additional proteins that do not share structural similarity to typical transverse filament proteins may also contribute to establishing SC central region . For example , the Small Ubiquitin-like MOdifier protein , SUMO , has been implicated in SC central region assembly on the basis of a dependence on central region proteins for its localization to SC [12]–[15] . The Synapsis Initiation Complex ( SIC ) proteins Zip2 , Zip3 and Zip4 promote SC central region assembly from distinct synapsis initiation sites along the length of the chromosome [16]–[18] . A set of synapsis initiation sites corresponds to centromeres , while the remainder are presumed , based on the number and distribution of SIC foci along chromosome arms , to correspond to crossover recombination sites [19] . Interestingly , SIC proteins remain localized as discrete foci along chromosomes even after full-length SC has been deposited [16]–[18] , raising the question of whether SICs have a later role in crossover recombination or SC maintenance at discrete sites , after their initial role in SC assembly . The existence of multiple layers of regulation that prevent inappropriate synapsis initiation suggests that SC may act as an irreversible tether between chromosomes [5] . Initial SC assembly interfaces closely with the homolog pairing process: Observations in multiple organisms of SC protein aggregation ( polycomplex formation ) in pairing-defective meiotic mutants indicate that cells normally regulate SC subunits such that their assembly on chromatin is contingent on homology verification [2] , [6] , [20] , and this notion has been borne out by the identification of checkpoint-like pathways that prevent SC assembly when homologous pairing fails [5] , [21] , [22] . However , the possibility of inappropriate initiations or interlocks between partially synapsed chromosome pairs raises the question of whether the SC central region maintains a capacity to remodel . Furthermore , assembled SC is the context within which meiotic recombination events mature , and analyses of SC-deficient meiotic mutants suggest that the SC functionally interfaces with at least a subset of recombination events [17] , [18] , [23] , [24] . Whether the dynamics or composition of SC central region is altered at Spo11-mediated recombination sites is not known . Here we investigate the dynamics of the fully assembled SC in budding yeast meiotic cells that are arrested at the pachytene stage of meiotic prophase ( when chromosomes are fully synapsed ) . Multimeric protein structures that exhibit both ongoing incorporation and ongoing exit ( “treadmilling” ) might be rapidly disassembled by lowering the “on-rate” in a local region; perhaps the budding yeast SC structure exhibits such dynamics that would enable it to disassemble quickly . On the contrary , we demonstrate that Zip1 continually incorporates into full-length synaptonemal complex and that , globally , Zip1 does not exit to the same extent as it enters the SC structure in pachytene-arrested cells . The rate of SC assembly and maximum size of SC central region increases in direct proportion to Zip1 copy number . Our observations suggest that the budding yeast SC structure behaves more like a “tether” than a “treadmill” during pachytene arrest . Interestingly , fully assembled SCs exhibit a non-uniformity in SC assembly dynamics such that initial post-synapsis Zip1 incorporation is favored in the vicinity of recombination events .
To explore the dynamics of the fully formed SC , we created a strain with inducible expression of the SC central region subunit , Zip1 ( or a tagged version , Zip1-GFP , a kind gift of D . Kaback [25] ) , using the estrogen-regulated Gal4-ER transcription factor in trans [26] , [27] . In the absence of β-estradiol , chromosome spreads from meiotic cells in which ZIP1 or ZIP1-GFP is solely under GAL1 promoter control exhibited a zip1 null phenotype: immunostaining of meiotic prophase chromosomes demonstrated that little to no Zip1 localizes to chromosomes in uninduced nuclei , and chromosomal axes often exhibited axial associations ( intermittent points of contact presumed to be crossover recombination events , flanked by regions of abnormally loose axial alignment between homologs ) ( Figure S1 ) . Moreover , in the absence of β-estradiol , the sporulation efficiency and spore viability of our “inducible-SC” strain phenocopied the zip1 null ( Figure S1 ) . On the other hand , when sporulated in the presence of β-estradiol , cells carrying an inducible ZIP1 allele exhibited Zip1 or Zip1-GFP at the interface of aligned , homologous chromosome axes , and the sporulation efficiency as well as the viability of the spore products of these meiotic cells was rescued to wild-type levels ( Figure S1 ) . These data indicate that the estrogen-inducible Zip1 can functionally substitute for endogenous Zip1 . Interestingly , when this strain was made homozygous for an ndt80 mutation , which arrests otherwise wild type cells at late prophase with fully synapsed chromosomes [28] , we observed that Zip1 can assemble de novo at the interface of homologous axes that have progressed to late prophase without SC ( Figure S2 ) . To explore the dynamics of the full-length SC in budding yeast , we next created a second ndt80 diploid strain , K39 , in which one ZIP1 locus encodes a tagged version of ZIP1 ( ZIP1-GFP ) under GAL1 promoter control , while the other ZIP1 locus is unmodified . After 24 hours of sporulation , ( 82% , n = 148 ) of K39 meiotic nuclei exhibited full length SCs comprised of untagged Zip1 ( as judged by the near absence of GFP on spread meiotic chromosomes ) . We used this K39 strain to ask: Will new Zip1 subunits continue to incorporate into pre-existing full-length SC structures , or will such superfluous Zip1 subunits become sequestered into a polycomplex structure ? ( Figure 1A ) . K39 meiotic cells that had been sporulating for 26 hours ( and thus predominantly containing full-length SC ) were exposed to β-estradiol to induce the expression of Zip1-GFP ( Figure 1B ) . Surface spread nuclei from uninduced and induced meiotic cells harvested 1 , 2 or 3 hours after β-estradiol addition were immunostained with anti-GFP and anti-Zip1 antisera to monitor the distribution of newly induced Zip1-GFP subunits , relative to previously assembled SC . Our induction experiment revealed that new Zip1-GFP readily incorporates into previously established , full length SCs instead of forming polycomplex ( Figure 1C , 1D ) . 96% ( n = 25 ) of cells harvested after 1 hour of incubation with β-estradiol exhibited Zip1-GFP incorporation along the length of fully formed SC . Of these nuclei , none exhibited polycomplex . Longer incubation in β-estradiol correlates with increasing levels of Zip1-GFP incorporation: over half of nuclei monitored after 2 hours of induction exhibited Zip1-GFP completely coincident with unlabeled Zip1 along extensive lengths of SC ( Figure 1D ) . Uninduced control nuclei exhibited rare GFP staining , resulting from either a low level of constitutive expression allowed by the inducible promoter system in some cells , or from nonspecific background staining . When we examined Zip1-GFP induction in meiotic mutants , such as zip3 , that display polycomplex in addition to SC stretches , induced nuclei always exhibited Zip1-GFP incorporation into polycomplex ( as well as into SC stretches , see below ) . To explore whether the newly induced Zip1-GFP that decorates pre-established SCs is functionally incorporated , we took advantage of a previously characterized zip1 allele , zip1-4LA [29] . While zip1 null mutant cells ( of the BR1919 genetic background ) fail to build SC but do sporulate at reduced levels , zip1-4LA homozygotes build full-length SC , exhibit a normal level of crossover recombination but altogether fail to make spores . Instead , zip1-4LA meiotic cells arrest late in prophase with fully synapsed chromosomes ( thus zip1-4LA meiotic cells cytologically resemble ndt80 meiotic cells at late prophase ) . Mitra and Roeder ( 2007 ) previously suggested that the pachytene arrest exhibited by zip1-4LA cells is triggered by an assembled ( albeit defective ) SC , based on their observation that spo11 zip1-4LA double mutants , in which Zip1-4LA is expressed but fails to assemble SC , sporulate to the same extent as spo11 single mutants . We reasoned that if Zip1 subunits functionally incorporate into previously deposited , full-length SC , then post-synapsis expression of wild-type Zip1 ( or Zip1-GFP ) may be sufficient to “remodel” SCs initially built of Zip1-4LA protein and suppress the meiotic arrest associated with Zip1-4LA SCs . To examine the possibility that new incorporation of Zip1 can remodel and alter the behavior of a full-length SC , we built a strain in which one chromosomal ZIP1 locus carries PGAL1[ZIP1] or PGAL1[ZIP1-GFP] while the other chromosome contains the zip1-4LA allele under the endogenous ZIP1 promoter ( Figure 2A ) . After 24 hours in sporulation media , over 90% of meiotic nuclei from this strain exhibited late prophase chromosome morphology and full length SCs ( built of Zip1-4LA protein ) , with little GFP visible in those meiotic nuclei carrying the PGAL1 [ZIP1-GFP] cassette ( Figure 2B ) . Moreover , less than 1% of these cells eventually formed spores or spore-like structures after 40 hours in sporulation media ( Figure 2C ) . When β-estradiol was added to the PGAL1[ZIP1-GFP] - containing strain at the 24 hour time point , Zip1-GFP was readily detected in full-length SCs after just one hour of induction ( Figure 2B ) . Thus , SCs built of Zip1-4LA are capable of incorporating Zip1-GFP subunits . Moreover , cells from strains containing either the ZIP1 or ZIP1-GFP inducible allele that were sporulated in the presence of β-estradiol exhibited near wild-type sporulation efficiency ( Figure 2C ) . These results indicate that post-synapsis incorporation of Zip1 can functionally alter SC behavior . Early Zip1-GFP incorporation into previously established , full-length SC exhibits a non-uniform pattern ( Figure 1C , Figure 2B ) . Instead of a uniform appearance throughout the SC surface , discrete Zip1-GFP foci initially decorate the previously established SC . This pattern suggests the existence of discrete sites along the length of the SC where Zip1 incorporation is favored . As SC normally builds from multiple discrete sites along the length of chromosomes during its assembly , we asked whether such sites of initial Zip1-GFP entry exhibit characteristics of synapsis initiation sites . We first assessed whether sites of initial Zip1-GFP incorporation into full-length SCs co-localize with the Synapsis Initiation Component ( SIC ) , Zip3 [16] . Meiotic nuclei containing fully-synapsed chromosomes and expressing Zip3-MYC were exposed to a short period ( approximately 45′ ) of ZIP1-GFP expression before they were harvested for chromosome spreads and immunostained with anti-Zip1 , anti-GFP and anti-MYC antibodies . Zip1-GFP and Zip3-MYC distribution was assessed on SCs from nuclei with maximally spread chromosomes . 72% ( n = 406 ) of total Zip1-GFP foci on chromosomes localized directly adjacent to or overlapping ( most typical ) a Zip3-MYC focus , and 93% ( n = 122 ) of total Zip1-GFP short linear stretches partially overlapped a Zip3-MYC focus ( Figure 3 ) . Zip1-GFP induction does not itself cause recruitment of additional Zip3-MYC to chromosomes , as meiotic chromosomes exhibit an equal number of Zip3-MYC foci before and after ZIP1-GFP induction ( Figure 3C ) . In order to assess whether the localization of initial post-synapsis Zip1-GFP incorporation events near Zip3-MYC foci is significant , Monte Carlo sampling analyses with 106 iterations ( see Methods ) were performed on a subset ( n = 69 ) of well-spread Zip1 stretches that exhibited a range of the smallest GFP foci ( 0 . 58 µm or less; average size = 0 . 31 µm ) , representing the earliest Zip1-GFP incorporation events ( see Figure S6 for examples ) . Results from Monte Carlo simulations indicated that the observed frequency of post-synapsis Zip1-GFP incorporation events that are completely encompassed by a Zip3-MYC focus ( 42/117 ) is significantly higher than expected from a random distribution of Zip1-GFP foci on Zip1 stretches , given the spatial organization of Zip3-MYC foci on each stretch and the dimensions of Zip1-GFP foci randomly taken from the sampled Zip1-GFP population ( P = 0 . 0072 ) . Furthermore , this statistical test indicated that the observed frequency of post-synapsis Zip1-GFP positioned directly adjacent to , partially overlapping with or completely encompassed by a Zip3-MYC focus ( 78/117 for this dataset ) is significantly higher than expected from a random distribution ( P = 0 . 008 ) . We conclude that at least a subset of initial post-synapsis Zip1-GFP events preferentially colocalize with Zip3 foci . During wild-type meiotic prophase in the BR1919 strain background , 50–80% of detectable earliest synapsis initiation events occur at centromeres [19] . We assessed whether “post-synapsis” Zip1 incorporation usually occurs first at centromeres by monitoring the distribution of induced Zip1-GFP relative to a tagged centromere protein , Ctf19-MYC ( Figure 4 ) . After approximately 45′ of induction , nuclei with earliest Zip1-GFP incorporation events ( i . e . nuclei with between 4–12 short GFP stretches ) were examined by immunostaining . Among these nuclei , typically fewer than half of short ( 0 . 35–0 . 5 µm ) Zip1-GFP entities were localized at or adjacent to a centromere , indicating that centromeres are not completely occupied by Zip1-GFP prior to Zip1-GFP incorporation at chromosome arm sites ( Figure 4B ) . We next examined all centromeres in a population of nuclei exhibiting clear Zip1-GFP induction ( >12 discrete Zip1-GFP incorporation events per nucleus ) . Among centromeres in this population , 28% ( n = 364 ) colocalized with a Zip1-GFP focus , 42% colocalized with a short or long stretch of Zip1-GFP , while 30% of centromeres lacked Zip1-GFP staining ( Figure 4C ) . These data suggest that , centromeres are not necessarily preferred over chromosomal arm sites for initial post-synapsis incorporation of Zip1 . Previous work had suggested a non-uniformity in the structure of the budding yeast SC central region: discrete , local domains of SC central region stain more intensely with Zip1 antibody ( Zip1 “peaks” ) as compared to other domains ( “valleys” ) [30] . Using a subset of immuno-stained meiotic nuclei that were maximally surface-spread such that Zip1 staining formed visually-apparent domains of thicker ( peaks ) and thinner ( valleys ) Zip1 staining regions along the length of the SC , we explored whether early sites of post-synapsis Zip1-GFP incorporation strictly associate with either “peaks” or “valleys” of a previously deposited SC . We observed a substantial fraction of post-synapsis Zip1-GFP incorporation events ( 25% ( n = 300 ) ) that clearly colocalized with a “valley” of SC central region ( Figure S3 ) . The remaining sites either localized at a recognizable “peak” of SC central region ( 45% ) or could not be unambiguously assigned to either an SC “peak” or “valley” ( 30% ) . Thus , the distribution of initial Zip1-GFP incorporation sites along the length of the SC does not precisely mimic the global domain structure of Zip1 within the SC itself . This conclusion is further supported by our observation that Zip3-MYC foci localize to Zip1“valleys” as well as to Zip1 “peaks” ( Figure S3 ) and that meiotic cells missing Pch2 , a protein that promotes the non-uniform distribution of Zip1 in SC [30] , nevertheless exhibit a non-uniform spatial pattern of Zip1-GFP deposition into previously established SCs that looks indistinguishable from PCH2+ cells ( see below ) . Our observation that most initial sites of Zip1-GFP incorporation occur at or near Zip3 foci on full-length SCs , in conjunction with previous demonstrations that the SICs , Zip2 , Zip3 and Zip4 , co-localize on fully synapsed chromosomes , raises the possibility that post-synapsis Zip1 incorporation requires SIC function . It is technically challenging to address the question of whether SIC function is required for SC maintenance dynamics , since SIC proteins are normally required for SC assembly in the first place . However , while SIC proteins are indispensable for SC assembly at a set of recombination sites along chromosome arms , SC assembly at centromeric synapsis initiation sites is less reliant on SIC function , provided that the Fpr3 and Zip3 proteins are absent [5] . In fact , zip3 mutant meiotic nuclei assemble a limited amount of SC , using predominantly centromeric synapsis initiation sites , even when Fpr3 is present [16] , [19] . Thus we monitored initial Zip1-GFP incorporation into previously established SC in zip3 single mutants , as well as in meiotic nuclei from zip3 fpr3 , zip2 zip3 fpr3 and zip4 zip3 fpr3 mutant strains . After a short incubation in β-estradiol ( 45 minutes ) , 62% , ( n = 1054 ) of Zip1 stretches exhibited by SIC-defective meiotic nuclei exhibited Zip1-GFP incorporation at multiple discrete sites across their entire lengths , suggesting that new Zip1 incorporation into previously established SCs can occur independent of SIC activity per se . Moreover , while SIC-defective mutants exhibited a lower cumulative length of SC as compared to wild type meiotic pachytene nuclei ( Figure S4 ) , they exhibited a similar average extent of incorporation of Zip1-GFP per unit length of previously established SC ( Figure 5B and Figure S4 ) . While de novo synapsis initiation occurs predominantly at centromeric sites in SIC-deficient meiotic cells [5] , chromosomal arms exhibit no significant deficit in favored sites of post-synapsis Zip1-GFP incorporation , as seen by the existence of a centromere marker in our immunostained preparations ( Figure 5A ) and consistent with our measurements above . We note that strains missing Zip2 and Zip4 exhibited a slight reduction in the frequency of discrete Zip1-GFP incorporation events per cumulative length of SC ( Figure S4 ) . However the range in the fraction of SC decorated by Zip1-GFP after a 45-minute induction in Zip2-and Zip4-deficient strains ( data derived from the same experiments ) was similar to wild type ( Figure 5B ) . These observations are consistent with the idea that Zip1-GFP incorporation perhaps occurred slightly faster in Zip2 and Zip4-deficient strains during these experiments , with either a similar or a reduced number of favored sites . Overall , our analyses demonstrate that while post-synapsis Zip1-GFP incorporation initially often localizes adjacent to Zip3 , ( and by inference , Zip2 and Zip4 ) , SICs are not required , per se , for ongoing Zip1 incorporation into previously established SC , nor for the existence of discrete sites of initial Zip1 incorporation . The Pph3 phosphatase regulates Zip1 phosphorylation status during meiotic prophase [31] . In light of the known role for phosphorylation dynamics in regulating assembly and disassembly dynamics of intermediate filaments [32] , [33] , we were interested in whether pph3 mutant cells would exhibit a defect in post-synapsis Zip1-GFP incorporation . The pattern and extent of Zip1-GFP incorporation into full length SCs built in the absence of Pph3 is similar to that displayed by wild-type cells , as shown in Figure 5B . Similarly , SCs built in the absence of the Pch2 protein , which has been proposed to influence the distribution of Zip1 within the SC central region [30] exhibits a non-uniform spatial pattern of Zip1-GFP deposition into previously-established SCs that appears similar to wild-type meiotic nuclei ( Figure 5B ) . Since Zip3 are presumed to mark interhomolog crossover recombination intermediates , we asked whether post-synapsis Zip1-GFP incorporation dynamics depend upon an interaction with a homolog by examining Zip1-GFP incorporation into assembled SC during haploid meiosis . MATa/MATα haploid cells carrying an untagged ZIP1 gene on a CEN plasmid and an inducible ZIP1-GFP gene at the chromosomal locus were sporulated for 26 hours and then exposed to β-estradiol to induce ZIP1-GFP expression . A fraction of surface-spread nuclei from sporulated haploid cells exhibited extensive Zip1 assembly , albeit with a temporal delay ( 18/101 nuclei exhibited long Zip1 stretches and 12/101 nuclei displayed Zip1 assembled along the full length of all chromosomes ) . Although SC assembly in haploid meiotic cells has been reported previously [34] , it should be noted that the ndt80 mutation in our strains may account for the somewhat higher frequency of full-length Zip1/SC stretches observed . After a 45-minute induction , 100% ( n = 25 ) of those haploid meiotic cells containing extensive Zip1 stretches displayed Zip1-GFP deposition at multiple discrete sites along the length of SCs ( Figure 5A ) . Thus , a homolog is dispensable for the establishment of an SC architecture that displays a non-uniform pattern of initial Zip1 entry sites . Interestingly , our studies revealed that Zip1 assembled on haploid chromosomes also exhibits a focal pattern of Zip3 ( Figure S5 ) , suggesting the possibility that Zip3-marked recombination structures ( presumably involving sister chromatids ) exist in haploid meiotic nuclei . In order to investigate whether Zip1 subunits exit to the same extent as they enter the SC structure , we carried out a complementary version of our first Zip1 induction experiment ( Figure 6A ) . We held ndt80 mutant cells expressing one chromosomal copy of ZIP1-YFP ( a kind gift of D . Kaback , constructed as in [35] ) at late prophase arrest until a majority of nuclei exhibited full-length , intrinsically fluorescent SC central region ( 22 hours ) . Next , we induced the expression of two copies of ZIP1+ using the estrogen-inducible PGAL1 system . In addition , we carried out the same induction experiment using a strain expressing one copy of ZIP1-YFP under the endogenous ZIP1 promoter and additionally carrying two copies of ZIP1-YFP under PGAL1 control . Given the fact that Zip1 subunits rapidly incorporate into full-length SCs , we reasoned that if Zip1 exit accompanies entry , we should observe a decrease in the intrinsic fluorescence of Zip1-YFP SCs after some hours of induced Zip1 ( untagged ) expression . On the other hand , we should observe no such decrease in SC fluorescence in meiotic nuclei from a strain in which ZIP1-YFP is induced from two chromosomal loci . Zip1-YFP fluorescence intensity was sampled in three adjacent 0 . 25×0 . 25×1 µm region-of-interest ( ROI ) volumes ( a volume that encompasses the full z-dimension of a segment of SC ) , positioned along the length of a well-spread SC ( Figure 6 ) . The range of fluorescence intensities of such ROI volumes did not change significantly for the uninduced strains throughout the time courses . Moreover , cells induced to express two copies of ZIP1+ exhibited no decrease in the range of SC fluorescence intensities . Interestingly , however , a significant increase ( two-tailed P<0 . 0001; see Figure 6 legend ) in the intrinsic fluorescence of SC volumes was observed several hours after induction of expression from two copies of ZIP1-YFP . When we quantified the intensity of Zip1 in SCs using an anti-Zip1 antibody that can detect both Zip1 and Zip1-YFP species , both strains exhibited similar , significant increases in the intensity of Zip1 immunofluorescence , presumably representing an increase in SC volume and/or density under induction conditions ( Figure 6 ) . Thus , we conclude that Zip1 subunits do not exit the SC structure to the same extent as they enter , and that , as a consequence , the full length SC continuously builds in volume and/or density during a steady state ( such as meiotic prophase arrest ) . To confirm the observation that steady-state SC grows over time and to explore whether the steady-state SC has an intrinsic size constraint within the context of aligned homologs , we analyzed SC central region size during a time course of meiotic prophase in ndt80 mutant ( thus prophase-arrested ) meiotic cells ( Figure 7A ) from strains carrying ZIP1-YFP as their sole source of Zip1 . We measured the intensity of Zip1-YFP in ROI volumes of full-length SC ( as described above ) in cells carrying one , two , four or six copies of ZIP1-YFP . All ZIP1-YFP-carrying strains exhibited similar sporulation efficiencies ( data not shown ) and spore viability ( Table S2 ) . Our observations from these experiments support the idea that Zip1 subunits readily enter but rarely exit the budding yeast SC . The range of intensities of Zip1-YFP within a specified ROI volume of full-length SC in all strains increased , on average , during the 11 hours of meiotic prophase sampled during the time course ( Figure 7A ) . Importantly , full-length SCs measured at time points spanning 16–21 hours of sporulation , which likely correspond to periods of “normal” pachytene progression in our BR strain background ( as opposed to an “arrested” pachytene state ) exhibited progressively larger Zip1-YFP signals per ROI volume . Moreover , at any given time point , full-length SC from strains carrying a larger ZIP1-YFP copy number exhibited progressively larger Zip1-YFP signals per ROI volume . This increase in Zip1-YFP signal was evident even between SCs from strains carrying one versus two copies of ZIP1-YFP . Thus , even at 16 hours of sporulation , full-length SCs from strains containing two copies of ZIP1-YFP typically had experienced some measure of post-synapsis Zip1-YFP incorporation , as these SCs , on average , contain substantially more Zip1-YFP than full-length SCs from strains carrying just one copy of ZIP1-YFP ( at the same early time point ) . These data indicate that the budding yeast SC central region exhibits net growth after its initial installation at the interface of homologous chromosomes . The fact that strains with increased ZIP1-YFP copy number displayed correspondingly increased average Zip1-YFP intensity in their full-length SCs at even the earliest time point suggests that meiotic cells carrying more copies of ZIP1-YFP generate full-length SCs earlier , on average , than cells with fewer ZIP1-YFP copies ( and thus have a “head start” on post-synapsis incorporation of Zip1-YFP ) . Consistent with this idea , we observed a direct correlation between ZIP1-YFP copy number and the rate at which chromosomes achieve full synapsis ( Figure 7B ) . ( We note that these data do not indicate the mechanism underlying faster achievement of full-length SC in strains carrying additional copies of ZIP1-YFP; such a mechanism could involve additional SC initiations or faster elongation from a given initiation site . ) Interestingly , Zip1-YFP polycomplex formation occurred at a frequency of 83% or more in nuclei from the ZIP1-YFP 4 and 6 copy strains , even at the earliest time points sampled , yet SC structures still exhibited net growth . This result indicates that the appearance of polycomplex per se does not signal a loss of capacity to incorporate into SC . Within the constraints of our time course experiment we did not observe any upper limit to SC intensity; in fact , during one of the two experiments shown for the strain with 6 copies of ZIP1-YFP , select SC regions exhibited intensities approaching that found in Zip1-YFP polycomplex aggregates . Together , these data suggest that a synapsed homolog pair exerts little constraint on SC growth . Furthermore , our untagged Zip1 induction experiment ( Figure 6 ) in conjunction with these growth experiments suggest that SC central region exhibits extremely low subunit turnover; previously deposited SC remains static as additional central region subunits continue to build .
Our induction experiments demonstrate that the full-length SC in budding yeast is dynamic , as new transverse filament subunits ( Zip1-GFP ) continuously incorporate when meiotic cells are arrested at late prophase . It will be informative to learn whether additional proteins that have been implicated in generating SC central region , such as SUMO , exhibit similar post-synapsis incorporation dynamics . Instead of a uniform incorporation of Zip1-GFP along the length of a previously established SC , post-synapsis Zip1-GFP subunit incorporation initially exhibits a focal pattern . Such initial Zip1 entry sites may occur at centromeres and a majority of them localize adjacent to sites marked by the synapsis initiation component , Zip3 . Our statistical analysis indicates that Zip3 marks at least a subset of initial post-synapsis Zip1-GFP incorporation sites . Our observation that at least a subset of post-synapsis Zip1-GFP incorporation events preferentially localize to Zip3-marked sites along the SC raises the possibility that many or all of the ongoing Zip1-GFP incorporation events into full-length SC could be the result of synapsis initiation activity by SIC proteins , as the de novo initiation of Zip1 assembly ( synapsis ) at presumed recombination sites during normal meiotic progression requires the SIC proteins Zip2 , Zip3 and Zip4 . On the contrary , post-synapsis Zip1 incorporation sites do not exhibit the same regulatory mechanisms as sites of de novo synapsis initiation , since our data demonstrate that post-synaptic events are not dependent on Zip2 , Zip3 or Zip4 proteins ( Figure 5 , Figure S4 ) . An interesting possibility is that favored Zip1 entry sites contribute to the establishment of Zip1 “peaks” and “valleys” in full length SC . However , under such a model one might expect that post-synapsis Zip1-GFP incorporation events would seldom localize to Zip1 “valleys” . We observed that sites of post-synapsis Zip1 incorporation do not strictly correspond to either local peaks or valleys of Zip1 within the previously deposited , full-length SC , and the Pch2 protein which regulates such domains of high and low Zip1 [30] does not alter the frequency or qualitative pattern of Zip1 incorporation sites . One could reconcile our observation that a fraction of initial post-synapsis Zip1-GFP events occur at Zip1 valleys with the idea that favored Zip1 incorporation sites contribute to the formation of Zip1 peaks if we further propose that rates of Zip1-GFP incorporation varies between favored sites or at a given site over time . Alternatively , Zip1 peaks and valleys may arise from a mechanism that mediates Zip1 subunit movement laterally within the SC . Favored Zip1 entry sites are intriguing because they indicate a level of non-uniformity in SC architecture . However , with time , the pattern of post-synapsis Zip1-GFP distribution completely overlaps the full-length SC . One explanation for the progressive change in the distribution of post-synapsis Zip1 might be that the budding yeast SC maintains a capacity to add new Zip1 all along its length but certain sites are more favorable for addition than others . Alternatively , as mentioned above , perhaps the SC incorporates new transverse filament subunits exclusively at certain sites but that some newly incorporated subunits can move to a different location along the length of the SC . Indeed , the frequent occurrence of post-synapsis Zip1-GFP foci that only partially overlap Zip3-MYC gives the impression that newly incorporated subunits might grow outward from the Zip3-MYC domain . Such immunofluorescence data , however , are also consistent with a model in which post-synapsis Zip1-GFP domains grow longer via continued incorporation at both Zip3 and ( eventually ) non-Zip3 sites . Our data indicate that , once the SC has assembled along the full length of meiotic axes in cells experiencing pachytene arrest , Zip1 subunits exhibit little turnover but continue to incorporate , allowing the SC to grow to many times its original density or volume . Thus , the SC central region appears to behave more like a “tether” than a “treadmill” , at least during late prophase when chromosomes are fully synapsed . The absence of significant turnover of previously deposited transverse filaments suggests that the SC may only have the capacity to remodel through new subunit addition . One can imagine that continuous SC assembly might facilitate at least some mechanistic aspects of synaptic adjustment , where an unsynapsed chromosomal domain , contiguous with an otherwise synapsed chromosome pair , eventually incorporates into the full length SC during prophase [2] , [36] . However , continuous assembly without SC subunit turnover raises the question of whether budding yeast can correct inappropriate synapsis . Perhaps SC assembly in budding yeast is sufficiently regulated at the outset so that irrevocable synapsis mistakes occur at an extremely low frequency . On the other hand , an SC disassembly mechanism that does not rely on ongoing central region turnover may exist that can be deployed over a local chromatin domain . Another possibility may be that SC central region can be physically disrupted , given enough applied force , without an active disassembly mechanism . Finally , the balance between subunit entry and exit may differ at different stages of budding yeast synapsis , for example during zygotene and early pachytene stages , potentially allowing error correction at those stages before solidifying a final synapsis configuration . Couteau and Zetka observed local SC central region disassembly in response to irradiation in C . elegans late meiotic prophase nuclei [37] , suggesting that C . elegans SC may have the capacity to remodel in response to recombination intermediates . Contrary to this picture , a relatively static central region in conjunction with ongoing Zip1 incorporation preferentially at Zip3 sites raises the possibility that the budding yeast SC only structurally accommodates those ( or a subset of those ) recombination events arising prior to or concomitant with SC deposition . This possibility is consistent with several recent models for meiotic prophase chromosome dynamics in budding yeast [38] , [39] . Under this scenario , the budding yeast SC central region may not need to “remodel” to structurally accommodate recombination sites , since those that will interface with the SC will have already progressed to a certain intermediate stage prior to SC installation . Such an intermediate stage would need to involve establishing a local environment in which recombination-associated structural changes in the DNA and recombination enzyme complexes can proceed unperturbed by ongoing deposition of SC central region proteins in the surrounding vicinity . We note , however , that our observation of little global SC turnover does not rule out the possibility that SC turnover occurs differentially at distinct sites . As SC demonstrates a non-uniformity in assembly dynamics in the vicinity of recombination events , SC subunit turnover may be higher at preferred entry sites but still below the detection level of our assay . The pattern of initial post-synapsis Zip1 incorporation near Zip3 foci , together with the fact that SC assembly is contingent upon early steps in the recombination pathway , suggests that recombination-based events establish much of the non-uniform architecture of the SC . One way to think about how the recombination landscape may shape an SC composite architecture is that certain recombination events create a meiotic axis perturbation that similarly interrupts the SC , and at these interruptions SC dynamics are distinct from those in the rest of the structure . Importantly , any model attempting to explain the non-uniform dynamics of SC architecture in terms of recombination events must account for the fact that interhomolog crossover recombination intermediates are not required for establishing such SC architecture . Haploid nuclei that have been genetically “tricked” into entering meiosis can build extensive SC ( albeit with a temporal delay ) , and we observe that initial Zip1-GFP incorporation into the previously assembled SCs in haploid meiotic cells occurs with similar timing and with a qualitatively similar distribution as compared to post-synapsis Zip1-GFP incorporation into diploid SCs . We suggest that initial sites of post-synapsis Zip1 incorporation into haploid SCs reflect a set of recombination intermediates analogous to those that we propose shape SC dynamics in diploids , but which engage the sister chromatid . Interestingly , Zip3-GFP or Zip3-MYC decorates the length of SCs in haploid nuclei ( Figure S5 ) , consistent with the idea that an analogous set of SIC-associated recombination events , albeit between sister chromatids , influences the architecture of Zip1 structures that are assembled on haploid meiotic chromosomes . The assembled SC is the context within which at least a subset of meiotic recombination events mature . Moreover , the SC influences the resolution of recombination events . Budding yeast SC-deficient mutants initiate recombination , but the fraction of those double-strand breaks that are repaired to a crossover outcome is diminished [17] , [18] , [23] , [24] , and the remaining interhomolog crossovers exhibited by such mutants do not exhibit interference [16]–[18] , [24] , [38] , [40] . Interference refers to a nonrandom distribution such that two crossover events rarely occur close together . Interestingly , Fung et al . demonstrated that chromosome axes display a cytological manifestation of interference , in the form of a nonrandom distribution of synapsis initiation complexes , even in the absence of assembled SC [41] . Yet genetic studies on SIC-deficient meiotic mutants suggest that assembled SC is required to generate interfering interhomolog crossovers . These conflicting observations are reconciled by proposing that assembled SC influences the repair outcome of a set of interfering recombination intermediates . As Zip3 sites have been proposed to mark such a set of interfering recombination intermediates , favored sites of post-synapsis Zip1 incorporation into assembled SC ( which largely appear near Zip3 ) may reflect functional interfaces between assembled SC and crossover-designated recombination intermediates that play a role in maintaining the designation of interfering crossover-destined recombination intermediates and/or play a role in influencing the repair outcome of the associated recombination events . Finally , ongoing SC growth may itself be functionally important for creating a rigid structure that aids in maintaining or influencing the repair outcome of interfering , crossover-destined recombination intermediates . Under this model , our observation that full-length SCs grow continuously in a Zip1 concentration-dependent manner leads to the prediction that meiotic cells carrying a larger Zip1 copy number will maintain an increased capacity to impose interference . Klutstein et al . ( 2009 ) recently reported evidence in support of this prediction; these authors discovered that crossover interference is reduced in strains carrying just a single copy of ZIP1 [42] .
All diploids are isogenic with BR1919-8B [43] . Strains used in this study are listed in Table S1 . Yeast genetic manipulations were carried out via standard procedures . Meiosis-competent haploid strains were constructed using pB211 to integrate MATa at THR1 in a MATα haploid [44] . The ZIP1-GFP fusion construct used for all experiments except for those measuring fluorescence intensity ( Figure 6 and Figure 7 ) is described in [25] . Fluorescence intensity experiments used ZIP1-YFP fusions , in which YFP is inserted between amino acids 700 and 701 of Zip1 , as described in [35] . Both fusion constructs were a kind gift of David Kaback . To make strains carrying multiple copies of ZIP1-YFP , a ZIP1-YFP fragment was excised from pRS316-ZIP1-YFP and placed into the LEU2-marked integrating shuttle vector pRS305 [45] . The verified clone , BAM179 , was cut with Xcm1 in order to target to leu2 . Integration and function of ZIP1-YFP::LEU2 was verified by sporulation rescue of a zip1 null and visual assay of fluorescent SCs in live cells . The TRP1::PGAL1 promoter cassette was placed upstream of the ZIP1 or ZIP1-GFP ORF by directed transformation of a PCR product with homology to the 5′ end of ZIP1 . pKB80 ( GAL4 . ER::URA3 ) was integrated at ura3 to introduce the chimeric protein that responds to β-estradiol and activates PGAL1 promoters [26] . Strains were grown overnight in YPADU media to late log/early stationary phase at 30° , washed with an equal volume of water and suspended at a 4-fold dilution in 2% potassium acetate ( pH 6–6 . 5 ) . Strains were induced with 1 µm β-estradiol ( Sigma E2257 , prepared in ethanol ) . Uninduced cultures were removed from the sporulating culture just prior to induction , and to these cultures an appropriate volume of 95% ethanol was added . 5–10 ml of sporulating culture was removed at various time points for chromosome spreads or TCA protein preparation . Meiotic chromosome spreads , staining and imaging were carried out as previously described [46] with the following modifications: 80 µl 1xMES and 200 µl 4% paraformaldehyde fix were added to spheroplasted , washed cells , then 80 µl of resuspended cell solution was put directly onto a frosted slide and cells were distributed over the entire slide using the edge of a coverslip with moderate pressure . The slide was allowed to air dry until less than half of the liquid remained , and then washed in 0 . 4% Photo-flo as described [46] . The following primary antibodies were used: chicken anti-GFP ( 1∶100 ) ( Abcam ) , mouse anti c-myc ( 1∶200 ) ( Invitrogen , 9E10 . 3 ) , affinity purified rabbit anti-Zip1 ( 1∶100 ) ( raised at YenZym Antibodies , LLC , against a C terminal fragment of Zip1 as described in [10] ) . Secondary antibodies were obtained from Jackson ImmunoResearch and used at a 1∶200 dilution . The time course experiments in Figure 7 were carried out over a series of three experiments each containing at least two strains: experiment 1 ( which is depicted first of the two datasets for each strain along the X axis in Figure 7 ) contained all four strains ( 1 copy , 2 copy , 4 copy and 6 copy ) ; experiment 2 contained the second dataset for the Zip1 1 copy and the Zip1 6 copy strain; experiment 3 contained the second dataset for the Zip1 2 copy and the Zip1 4 copy strain . Imaging was carried out using a Deltavision RT imaging system ( Applied Precision ) adapted to an Olympus ( IX71 ) microscope . Zip1 , Zip1-GFP and Zip3-MYC lengths were measured using the Softworx Measure Distance Tool . Graphpad Prism software was used for scatterplot generation and statistical analysis . All slides used for quantitative fluorescence analysis were assayed for intensity of the native fluorophore ( Zip1-YFP ) and were stained only with DAPI to visualize DNA . Samples prepared for fluorescence quantitation experiments were imaged with a fixed exposure time so that intensity measurements could be compared between time points and strains . For experiments not requiring fluorescence quantitation , exposure times were optimized on a slide-by-slide basis to obtain linear range . All fluorescence quantitation experiments used the following imaging conditions . 7 Z-stack sections of 0 . 2 µm were collected in the FITC channel using a 1 second exposure per section . This exposure time was selected based on the minimal time to achieve a quantifiable fluorescent image , above background levels , in the dimmest SCs ( early synapsis in a strain carrying only one copy of ZIP1-YFP ) . As reference , an additional DAPI channel image was acquired at the middle section , with a 0 . 5 second exposure . Projections were constructed from raw Z-stack data by building a summed-intensity projection of the 5 best-resolved 0 . 2 µm sections of the Z-stack ( usually the 5 middle sections ) . The intensity in the FITC channel along well-spread SCs was analyzed by using the Softworx Data Inspector tool . Three adjacent 0 . 2572×0 . 2572 µm square region-of-interest boxes were placed over the region of the SC to be analyzed in the summed-intensity projection . The total intensity of the region of interest ( ROI ) , in units of arbitrary fluorescence intensity , was recorded . The ROI for all intrinsic fluorescence measurements were three-dimensional volumes of 0 . 066 µm3 , calculated as follows: 1 pixel = 0 . 0643 µm; 4 pixels = 0 . 2527 µm ( length of side of 4×4 pixel box ) ; Area of box = ( 0 . 2572 µm ) 2 = 0 . 06615 µm2; Volume of a projected box ( 5 sections of 0 . 2 µm ) = ( 0 . 06615 µm2 ) ( 5 sections ) ( 0 . 2 µm/section ) = 0 . 066 µm3 ) . Monte Carlo simulations were done to assess the statistical significance of the distribution of Zip1-GFP foci , relative to Zip3-MYC foci , on 69 well-spread Zip1 linear stretches exhibiting Zip1-GFP foci whose largest dimension spanned 0 . 58 µm or less of the Zip1 stretch ( average size = 0 . 31 µm ) . Two distinct analyses were performed . First , we analyzed “adjacency” , defined as the presence of a Zip1-GFP focus directly adjacent to ( touching ) , partially overlapping , or completely encompassed by a Zip3 focus . In this case , the SC domain in which a Zip1-GFP focus would be considered adjacent to , partially overlapping with or encompassed by a Zip3 focus corresponds to the length of the Zip3-MYC focus plus half of the length of a Zip1-GFP focus on each side of the Zip3 focus . For the second analysis , we analyzed “complete overlap” , defined as Zip1-GFP completely encompassed by a Zip3-MYC focus . In this case , the SC domain in which a Zip1-GFP focus would be considered completely overlapping corresponds to the length of the Zip3-MYC focus minus half the length of a Zip1-GFP from the boundaries of that Zip3 focus . Both analyses followed a similar procedure . First , the size of Zip1-GFP is randomly sampled from the experimental data ( 117 distinct foci ) . For each of 69 Zip1 stretches of any given size , Zip1-GFP foci of that fixed size are randomly distributed , considering the observed coordinates of Zip3-MYC foci and limiting the allowed number of Zip1-GFP foci to the observed number of Zip1-GFP foci on a particular stretch ( i . e . if a given Zip1 stretch displayed three Zip1-GFP foci , each random distribution procedure will place three Zip1-GFP foci onto that stretch ) . For a generated random distribution , the total number of Zip1-GFP foci falling within the SC domain of “adjacency” or “complete overlap” on the 69 Zip1 stretches is calculated , out of a possible 117 . This random assignment of Zip1-GFP foci of the same fixed size on the 69 Zip1 stretches , constraining the allowed number of Zip1-GFP foci on a given Zip1 stretch to the observed number of Zip1-GFP foci on that stretch , is performed 1000 times . Next , a new Zip1-GFP size is randomly selected from the observed Zip1-GFP focus sizes , and the aforementioned steps are repeated . In total , 1000 random samplings of Zip1-GFP sizes are performed , bringing the total number of individual iterations to 1 , 000 , 000 . The observed number of adjacent Zip1-GFP foci ( 78/117 ) or completely encompassed ( 42/117 ) is compared to the number of iterations in which they were equal or greater , thus the reported p-value represents the fraction of random iterations in which the number of adjacent/encompassed Zip1-GFP was equal or superior to the observed frequency . Protein pellets were isolated by TCA precipitation using 10 mL of sporulating cell culture [15] . The final protein pellet was resuspended at a concentration of 10 µg/µl in 2× Laemmli sample buffer supplemented with 30 mM DTT . Protein samples were heated for 10 minutes at 65° , centrifuged at top speed and 150 µg was loaded onto an 8% polyacrylamide/SDS gel . Proteins were transferred to Whatman Protran nitrocellulose membrane . Rabbit anti-Zip1 antibody was used at 1∶2500 dilution and Alkaline Phosphatase-conjugated AffiniPure Donkey anti-Rabbit ( Jackson ImmunoReasearch ) was used at 1∶2500 dilution . Yeast genomic DNA was digested with BglII ( NEB ) overnight at 37°C and separated on a 0 . 8% agarose gel . Subsequent transfer to nitrocellulose membrane ( Roche ) was carried out by the alkali method described in Sambrook and Russell [47] . A 1 . 8 Kb PCR fragment containing the LEU2 gene was used as a probe against genomic DNA , using DIG High Prime DNA Labeling and Detection Starter Kit II ( Roche ) . Membrane-bound probe was visualized by incubating with an alkaline phosphatase-conjugated anti-DIG antibody followed by BCIP/NBT addition in alkaline phosphatase buffer . Genomic DNA without an integrated ZIP1-YFP plasmid gives a 2 . 8 Kb band whereas one , two , or three ZIP1-YFP integration events are predicted to result in a 12 . 7 Kb , 22 . 6 Kb , or 32 . 5 Kb band , respectively .
|
Sexually reproducing parents use meiosis to generate specialized cells in which chromosome sets are reduced from two to one . Accurate chromosome reduction relies on the prior establishment of pair-wise associations between homologous chromosomes ( homologs ) ; maintenance of paired associations typically occurs via interhomolog crossover recombination events . The proteinaceous , structurally conserved synaptonemal complex ( SC ) assembles along the full length of aligned homolog axes . The SC is the context in which crossover recombination events mature , and it influences both the level and distribution of crossover events between homologs . However it is not clear whether the SC maintains the capacity to remodel , for example to structurally accommodate recombination events . We explore the dynamics of budding yeast SC and find that full-length SCs exhibit ongoing subunit incorporation but little subunit turnover during a meiotic cell cycle arrest , thus SC grows over time . Interestingly , initial subunit incorporation into full-length SCs occurs predominantly at or adjacent to Zip3 foci , a presumed marker of crossover sites . Our observations suggest that budding yeast SC continues to assemble during a steady state and that , while it may have little capacity for global turnover after installation , the SC maintains differential assembly dynamics at recombination-associated perturbations in the meiotic axis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"biology"
] |
2012
|
Full-Length Synaptonemal Complex Grows Continuously during Meiotic Prophase in Budding Yeast
|
Helicobacter pylori ( H . pylori ) is the major risk factor for the development of gastric cancer . Our laboratory has reported that the Sonic Hedgehog ( Shh ) signaling pathway is an early response to infection that is fundamental to the initiation of H . pylori-induced gastritis . H . pylori also induces programmed death ligand 1 ( PD-L1 ) expression on gastric epithelial cells , yet the mechanism is unknown . We hypothesize that H . pylori-induced PD-L1 expression within the gastric epithelium is mediated by the Shh signaling pathway during infection . To identify the role of Shh signaling as a mediator of H . pylori-induced PD-L1 expression , human gastric organoids generated from either induced pluripotent stem cells ( HGOs ) or tissue ( huFGOs ) were microinjected with bacteria and treated with Hedgehog/Gli inhibitor GANT61 . Gastric epithelial monolayers generated from the huFGOs were also infected with H . pylori and treated with GANT61 to study the role of Hedgehog signaling as a mediator of induced PD-1 expression . A patient-derived organoid/autologous immune cell co-culture system infected with H . pylori and treated with PD-1 inhibitor ( PD-1Inh ) was developed to study the protective mechanism of PD-L1 in response to bacterial infection . H . pylori significantly increased PD-L1 expression in organoid cultures 48 hours post-infection when compared to uninfected controls . The mechanism was cytotoxic associated gene A ( CagA ) dependent . This response was blocked by pretreatment with GANT61 . Anti-PD-L1 treatment of H . pylori infected huFGOs , co-cultured with autologous patient cytotoxic T lymphocytes and dendritic cells , induced organoid death . H . pylori-induced PD-L1 expression is mediated by the Shh signaling pathway within the gastric epithelium . Cells infected with H . pylori that express PD-L1 may be protected from the immune response , creating premalignant lesions progressing to gastric cancer .
Helicobacter pylori ( H . pylori ) infects nearly 50% of the world's population and is the number one risk factor for gastric cancer [1] . Albeit a controversial issue , it may be that although H . pylori infection treated with antibiotics is cleared , once a patient has progressed to a metaplastic phenotype , elimination of the bacteria does not reduce the risk of developing gastric cancer [2] . H . pylori induces pathogenesis by injecting one key virulence factor cytotoxic associated gene A ( CagA ) into the gastric epithelial cells [3] . Importantly , CagA stimulates a drastic increase in Sonic Hedgehog ( Shh ) signaling from parietal cells , a response that is mediated by NFκB signaling [4 , 5] . Shh is a gastric morphogen known to initiate gastritis in response to H . pylori infection [4] . Upon infection H . pylori induces the secretion of Shh from the acid-secreting parietal cells [4] . Following a sustained increase in Shh secretion and signaling , macrophages are recruited to the infection site [4] . These macrophages secrete IL-1β which inhibits acid secretion causing atrophic gastritis and the atrophy of parietal cells [4 , 6] . Overall , Shh signaling plays a fundamental role in the initiation of H . pylori-induced gastritis [4 , 5] . It has also been observed that following H . pylori infection programmed death ligand 1 ( PD-L1 ) expression on the gastric epithelium is drastically increased [7] . The expression of PD-L1 in human gastric biopsies of infected patients has never been investigated . PD-L1 interacts with programmed death 1 ( PD1 ) on the surface of cytotoxic T lymphocytes ( CTLs ) rendering CTLs unable to induce apoptosis [8 , 9] . Thus , PD-L1 signaling induces cellular proliferation and survival [10 , 11] . H . pylori infection combined with the atrophy of the acid secreting parietal cells leads to the development of spasmolytic polypeptide/Trefoil Factor ( TFF ) 2-expressing metaplasia ( SPEM ) [12 , 13] . SPEM is the first step in a series of neoplastic changes that occur in the gastric epithelium prior to the development of gastric cancer [14 , 15] . In the setting of chronic inflammation and persistent bacterial infection there is the progression of SPEM to intestinal metaplasia and gastric cancer [15] . PD-L1 is a protective ligand that is known to suppress the immune system by shutting down T cell effector function [8 , 9] . Here we demonstrate that H . pylori-induced PD-L1 expression is mediated by Shh signaling as an early epithelial response to infection and a mechanism by which the bacteria evades the immune response . We also demonstrate here that SPEM cells may survive chronic inflammation by expressing the immunosuppressive ligand PD-L1 for the persistence of infection and progression of disease to cancer .
To determine whether H . pylori induces PD-L1 expression in the stomach , we first collected gastric biopsies from uninfected normal patients ( Fig 1A ) , and infected patients that exhibited metaplasia ( Fig 1B ) . Compared to the normal control patients ( Fig 1C ) , there was an increase in PD-L1 expression in response to H . pylori infection ( Fig 1D and 1E ) . PD-L1 expression within the infected stomach co-localized with SPEM glands that co-expressed Trefoil factor 2 ( TFF2 ) and CD44v9 [16 , 17] within the metaplastic epithelium ( Fig 1D and 1E ) . The effect of H . pylori infection on the gastric epithelium was then investigated using gastric organoids derived from human induced pluripotent stem cells ( HGOs ) ( Fig 1F–1K ) . PSC-derived HGOs are truly naïve gastric tissue that has never been exposed to any commensal or pathogenic bacteria . In addition , HGOs can be generated into regionally specific gastric organoids that have either fundic or antral epithelium thus allowing us to investigate the unique effects of the two different epithelia . Fundic/corpus ( FHGOs ) and antral ( AHGOs ) gastric organoids were infected with H . pylori for 72 hours . Histological evaluation revealed that compared to control ( Fig 1F ) FHGOs , there was the development of a dysplastic epithelium in response to H . pylori infection ( Fig 1F and 1K ) . Treatment of infected FHGOs with Hedgehog signaling inhibitor GANT61 , resulted in the inhibition of the development of dysplasia ( Fig 1H and 1K ) . FHGOs infected with a mutant G27 H . pylori strain bearing a CagA deletion ( ΔCagA ) did not exhibit that same morphological changes in the epithelium as that observed with the wild type G27 strain ( Fig 1J and 1K ) despite colonization of that both bacterial strains within the organoids ( Fig 3A–3F ) . While H . pylori infection also induced dysplasia in AHGOs , in contrast to FHGOs , GANT61 treatment did not inhibit this response ( Fig 2E–2J ) . Fig 2A–2D are representative images of the grading scale used to score the histology of infected HGOs . To identify whether the morphological changes observed in the HGOs in response to H . pylori infection were metaplastic changes , we immunostained sections prepared from organoids for gastric cancer stem cell and SPEM marker CD44v9 ( Fig 3G–3L ) . While CD44v9 was not expressed in either the control or ΔCagA infected FHGOs ( Fig 2G , 2H , 2K and 2L ) , there was a robust induction of this marker in FHGOs infected with H . pylori ( Fig 3I and 3J ) and this correlated with a significant increase in epithelial cell proliferation ( Fig 3M–3Q and 3W ) . The proliferative response was abrogated with GANT61 treatment of FHGOs ( Fig 3M–3Q and 3W ) . A similar response was observed in AHGOs infected with H . pylori , although GANT61 did not block the proliferation as seen in the FHGOs ( Fig 3R–3V and 3W ) . Acridine Orange is a dye known to show green fluorescence ( F488 ) at a neutral pH and a shift in the fluorescent spectrum to red ( F458 ) when it accumulates in the acidic organelles , as the secretory canaliculus of parietal cells [18] . Immunohistochemical staining revealed the clear presence of H+ , K+-ATPase positive parietal cells within the epithelium of FHGOs ( Fig 4A and 4B ) . Importantly , in response to histamine , Acridine Orange accumulated in cell vesicles as indicated by the increase in the shift in red fluorescence and increase in the ratio of F458 ( red ) /F488 ( green ) ( Fig 4C , 4D and 4F ) . AHGOs treated with histamine did not exhibit accumulation of Acridine Orange as documented by a lack in an increase in F458/F488 ratio ( Fig 4K ) . FHGOs infected with H . pylori for 24 hours also exhibited areas of acidic accumulation within the epithelium ( Fig 4E ) . Studies have demonstrated that Sonic Hedgehog ( Shh ) is found within the gastric parietal cells and processed from a 45kDa to a 19kDa bioactive protein via a mechanism that is acid- and protease-dependent [19–21] . Supported by previous findings , consistent with the expression and secretion of acid within FHGOs , there was a significant increase in the expression of Shh in response to H . pylori , that was not observed in infected AHGOs that were devoid of parietal cells ( Fig 4G–4I ) . The response was CagA dependent ( Fig 4G–4I ) . Collectively , these data demonstrate that Shh expression is induced in acid-secreting FHGOs by H . pylori infection . Immunofluorescence staining and western blot analysis of FHGOs for the expression of PD-L1 and co-expression of SPEM markers TFF2 and Griffonia Simplicifolia II ( GSII ) revealed increased PD-L1 expression within metaplastic glands of infected organoids ( Fig 5C and 5F ) . Treatment of infected FHGOs with GANT61 inhibited the PD-L1 expression that was triggered in response to H . pylori ( Fig 5D and 5F ) , when compared to control ( Fig 5A and 5F ) and GANT61 ( minus H . pylori ) ( Fig 5B , F ) treated groups . Organoids infected with the G27 H . pylori strain that expressed a deletion of CagA ( ΔCagA ) did not differ from the controls with regards to PD-L1 expression ( Fig 5E and 5F ) . In contrast to FHGOs , H . pylori infection did not induce PD-L1 expression as assessed by and western blot ( Fig 5F ) . Consistent with changes in protein expression , quantitative RT-PCR data showed a significant increase in PD-L1 , Shh and SPEM markers clusterin ( CLU ) and Human Epididymis Protein 4 ( HE4 ) gene expression specifically within the FHGO epithelium ( Fig 6A ) , when compared to AHGOs ( Fig 6B ) . Hedgehog signals are regulated based on the positive feedback loop via GLI1 and negative feedback loop via patched 1 ( PTCH1 ) , patched 2 ( PTCH2 ) , and hedgehog interacting protein ( HHIP ) [22] . We also observed a significant increase in canonical Hedgehog signaling within the FHGO epithelium ( Fig 6C ) . A response that was not observed in the AHGOs ( Fig 6D ) . Collectively , these data demonstrate that H . pylori-induced PD-L1 is localized to the fundic/corpus epithelium , and this response is mediated by Hedgehog signaling . To identify the mechanism by which H . pylori induces PD-L1 within the human gastric corpus epithelium , we developed a 2D/monolayer culture of H . pylori infection using human-derived gastric organoids . We first established fundic organoids derived from human stomachs ( huFGO ) . After 4 days of culture , huFGOs were transferred into 2D dense planar cultures of polarized epithelial cells according to a modification to a published protocol ( Fig 7A ) [23] . Forty eight hours after culture , membranes were collected and immunostained for surface mucous cell marker Ulex Europeus I ( UEAI ) to demonstrate apical expression of this pit cell marker in the polarized gastric cultures ( Fig 7B ) and parietal cell specific H+ , K+-ATPase ( Fig 7C ) . The monolayers expressed all major mature gastric cell lineages ( Fig 7D ) . As discussed , Acridine Orange is a dye that accumulates in the acidic organelles such as the secretory canaliculus of parietal cells leading to a fluorescence shift from green to red [18] . As observed in FHGOs , in response to histamine , Acridine Orange accumulated in acidic cell vesicles within gastric epithelial monolayers , and thus leading to the increase in a shift in red fluorescence and increase in the ratio of F458 ( red ) /F488 ( green ) ( Fig 8A , 8B and 8G ) . A similar response was observed in monolayers infected with H . pylori ( Fig 8C , 8D and 8G ) . Acid secretion was also induced in human-derived gastric organoids ( huFGOs ) in response to histamine ( Fig 8E and 8G ) . The accumulation of Acridine Orange was also observed within resident parietal cells of huFGOs in response to a 24 hour H . pylori infection ( Fig 8F ) . Stimulation of acid secretion in response to H . pylori infection , correlated with increased Shh expression within H+ , K+-ATPase positive parietal cells ( Fig 8H–8J ) . The induction of Shh was CagA dependent ( Fig 8J ) . Human gastric-derived monolayer cultures were infected with H . pylori with or without pretreatment with Hedgehog signaling Gli inhibitor GANT 61 ( Fig 9A–9D ) . We observed an increase in PD-L1 membrane-specific expression following H . pylori infection ( Fig 9B ) that was blocked with GANT 61 treatment ( Fig 9C ) . Quantitative RT-PCR confirmed the significant induction of PD-L1 and Shh expression in response to H . pylori infection , and this response was mediated by canonical Hedgehog signaling ( Fig 9E ) . This response was blocked by a second Hedgehog inhibitor vismodegib ( VIS ) , and appeared to by CagA dependent ( Fig 9E ) . Thus , our studies further demonstrate a role of Hedgehog signaling as a mediator of H . pylori-induced PD-L1 expression during early infection . Fig 10 demonstrates that the increase in PD-L1 expression in response to H . pylori infection was localized to GSII/TFF2 co-expressing cells within the monolayers ( Fig 10B ) . These data are consistent with the presence of SPEM markers as confirmed by the increase in CLU and HE4 in response to infection ( Fig 6E ) . Interestingly , treatment with GANT61 or VIS not only inhibited PD-L1 expression but also the increase in SPEM markers CLU and HE4 ( Fig 10E ) , suggesting a role of Hedgehog signaling in the emergence of metaplasia . To identify the cellular origin of Shh and PD-L1 expression in response to H . pylori infection , monolayers were infected with the bacteria for a period of 0 to 24 hours , harvested and analyzed by flow cytometry using markers specific for parietal ( H+ , K+-ATPase ) , mucous neck ( GSII ) and chief ( PgA ) cells ( Fig 10F–10M ) . Compared to the unstained controls ( Fig 10F ) , at baseline ( 0 hours ) there was no expression of Shh localized within parietal cells ( Fig 10G and 10K ) . However , over a period of a 24 hour infection Shh expression was induced within parietal cells ( Fig 10H–10K ) . H . pylori infection also induced increased GSII/PgA co-expressing cells ( Fig 10G–10J and 10L ) . As the mucous neck cells ( GSII-expressing ) migrate toward the base of the gastric gland , these cells differentiate into the zymogen/chief cells ( PgA-expressing ) [24] . Importantly , expansion of GSII/PgA co-expressing transitional cells is indicative of the development of SPEM [13 , 25] . In addition , PD-L1 was induced within GSII-expressing cells in response to H . pylori infection ( Fig 10G–10J and 10M ) . These data suggest that PD-L1 may be induced specifically within SPEM glands in response to H . pylori infection . PD-L1 interacts with programmed death 1 ( PD1 ) on the surface of cytotoxic T lymphocytes ( CTLs ) rendering them unable to induce apoptosis [9 , 26] . PD-L1 signaling induces cellular proliferation and survival [26] . To study PD-L1/PD-1 interactions between the gastric epithelium and the host's immune response during H . pylori infection , we developed an organoid/immune cell co-culture system ( Fig 11A ) . We obtained autologous patient blood from which dendritic cells were cultured and FACS sorted ( Fig 11B and 11C ) . From the blood , CTLs were also isolated and cultured together with the patient's own gastric organoids ( Fig 11A ) . After the organoid/immune cell co-culture was infected for 72 hours , CTLs were extracted from the culture by a CD8 positive selection kit . These T cells were analyzed for activation and proliferation by flow cytometry and CFSE uptake ( Fig 11D–11F ) . Within the co-culture , H . pylori significantly induced CTLs to express PD-1 , IL-2 and IFNγ ( Fig 11D ) . While H . pylori infection resulted in a decrease in CTL proliferation , treatment with PD-1Inh induced high CTL proliferation ( Fig 11E and 11F ) . Unselected cells were then immunostained for CD11c ( myeloid-derived dendritic cells ) and epithelial marker EpCAM . These cells were then FACs sorted and the EpCAM positive cells were collected and analyzed for PD-L1 expression and cell viability ( Fig 11G and 11H ) . HuFGOs infected with H . pylori had a significantly increased population of EpCam positive cells that expressed PD-L1 when compared to control uninfected huFGOs ( Fig 11H ) . These data suggest that while bacterial infection results in decreased CTL proliferation , inhibition of PD-L1/PD-1 interactions induced proliferation of CTLs within the co-culture in the presence of H . pylori infection . To investigate the interaction between the infected gastric epithelium and the host's immune response , infected organoids were co-cultured with the patient's DCs and CTLs in the presence and absence of a PD-1Inh and epithelial cell death was measured ( Fig 12 ) . Compared to the organoid/immune cell co-cultures in the control ( Fig 12A–12C ) or PD-1Inh alone ( Fig 12D–12F ) treatment , H . pylori infected organoids exhibited a significant increase in cell death with a concomitant increase in PD-L1+ve expressing epithelial cells ( Fig 12G–12I , 12S and 12T ) . However , a further significant increase in epithelial cell death was observed in co-cultures treated with PD-1I that was reflected by a decrease in PD-L1+ve expressing epithelial cells ( Fig 12J–12L , 12S and 12T ) . PD-1Inh alone without immune cells present had no effect on PD-L1 or organoid viability ( Fig 12M–12O , 12S and 12T ) . H . pylori alone , in the absence of immune cell in culture , continued to have a significant increase in epithelial cell death , however not to the extent as that observed in combination with immune cells and PD-1Inh ( Fig 12T ) . Importantly , H . pylori infection significantly induced PD-L1 expression in the absence of immune cells from the culture ( Fig 12S ) . The co-cultures confirm that H . pylori induces the expression of PD-L1 within the gastric epithelium . In addition , the decreased CTL effector function in response to bacterial infection is inhibited by the PD-1Inh leading to PD-L1 expressing epithelial cell death .
In the current study we show that PD-L1 is induced following initial H . pylori infection . Previous studies using primary gastric epithelial cells collected from biopsies of patients diagnosed with dyspepsia and gastric cancer cell lines showed an increase in PD-L1 expression following H . pylori infection [7 , 27] . The novelty of this study is the use of gastric organoids derived from patients . To the best of our knowledge we are the first to show induction of PD-L1 expression in human tissue and human derived-organoid models as an early response to bacterial infection . Importantly , induction of PD-L1 expression in gastric organoids and epithelial monolayers was not observed in response to infection with G27 H . pylori strain bearing a CagA deletion ( ΔCagA ) . The involvement of CagA in H . pylori-induced PD-L1 expression is significant because CagA is associated with an increased risk of developing gastric cancer [3 , 28] . Our data suggests a role of PD-L1 as a potential mechanism by which virulent strains of H . pylori allow for the persistence of infected gastric epithelial cells . Shh signaling mediates H . pylori-induced PD-L1 expression . Consistent with our findings , we have shown that H . pylori infection induces an increase in Shh secretion and signaling via a CagA dependent pathway [5] . We further demonstrate that canonical Shh downstream effectors were drastically increased specifically in the fundus/corpus of the stomach following H . pylori infection . Shh signaling in the epithelium of infected antral differentiated organoids was not observed . An explanation for this observation is data demonstrating that Shh is secreted from the acid-secreting parietal cells within the fundic region of the stomach [29 , 30] . Indeed by Acridine Orange accumulation we demonstrate here functional acid-secreting parietal cells with FHGOs , huFGOs and gastric epithelial monolayers . In the presence of GANT 61 , a Gli/Hedgehog signaling inhibitor , and vismodegib , that targets the Hedgehog signaling pathway by blocking Ptch and SMO , the expression of canonical SHH downstream effectors decreased . Interestingly , PD-L1 and Shh expression robustly increase in fundic organoids following infection with H . pylori , a response that was ablated with GANT 61 and vismodegib treatment . Parietal cells in this region secrete Shh which subsequently induces the secretion of acid , normal epithelial cell function and regeneration [29 , 31–33] . We advance our initial studies by demonstrating that the release of Shh within the corpus mediates the early induction of PD-L1 expression in response to bacterial infection . In support of our findings , it has been documented that Mycobacteria-responsive Shh signaling within human dendritic cells also mediates PD-L1 expression [34] . In biopsies collected from H . pylori infected patients , PD-L1 expression co-localized with proteins that classically mark SPEM cells including TFF2 and CD44v9 [16 , 17] . Different regions of the stomach respond differently to early transforming factors . For example , individuals most at risk of developing gastric cancer are those in whom the bacteria colonize the corpus ( or fundus ) of the stomach , when acid secretion is impaired . In contrast , bacterial colonization of the antrum is associated with low levels of inflammation in the corpus , high acid secretion and the development of duodenal ulcer disease [35–37] . Differences in the regional response to H . pylori infection is evident from our studies . The use of human PSC-derived antral and fundic gastric organoids has allowed us to identify how these unique regions of the human stomach differentially respond to H . pylori infection . To identify whether PD-L1 expression protects the epithelium from chronic inflammation , we developed an organoid/autologous immune cell co-culture system . Organoids infected with H . pylori highly expressed PD-L1 and suppressed CTL proliferation . CTLs are the main pro-apoptotic cell within the gastric cancer microenvironment [38] . When H . pylori infected organoids were co-cultured with CTLs and treated with a PD-1 inhibitor ( PD-1Inh ) there was an increase in proliferating CTLs and a decrease in live PD-L1 expressing gastric epithelial cells . Therefore , this suggests that PD-L1 expression was protective to the infected cells . These results are significant because once a patient progresses to a metaplastic state , the eradication of H . pylori does not decrease the risk of developing gastric cancer [2] . PD-L1 expression lasts through to the development of gastric cancer . Up to 69% of all gastric cancers express PD-L1 [39] . Here we present an organoid/immune cell co-culture to model infection with H . pylori and treatment with immune checkpoint inhibitors . From this study , we proposed that PD-L1 , that is induced by parietal cell-derived Shh , may be protective to SPEM cells in the presence of bacterial infection ( Fig 13A and 13B ) . When the interaction between PD-1 and PD-L1 is inhibited , activated CTLs may target the SPEM glands ( Fig 13C ) . These models could be used to devise a therapy for patients that have progressed to a metaplastic state and would therefore not benefit from eradication of H . pylori . In addition , this co-culture system could possibly be used to discover new therapies for gastric cancer .
Human gastric tissue and blood was collected during sleeve gastrectomies were specifically collected for this study with the approval of the Institutional Review Board ( IRB protocol number: 2014–0427 Helmrath , Cincinnati Children's Hospital Medical Center and 2015–4869 , Zavros , University of Cincinnati ) . All subjects provided informed written consent . A parent or guardian of any minor participant provided informed consent on their behalf . For generation of iPSC263_10 whole blood from a healthy blood donor was obtained from the CCHMC Cell Processing Core , Division of Experimental Hematology and Cancer Biology . Peripheral blood mononuclear cells ( PBMCs ) were isolated from whole blood using Ficoll centrifugation in SepMate tubes ( Stem Cell Technologies ) . PBMCs were then frozen in cryopreservation media ( 90% FCS + 10% DMSO ) until iPSC generation . PBMCs were thawed and 1-5x10e6 cells were primed for iPSC generation by culture in erythroid expansion media for 8 days ( EEM; StemCell Technologies ) . During priming , 1mL of fresh EEM was added to existing media every 2 days . At the completion of priming ( d0 ) , 1x10e6 cells were transduced for 3 h with recombinant VSV-G pseudotyped polycistronic lentiviral particles co-expressing reprogramming factors Oct4 , Klf4 , Sox2 , cMyc and dTomato ( Warlich et al . , 2011 ) in the presence of 8ug/mL polybrene . Transduced cells were then plated on 0 . 1% gelatin-coated dishes containing 2 x 10e4 irradiated MEFs/cm2 ( GlobalStem ) in 2mL EEM . On d2 , 1 mL fresh EEM was added to wells . On days 3 and 5 , 1 mL hESC media ( DMEM:F12 containing 20% knockout serum replacement , 1 mM L-glutamine , 0 . 1 mM β-mercaptoethanol , 1x non-essential amino acids , and 4ng/mL bFGF ) was added to the existing media in each well . Starting on d5 , wells underwent a complete daily media change with 2 . 5 mL hESC media . Putative iPSC colonies were then manually excised and replated in feeder free culture conditions consisting of matrigel ( BD BioSciences ) and mTeSR1 ( Stem Cell Technologies ) . Lines exhibiting robust proliferation and maintenance of stereotypical human pluripotent stem cell morphology were then expanded and cryopreserved at ~ passage 10 [40 , 41] . Donor material for preparation of iPSCs was demonstrated mycoplasma-free using the MycoAlert kit ( Lonza; LT07-118 ) . The assay was performed exactly as recommended by the manufacturer and included the use of a positive control ( Lonza; LT07-518 ) . Human stomach was digested to glands and embedded into MatrigelTM following a published protocol [16 , 42] . Briefly , the epithelium was dissociated from the muscle layer , finely minced and washed in sterile PBS without Ca2+ and Mg2+ supplemented with 1% Penicillin/Streptomycin . Epithelial tissue was further digested in DMEM/F12 ( 1263–010 , Gibco Life Technologies ) containing collagenase A ( from Clostridium histolyticum , Sigma C9891 , 1 mg/ml ) and bovine serum albumin ( 2 mg/ mL ) for 15–30 min to liberate glands from tissue . The reaction was stopped using DMEM/F12 ( 1263–010 , Gibco Life Technologies ) supplemented with Kanamycin ( 50 mg/ml ) and Amphotericin B ( 0 . 25 mg/ml ) /Gentamicin ( 10 mg/ml ) , and the glands were filtered through sterile gauze and allowed to settle on ice for 10 mins . Glands were washed with PBS supplemented with Kanamycin ( 50 mg/ml ) and Amphotericin B ( 0 . 25 mg/ml ) /Gentamicin ( 10 mg/ml ) and suspended in MatrigelTM . Organoids were plated at a density of 50 μL/well and cultured in 3D human gastric organoid media ( DMEM/F12 supplemented with 10 mM HEPES , 1X Glutamax , 1% Pen/Strep , 1X N2 , 1X B27 , 1 mM N-Acetylcystine , 10 mM Nicotidamide , 50 ng/mL Epidermal Growth Factor ( EGF ) , 100 ng/mL Noggin , 20% R-Spondin Conditioned Media , 50% Wnt Conditioned Media , 200 ng/mL FGF10 , 1 nM Gastrin , 10uM Y-27632 , Kanamycin ( 50 mg/ml ) and Amphotericin B ( 0 . 25 mg/ml ) /Gentamicin ( 10 mg/ml ) ) . Following 6–7 days 3D organoids grew from glands . Following this time 3D organoids were infected with H . pylori or transferred to 2D organoid monolayers . Human-derived gastric epithelial monolayers were prepared according to a modified published protocol [23] . Organoids were harvested form MatrigelTM using cold PBS . Organoids were suspended in 2D media containing ( DMEM/F12 supplemented with 10% Fetal Calf Serum , 10 mM HEPES , 2 mM GlutaMAX , 1% Pen/Strep , 1X N2 , 1X B27 , 10 mM Nicotidamide , 50 ng/mL EGF , 10 nM Y-27632 , 1 nM Gastrin , 50 mg/ml Kanamycin and 1 μM TGF-βI ) and plated onto MatrigelTM coated plates . Briefly , MatrigelTM was diluted tenfold into cell culture grade water and allowed to coat 2 well chamber slides or 12 well plates at 37°C for 1 hour . Excess water was removed from the plate and MatrigelTM coating was allowed to dry for 1 hour at room temperature . H . pylori strain G27 [43 , 44] and ΔCagA strain , a mutant strain of G27 bearing a CagA deletion ( ΔcagA::cat ) [45] , were grown on blood agar plates containing a Columbia Agar base ( Fisher Scientific ) containing 5% horse blood ( Colorado Serum Company ) , 5 μg/ml vancomycin and 10 μg/ml trimethoprim as previously described [4 , 5] . HGOs cultured for 32 days and HuFGOs cultured for 7 days were injected with 200 μL of Brucella broth containing approximately 2X105 bacteria using a Nanoject II ( Drummond ) microinjector . Gastric epithelial monolayers cultured for 4 days were infected with 50 μL of DMEM/F12 ( 1263–010 , Gibco Life Technologies ) containing 5-8million bacteria . Whole blood was collected from young sleeve gastrectomy patients ( 15–21 years old ) . The SepmateTM tubes ( Stemcell ) and LymphoprepTM ( Stemcell ) were used to separate out red blood cells and platelets according to manufacturer’s protocol . Briefly , 50 mL SepmateTM tubes were filled at the bottom with 15 mL of LymphoprepTM . Whole blood was diluted with phosphate-buffered saline containing 2% fetal bovine serum . Diluted whole blood was added to the tube containing LymphoprepTM . The tubes were centrifuged at 1200 g for 10 minutes . Following this supernatant was poured into a separate tube . The supernatant was diluted with phosphate-buffered saline containing 2% fetal bovine serum . The supernatant was centrifuged at 300 g for 8 minutes . The supernatant was discarded , and the pellet was re-suspended in phosphate-buffered saline containing 2% fetal bovine serum . The pellet was centrifuged at 120 g for 10 minutes . The resulting peripheral blood mononuclear cells were cultured in dendritic cell media or put through the negative selection EasySepTM Human CD8+ T cell Enrichment Kit ( Stemcell ) . PBMCs were matured into dendritic cells using a published protocol [46] . PBMCs are cultured in dendritic base media . Briefly , AIM V cell culture media ( Invitrogen ) is supplemented with 10% human serum albumin ( Gemini BioScience ) , β-mercaptoethanol ( 50μM ) , 1% Penicillin/Streptomycin , 0 . 1% amphotericin B , 800 U/mL GM-CSF ( LifeTechmologies ) , 500 U/mL IL-4 ( LifeTechnologies ) . After three days cells were fed with dendritic base media . On day 5 immature dendritic cells were fed with dendritic base media supplemented with 5 ng/mL TNF ( Life Technologies ) , 5 ng/mL IL-1β ( Life Technologies ) , 150 ng/mL IL-6 ( Life Technologies ) , and 1 μg/mL prostaglandin E2 ( PGE2; Life Technologies ) . On day 6 mature dendritic cells were shorted by fluorescence-activated cell sorting ( FACs ) for the expression of HLA-DR ( Biolegend ) . On day 7 FACs sorted mature dendritic cells were co-cultured with control or H . pylori huFGOs . CD8+ T Cells were extracted from PBMCs isolated from whole blood using the EasySepTM Human CD8+ T cell Enrichment according to manufacturer’s protocol . Briefly , PBMCs were suspended in EasySepTM buffer ( Cell Separation Buffer ) ( Stemcell ) in a 14mL round bottom centrifuge tube ( Corning ) . 50 μL/mL of Enrichment Cocktail was added to PBMCs and allowed to incubate at room temperature for 10 minutes . Magnetic particles were mixed by vortexing for 30 seconds . 150 μL/mL of magnetic particles were added to the PBMCs and allowed to incubate for 5 minutes at room temperature . The PBMC cocktail was topped up to 5 mL using EasySepTM Buffer . The PBMC cocktail was added to “The Big Easy” magnet ( Stemcell ) and allowed to incubate at room temperature for 5 minutes . The CD8+ T cells are the cells that have no bound magnets . These were poured into a fresh 15 mL conical and centrifuged at 1200 rpm for 5 minutes and plated in T cell media containing RPMI 1640 ( Invitrogen ) , 10% fetal calf serum , β-mercaptoethanol ( 50 μM ) , 1% Pennecillin/Streptomycin , 1% Insulin-tellurium-selenium ( Thermofisher ) , IL-2 ( 30 U/mL ) ( Thermofisher ) and IL-7 ( 0 . 5 ng/mL ) ( Thermofisher ) [9] . HuFGOs were harvested from MatrigelTM with cold DMEM/F12 and centrifuging the organoid suspension at 400 g for 5 minutes . CD8+ T cells were harvested and centrifuged at 300 g for 5 minutes . CTLs were suspended in a 5 μM Carboxyfluorescein succinimidyl ester ( CFSE ) for 20 minutes at 37°C . Following this cells were washed with DPBS and centrifuged at 300 g for 5 minutes . CTLs were then incubated in huFGO full media for 10 minutes at 37°C . CTLs were then centrifuged at 300 g for 5 minutes . Mature dendritic cells were centrifuged at 300 g for 5 minutes . HuFGOs , CD8+ T cells and dendritic cells were suspended in MatrigelTM and plated in 4 well plate . HuFGOs were injected with 200 nL of Brucella broth containing approximately 2*105 bacteria using a Nanoject II ( Drummond ) microinjector . One well of uninfected huFGOs co-cultured with CD8+ T Cells and dendritic cells and one well of H . pylori infected huFGOs co-cultured with CD8+ T Cells and dendritic cells were treated with Nivolumab ( A2002 , Selleckchem ) , a PD-1 inhibitor . Cells were co-cultured for 5 days . Tissue slides were hydrated with ethanol , xylenes and water . Slides were then blocked with 20% donkey serum at room temperature for one hour and incubated with primary antibodies for PD-L1 ( Rat , Novus , 1:100 dilution ) , TFF2 ( Rabbit , 1:200 dilution ) or CD44v9 ( rat , CosmoBio , 1:1000 dilution ) , PD-L1 ( Rabbit , Novus , 1:100 dilution ) or GSII ( ThermoFisher , 1:100 dilution ) overnight at 4°C . Slides were washed in 0 . 01% triton x-100 in PBS and treated with a secondary for donkey anti-rat 488 , anti-rabbit 647 or donkey anti-rat 488 , donkey anti-rabbit 555 and GSII 647 as well ( Hoechst 33342 , 10 μg/ml , Invitrogen ) . Media was removed from HGOs , 2D organoid monolayers or huFGO co-cultures with immune cells and 3 . 7% formaldehyde was added to the organoids for 15 minutes at room temperature . The cultures were washed with PBS and then permeabilized with 0 . 5% Triton X-100 in DPBS for 20 minutes at room temperature . Blocking was done with 2% normal donkey serum for 1 hour at room temperature . Monolayer cultures were then incubated overnight at 4°C with primary antibodies specific for H+/K+ ATPase ( Thermofisher , mouse , 1:1000 dilution ) and E-cadherin ( R&D , goat , 1:400 dilution ) . HGOs and monolayers were incubated overnight at 4°C with PD-L1 ( Rat , Novus , 1:100 dilution ) and TFF2 ( Rabbit , 1:100 dilution ) or HK ( mouse , thermofisher , 1:1000 dilution ) and Sonic Hedgehog ( Goat , Novus , 1:200 dilution ) . Monolayers were incubated for 1 hour at room temperature with secondary antibodies donkey anti-mouse 594 , UEAI ( Sigma , 488 ) , donkey anti-goat 647 or donkey anti-mouse 555 and donkey anti-goat 647 and counter stained with ( Hoechst 33342 , 10 μg/ml , Invitrogen ) . Monolayers and HGOs were incubated for 1 hour at room temperature with Griffonia simplicifolia ( GSII ) ( Thermofisher , 488 , 1:100 dilution ) , anti-rat 594 , anti-rabbit 647 and counter stained with ( Hoechst 33342 , 10 μg/ml , Invitrogen ) . huFGOs co-cultured with immune cells were incubated overnight at 4°C with primary antibodies specific for CD8a ( Mouse , Novus ) , CD11c ( Rabbit , Novus ) and E-cadherin ( R&D , Goat ) . Organoids were then treated with secondary antibodies anti-mouse 594 , anti-rabbit 488 or anti-goat 647 and counter stained with ( Hoechst 33342 , 10 μg/ml , Invitrogen ) for 1 hour at room temperature . Organoids were visualized using the Zeiss LSM710 . Organoids were fixed in 4% paraformaldehyde for 15 minutes . They were then embedded in paraffin and cut into 5 μM sections . Slides were then deparaffinized and antigen retrieval was done by heating slides for 10 minutes at 100°C in 0 . 01 M sodium citrate buffer ( Antigen Unmasking Solution , Vector Laboratories , Burlingame , CA ) . Endogenous peroxide activity was then blocked by incubating slides with 0 . 3% hydrogen peroxide in methanol for 20 minutes . Slides were then incubated with 20% horse serum ( PCNA , Helicobacter pylori and CD44v9 ) ( ImmPRESS HRP reagent kit , Vector ) or 20% goat serum ( H+/K+ ATPase ) . Slides were then incubated with a 1:2000 dilution of PCNA ( rabbit , Novus ) , 1:1000 dilution of H+/K+ ATPase ( mouse , Thermofisher ) or 1:1000 dilution of CD44v9 ( rat , CosmoBio ) overnight at 4°C . Helicobacter pylori ( Rabbit , Ventana ) stained slides were incubated with the pre-diluted antibody for 28 minutes at 37°C . Slides were then biotinylated with an IgG secondary antibody for either rabbit , mouse or rat for 30 minutes at room temperature . Finally slides were incubated with ABC reagent ( Vectastain ABC kit; Vector Laboratories , Burlingame , CA ) for 30 minutes at room temperature . The color of each set of slides was then developed with 3 , 3’-diaminobenzidine ( DAB ) from the DAB Substrate Kit ( Vector Laboratories , Burlingame , CA ) . The slides were counterstained with hematoxylin ( Fisher Scientific Company , Kalamazoo , MI ) , dehydrated and mounted with Permount . Dye was added to the culture and monitored on the Zeiss LSM710 microscope . Helicobacter pylori was added to the medium of the monolayers at a concentration of 50 μL of DMEM/F12 ( 1263–010 , Gibco Life Technologies ) containing 5–8 million bacteria . Histamine was added to the medium of 3D huFGOs , iPSC-derived HGO or 2D gastric epithelial monolayers at a concentration of 6 . 67 mM ( Sigma Aldrich ) . Images were analyzed using the Zeiss LSM710 Microscope and background corrected 550-620/620-700 nm ratio values were converted to fold change corresponding to pH change using Prisim Graph Pad software . Organoids and monolayers were harvested in cold DMEM/F12 and lysed in M-PER Mammalian Protein Extraction Reagent ( Thermofisher ) supplemented with protease inhibitors ( Roche ) according to the manufacturer’s protocol . Cell lysates were suspended in 40 μL of Laemmli Loading Buffer containing β-mercaptoethanol ( BioRad ) . Samples containing 20 μg of protein were then loaded onto 1 4–20% Tris-Glycine Gradient Gels ( Invitrogen ) and run at 120 V for 1 . 5 hours before transferring the protein onto nitrocellulose membranes ( Whatman Protran , 0 . 45 μM ) at 105 V for 1 . 5 hours at 4°C . Membranes were blocked for 1 hour at 23°C using KPL Detector Block Solution ( Kirkegaard & Perry Laboratories , Inc . ) . Next membranes were incubated overnight at 4°C with a 1:1000 dilution of anti-PD-L1 ( Novus , NBP1-76769 ) a 1:1000 dilution of anti-Shh ( Novus , AF464 ) or 1:2000 dilution of anti-GAPDH ( Millipore , MAB374 ) . The membranes were washed 3 times for 5 minutes each . Following this , the membranes were incubated with a 1:1000 dilution anti-mouse , 1:1000 dilution anti-goat or anti-rabbit Alexa Fluor 680 ( Invitrogen ) . The blots were then imaged using a scanning densitometer along with analysis software ( Odyssey Infrared Imaging Software System ) . Total RNA was isolated from tissue , glands , 3D organoids , HGOs and 2D organoid monolayers using TRIzol ( Life Technologies ) according to manufacturer’s protocol . A High Capacity cDNA Reverse Transcription Kit synthesized cDNA from 100 ng of RNA following protocol provided by Applied Biosystems . Real-time PCR assays were utilized for the following genes in HGOs and 2D organoid monolayers: GAPDH ( Hs02786624_g1 ) , PD-L1 ( Hs01125296_m1 ) , SHH ( Hs00179843_m1 ) , TFF2 ( Hs00193719_m1 ) , Clustrin ( Hs00156548_m1 ) , and HE4 ( Hs00899484_m1 ) . Cell lineage markers were determined in tissue , glands , hFGOs , 2D monolayers fundic and antral HGOs by RT-PCR for Mucin 5AC ( Hs01365616_m1 ) , Mucin 6 ( Hs01674026_g1 ) , H+/K+ ATPase ATP4B ( Hs01026288_m1 ) , Pepsinogen C ( Hs00160052_m1 ) Pepsinogen A ( Hs05416800_g1 ) and Mist 1 ( Hs00703572_s1 ) . PCR amplifications were done with a pre-validated 20X TaqMan Expression Assay primers and a 2X TaqMan Universal Master Mix ( Applied Biosystems ) and a cDNA template in a total volume of 20 μL . Amplifications were performed in duplicate wells in a StepOne Real-Time PCR System ( Applied Biosystems ) . Fold change was calculated at ( Ct-Ct high ) = n target , 2ntarget/2nGAPDEH = fold change where Ct = threshold cycle . The media was removed from huFGO co-cultured with immune cells and infected or uninfected with H . pylori . The cultures were treated with accutase for 10 minutes at 37o C . The organoids were then passed through a 27 1/8-gauge syringe in order to dissociate the organoids into single cells . CTLs were extracted using the EasySep Human CD8 Positive Selection Kit II ( STEMCELL , 17853 ) . Breifly , cells were incubated with 100 μL/mL of sample selection cocktail and incubated at room temperature for 3 minutes . 50 μL/mL of sample RapidSpheres were added to the sample and incubated at room temperature for 3 minutes . Samples were topped up to 5 mL with EasySep Buffer ( STEMCELL , 20144 ) and incubated at room temperature for 3 minutes . Cells that did not bind to the magnetic beads were collected in a 50 mL conical . Samples were wash two more times with 5 mL of Easy Sep Buffer ( STEMCELL , 20144 ) . Cells that were adherent to magnetic beads were CTLs and were collected and centrifuged at 300 g for 5 minutes . Cells that did not bind to the magnetic beads were DCs and epithelial cells . These cells were also centrifuged at 300 g for 5 minutes . Fluoresence Assisted Cell Sorting was using to collect epithelial cells from the epithelial cell/DC mixture . This cell mixture was stained with EpCam ( Biolegend ) and CD11c ( Biolegend ) . EpCam positive cells were collected during sorted and CD11c cells were disgarded . EpCam positive cells were suspended in 100 μL of a 1:1000 dilution of the zombie red cocktail ( BioLegend ) . 1 μL of a 1:1000 dilution of the calcein violet cocktail ( BioLegend ) was added to this cell suspension . The cells were incubated in this cocktail for 20 minutes at room temperature . The cells were then washed with 1 mL of 5% BSA at 300 g for 5 minutes . The cells were then suspended in 100 μL of 5% BSA , treated with 1 μL of anti-PD-L1 ( BioLegend ) and incubated for 15 minutes at room temperature . The cell suspension was then incubated at room temperature for 15 minutes with 100 μL of Reagent A ( Thermofisher ) . The cells were then washed with 1 mL of 5% bovine serum albumin at 300 g for 5 minutes . CTLs that were extracted from culture were suspended in 100 μL of 5% BSA , treated with 1 μL of anti-CD8 ( BioLegend ) and anti-PD-1 ( BioLegend ) and incubated for 15 minutes at room temperature . The cells were then incubated at room temperature for 15 minutes with 100 μL of Reagent A ( Thermofisher ) . The cells were then washed with 1 mL of 5% bovine serum albumin at 300 g for 5 minutes . The cells were suspended in 100 μL of Reagent B ( Thermofisher ) and 1 μL of anti-IL2 and 1 μL of anti-IFN-γ were added to the cells . This cocktail was incubated at room temperature for 20 minutes . The cells were washed with 1 mL of 5% bovine serum albumin at 300 g for 5 minutes . All cells were then suspended in 500 μL of 5% bovine serum albumin . Samples were run on the CANTO 3 and analyzed by FlowJo data analysis . The significance of the results was tested by one-way ANOVA , two-way ANOVA or student’s t-test using commercially available software ( GraphPad Prism , GraphPad Software , San Diego , CA ) . A P value <0 . 05 was considered significant .
|
Gastric cancer is the 5th most common cancer worldwide and the 3rd most common cause of cancer-related death . Helicobacter pylori ( H . pylori ) infection is the major risk factor for the development of gastric cancer . Our laboratory has reported that the Sonic Hedgehog ( Shh ) signaling pathway is an early response of the gastric epithelium to infection and fundamental to the initiation of H . pylori-induced gastritis . H . pylori also induces programmed death ligand 1 ( PD-L1 ) expression on gastric epithelial cells , yet the mechanism is unknown . PD-L1 is a protective ligand that is known to suppress the immune system by shutting down T cell effector function . We hypothesized that H . pylori-induced PD-L1 expression within the gastric epithelium is mediated by the Shh signaling pathway during infection . Moreover , we showed that metaplastic cells may survive chronic inflammation by expressing the immunosuppressive ligand PD-L1 for the persistence of infection and progression of disease to cancer .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"blood",
"cells",
"organoids",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"biological",
"cultures",
"immunology",
"microbiology",
"epithelial",
"cells",
"helicobacter",
"parietal",
"cells",
"gastroenterology",
"and",
"hepatology",
"organ",
"cultures",
"bacteria",
"bacterial",
"pathogens",
"research",
"and",
"analysis",
"methods",
"white",
"blood",
"cells",
"animal",
"cells",
"medical",
"microbiology",
"microbial",
"pathogens",
"t",
"cells",
"biological",
"tissue",
"gastrointestinal",
"infections",
"hedgehog",
"signaling",
"signal",
"transduction",
"helicobacter",
"pylori",
"cell",
"biology",
"anatomy",
"epithelium",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"cell",
"signaling",
"organisms"
] |
2019
|
Increased Programmed Death-Ligand 1 is an Early Epithelial Cell Response to Helicobacter pylori Infection
|
Mycobacterium tuberculosis ( Mtb ) survives under oxidatively hostile environments encountered inside host phagocytes . To protect itself from oxidative stress , Mtb produces millimolar concentrations of mycothiol ( MSH ) , which functions as a major cytoplasmic redox buffer . Here , we introduce a novel system for real-time imaging of mycothiol redox potential ( EMSH ) within Mtb cells during infection . We demonstrate that coupling of Mtb MSH-dependent oxidoreductase ( mycoredoxin-1; Mrx1 ) to redox-sensitive GFP ( roGFP2; Mrx1-roGFP2 ) allowed measurement of dynamic changes in intramycobacterial EMSH with unprecedented sensitivity and specificity . Using Mrx1-roGFP2 , we report the first quantitative measurements of EMSH in diverse mycobacterial species , genetic mutants , and drug-resistant patient isolates . These cellular studies reveal , for the first time , that the environment inside macrophages and sub-vacuolar compartments induces heterogeneity in EMSH of the Mtb population . Further application of this new biosensor demonstrates that treatment of Mtb infected macrophage with anti-tuberculosis ( TB ) drugs induces oxidative shift in EMSH , suggesting that the intramacrophage milieu and antibiotics cooperatively disrupt the MSH homeostasis to exert efficient Mtb killing . Lastly , we analyze the membrane integrity of Mtb cells with varied EMSH during infection and show that subpopulation with higher EMSH are susceptible to clinically relevant antibiotics , whereas lower EMSH promotes antibiotic tolerance . Together , these data suggest the importance of MSH redox signaling in modulating mycobacterial survival following treatment with anti-TB drugs . We anticipate that Mrx1-roGFP2 will be a major contributor to our understanding of redox biology of Mtb and will lead to novel strategies to target redox metabolism for controlling Mtb persistence .
It is estimated that nearly 2 billion people currently suffer from latent Mycobacterium tuberculosis ( Mtb ) infection and ∼1 . 4 million people succumb to tuberculosis ( TB ) annually [1] and [www . who . int/tb] . The ability of Mtb to adapt and resist killing by the immune system facilitates its survival , replication , and persistence . Following aerosol exposure , Mtb is engulfed by macrophages , and exposed to antimicrobial redox stresses including reactive oxygen and nitrogen species ( ROS and RNS ) [2] . Mycobacterial killing by ROS and RNS is important for host resistance as demonstrated by the increased susceptibility of NADPH oxidase ( NOX2 ) and nitric oxide synthase ( iNOS ) deficient mice following Mtb challenge [3] , [4] . Moreover , children with defective NOX2 suffer from chronic granulomatous disease , are susceptible to TB and even develop severe complications after vaccination with BCG [5] . Consistent with these observations , a recent study elegantly demonstrated the requirement of NADPH oxidase in exerting neutrophil-mediated killing of mycobacteria during infection [6] . In response to oxido-reductive stress , Mtb upregulates multiple redox sensing pathways , including SigH/RshA , DosR/S/T , MosR , and the WhiB family , to maintain redox homeostasis [7]–[12] . Furthermore , modified cell wall lipids [13] , antioxidant enzymes , and cellular redox buffers , such as MSH , thioredoxins ( TRXs ) , and ergothionine ( ERG ) , assist Mtb in mitigating redox stress [14] . Collectively , these studies indicate that dynamic reprogramming of intrabacterial redox metabolism in response to host environment is vital for the persistence of Mtb . Despite its recognized importance , tools for monitoring changes in redox state of Mtb during infection do not exist . Customary approaches involving NAD+/NADH and MSH/MSSM measurements require cell disruption , which precludes real-time analyses , and are plagued by oxidation artifacts . Alternatively , redox-sensitive dyes which are commonly used to detect ROS generation in cells , suffer from non-specificity , irreversibility , and these dyes cannot deliver information regarding the redox potential of a specific redox couple [15] , [16] . Therefore , development of a specific , sensitive , and non-invasive technology to study defined redox changes in Mtb would contribute significantly to delineating novel redox pathways involved in persistence , drug tolerance , as well as pathogenesis of Mtb , and may also have utility in high throughput screens to identify small-molecule modulators of intrabacterial redox homeostasis . Genetically encoded reduction-oxidation sensitive GFP indicators ( roGFPs ) have been developed to measure the intracellular glutathione ( GSH ) redox potential ( EGSH ) through interaction with glutaredoxins ( Grxs ) in many organisms [17] . However , the preference of roGFPs for GSH limits their use in non-GSH producers like gram positive bacteria ( e . g . Bacillus species , Staphylococcus aureus , Deinococcus radiodurans ) and actinomycetes ( e . g . Mycobacteria , Corynebacteria , Streptomyces , Nocardia ) , which respectively contain bacillithiol ( BSH ) and MSH as their major redox buffers [18] , [19] . Because MSH is functionally analogous to GSH and plays a prominent role in maintaining the reduced state of the mycobacterial cytoplasm [20] , [21] , we engineered roGFP2 to generate a MSH-specific intracellular probe , Mrx1-roGFP2 . Importantly , Mrx1-roGFP2 allows imaging of EMSH in diverse mycobacterial species and strains , including drug-resistant clinical isolates during infection . Lastly , we examine the potential of antibiotics in inducing intramycobacterial oxidative stress in the physiological context of infection and demonstrate the functional importance of MSH redox signaling in intramacrophage survival and sensitivity to anti-TB drugs . Our study provides an elegant tool to probe redox biology of Mtb under diverse environmental conditions including in vivo experimental models of TB .
We selected roGFP2 as a fluorescent partner in the biosensor construct because it exhibits the largest dynamic range , it is brighter , pH insensitive , and is resistant to photoswitching [22] . The oxidation of two cysteines on either side of roGFP2 chromophore ( S147C and Q204C ) [22] generates a disulfide bond and increases the fluorescence intensity at ∼400 nm with concomitant decrease at ∼490 nm , while reduction reverses the spectrum . The 400/490 nm ratio thus reports the redox state of the cell or compartment in which it is expressed [22] . Because the sensor is ratiometric it eliminates errors due to variations in roGFP2 concentrations during different growth phases of an organism . Conventional roGFP2 predominantly equilibrates with the cytosolic glutathione redox buffer through interaction with endogenous glutaredoxins [23] . However , the response kinetics of roGFP2 was slow and absolute specificity towards GSH/GSSG redox couple cannot be guaranteed [17] , [24] . To resolve this , roGFP2 bioprobe was recently fused to human glutaredoxin 1 ( Grx1; Grx1-roGFP2 ) , which ensured complete specificity and rapid equilibration with intracellular GSH/GSSG couple [24] . On this basis , we explored the concept of covalently coupling roGFP2 to a mycothiol-specific oxidoreductase such as mycoredoxin ( Mrx1 ) to generate a biosensor ( Mrx1-roGFP2 ) that exclusively responds to perturbations in mycothiol redox potential ( EMSH ) . The mechanistic basis of coupling Mrx1 to roGFP2 for measuring EMSH is depicted in Figure 1 . Recently , a glutaredoxin ( Grx1 ) homologue ( mycoredoxin-1; Mrx1 ) which exclusively interacts with the mycothiol redox system has been reported in a non-pathogenic saprophytic mycobacteria , Mycobacterium smegmatis ( Msm ) [25] . We performed homology based analysis and identified three putative Mrx1 like proteins in Mtb H37Rv . Out of the three proteins ( Rv3053c , Rv0508 , and Rv3198A ) , Rv3198A demonstrate highest similarity with Mrx1 of Msm ( 72% identity ) . Based on this , we selected Rv3198A ORF as a putative mycoredoxin-encoding gene and named its product as Mtb Mrx1 . The Mtb Mrx1 contains an active site ( CGYC ) similar to Msm Mrx1 , supposedly required for thiol-disulfide exchange activity [25] . We independently replaced the two cysteine ( Cys ) residues present in the catalytic site of Mrx1 by alanine to generate Mrx1 ( CGYA ) and Mrx1 ( AGYC ) . The wt Mrx1 along with its Cys variants were then separately fused to the N-terminus of roGFP2 via a 30-amino acid linker , ( Gly-Gly-Ser-Gly-Gly ) 6 . The resulting chimeras were affinity purified as His-tagged proteins and analyzed by spectrofluorometry . We observed that Mrx1-roGFP2 exhibits two distinct excitation peaks ( 390 nm and 490 nm ) at a fixed emission wavelength of 510 nm . Therefore , in all subsequent experiments using spectrofluorometer the excitation ratio from these two wavelengths ( 390 and 490 nm ) was measured to determine the extent of biosensor oxidation . Our analysis demonstrated that the intrinsic ratiometric changes exhibited by roGFP2 upon oxidation or reduction were recapitulated in Mrx1-roGFP2 ( Figure S1A ) . In addition , we monitored the response of Mrx1-roGFP2 over a physiologically relevant pH range ( pH 5 . 5–8 . 5 ) and found that the fluorescence excitation ratio exhibited by Mrx1-roGFP2 was insensitive to pH variations ( Figure S1B ) . We also verified that the reported midpoint potential ( −280 mV ) and dynamic range ( −320 mV to −240 mV ) of roGFP2 were not influenced by the fusion ( Figure S1C ) . Since redox stress leads to an increase in oxidized mycothiol ( MSSM ) concentrations [26] , we anticipated that the Mrx1 fusion would enable roGFP2 to selectively monitor this transformation . To test this proposal , we reduced uncoupled roGFP2 , Mrx1-roGFP2 , Mrx1 ( CGYA ) -roGFP2 , and Mrx1 ( AGYC ) -roGFP2 and examined their oxidation by MSSM . Only Mrx1-roGFP2 ( Lane 2 , Figure 2A ) and Mrx1 ( CGYA ) -roGFP2 ( Lane 4 , Figure 2A ) ratios increased upon MSSM addition , whereas uncoupled roGFP2 ( Lane 1 , Figure 2A ) and Mrx1 ( AGYC ) -roGFP2 ( Lane 3 , Figure 2A ) remained non-responsive . This suggests that the Mrx1-roGFP2 reaction mechanism is similar to monothiol mechanism of glutaredoxins , wherein nucleophilic N-terminus cysteine interacts with GSSG to form mixed protein-glutathione disulfide intermediate followed by protein oxidation [24] . Importantly , Mrx1-roGFP2 did not respond to other disulfide-based compounds such as cystine ( Cys2 ) , GSSG , or 2-hydroxyethyl disulfide ( HED ) , thus confirming the specificity of this biosensor towards MSSM ( Figure 2B ) . Next , we examined the response of Mrx1-roGFP2 to reduced mycothiol ( MSH ) . To continuously maintain a reduced state of MSH in our assays , we used MSH disulfide reductase ( Mtr ) enzyme . Mtr is known to catalyze the NADPH-dependent reduction of MSSM to MSH in the mycobacterial cells [27] . We first confirmed the activity of purified Mtr by monitoring NADPH oxidation in the presence of MSSM . A time-dependent decrease in 340 nm absorption due to NADPH consumption confirmed cycling of electrons from NADPH to MSSM by Mtr ( Figure S1D ) . Next , oxidized Mrx1-roGFP2 was added as substrate for the MSH/Mtr/NADPH electron transfer assay as depicted in Figure 2C . By monitoring the decrease in the 390/490 excitation ratio , we examined the real-time response of oxidized Mrx1-roGFP2 to MSH generated via NADPH-dependent reduction of MSSM by Mtr . As shown in Figure 2D , a time-dependent decrease in 390/490 ratio confirms reduction of Mrx1-roGFP2 by MSH . The slow response of this biosensor towards MSH is consistent with the low catalytic turnover rate of Mtr [27] . No response was observed if either MSH was omitted from the Mtr/NADPH/Mrx1-roGFP2 mixture ( Figure 2D ) or catalytically inactive Mrx1 ( AGYC ) -roGFP2 ( Lane 3 , Figure 2E ) or uncoupled roGFP2 ( Lane 1 , Figure 2E ) were used as substrates , whereas a response was readily detected in the case of Mrx1 ( CGYA ) -roGFP2 ( Lane 4 , Figure 2E ) . Finally , to measure the sensitivity of the biosensor , we incubated pre-reduced Mrx1-roGFP2 with various ratios of MSH/MSSM at a physiological concentration ( 1 mM total ) under anaerobic conditions and the ratiometric response was monitored . We found that a small increase in MSSM led to a significant increase in biosensor oxidation . For example , an increase in the amount of mycothiol oxidation ( OxDMSH , see Materials and Methods for a mathematical explanation ) from 0 . 00001 to 0 . 0001 ( i . e an ∼100 nM increase in absolute MSSM ) led to a larger increase in the biosensor oxidation ( i . e from ∼40% to 90% ) ( Figure 2F ) . These results confirm that Mrx1-roGFP2 is capable of rapidly sensing nanomolar changes in MSSM against the backdrop of a highly reduced MSH pool ( 1 mM ) . Lastly , uncoupled roGFP2 remained completely non-responsive to changes in MSH/MSSM ratios ( Figure 2F ) . Together , our results show that Mrx1-roGFP2 is exceptionally sensitive to measure physiological and dynamic changes in MSH/MSSM redox state . To investigate the redox responsiveness of Mrx1-roGFP2 in mycobacteria , we stably expressed Mrx1-roGFP2 in Msm . Next , we confirmed that the biosensor responds ratiometrically upon exposure of Msm to diamide or dithiothreitol ( DTT ) ( Figure 2G ) . To examine if Mrx1-roGFP2 senses EMSH in vivo , we pharmacologically and genetically perturbed MSH levels in Msm . We first analyzed the response of Mrx1-roGFP2 upon depletion of the cytosolic MSH pool in Msm using dequalinium [an established small-molecule inhibitor of MSH ligase ( MshC ) [28]] . For comparison , we also tested inhibitors of TRX reductase ( cisplatin ) [29] and dihydrolipoamide dehydrogenase ( 5-methoxyindole-2-carboxylic acid ) [30] , both of which do not affect the cellular MSH pool . As expected , only dequalinium treatment led to a substantial increase in the fluorescence excitation ratio of Mrx1-roGFP2 ( Figure 2H ) . We further validated the MSH-specific response of Mrx1-roGFP2 by expressing it in MSH-negative ( MsmΔmshA ) and MSH-depleted ( MsmΔmshD ) strains of Msm [31] , [32] . In addition to MSH , mycobacteria express the NADPH-dependent TRX system to efficiently counter oxidative stress [33] . Importantly , multiple components of the TRX system were down-regulated in mycobacterial strains lacking extracytoplasmic sigma factor , SigH [33] , [34] . Therefore , to rule out the interaction of Mrx1-roGFP2 with the TRX system inside mycobacteria , we analyzed the biosensor response in SigH-deleted strain of Msm ( MsmΔsigH ) . Oxidation of the biosensor was nearly quantitative ( ∼95%±3 oxidized ) in MsmΔmshA as compared to ∼20%±2 in both wt Msm and MsmΔsigH , and ∼50%±4 in MsmΔmshD ( Figure 2I ) . Since MsmΔmshD contains only 1% to 3% of total cellular MSH but produces two related thiols ( Suc-mycothiol and formyl-mycothiol ) [35] , our data suggest that Mrx-1 can facilitate roGFP2 reduction with Suc-MSH and/or formyl-MSH , albeit suboptimally . A previous study reported a marked accumulation of ERG in MsmΔmshA [36] . Therefore , we also examined if Mrx1-roGFP2 can function as a sensor of ERG in mycobacteria . However , Mrx1-roGFP2 did not respond to ERG in vitro , further supporting mycothiol-specific response of Mrx1-roGFP2 ( Figure S2A ) . Taken together , data generated from several independent techniques demonstrate that Mrx1-roGFP2 responds to the MSH redox buffer in mycobacteria . Specific equilibration of Mrx1-roGFP2 with the MSH redox buffer enables precise measurement of the EMSH in various strains of Msm using the Nernst equation as described in SI Materials and Methods . Our studies reveal EMSH in wt Msm , MsmΔmshA , MsmΔmshD , and MsmΔsigH to be −300±2 mV , −239±7 mV , −275±7 mV , and −300±3 mV , respectively . Notably , the oxidizing EMSH values observed for MsmΔmshA and MsmΔmshD are consistent with our earlier findings indicating that the thiol-disulfide redox switch in Mrx1-roGFP2 is a specific substrate for the MSH reductive pathway . We next investigated whether coupling of roGFP2 with Mrx1 enhanced the sensitivity of the new biosensor towards transient changes in intracellular EMSH . Initial studies confirmed that H2O2 alone ( 0 . 5–5 mM ) does not directly oxidize the Mrx1-roGFP2 protein in vitro ( Figure S2B ) . By contrast , application of H2O2 to cells led to rapid ( ∼2 min ) and substantial oxidation of the biosensor within Msm ( Figure 3A ) . These results suggest that H2O2–mediated oxidation of MSH to MSSM is necessary for biosensor oxidation in Msm . Also , Mrx1-roGFP2 ratio rapidly increased upon exposure of Msm to diverse oxidants such as menadione , aldrithiol , and diamide ( Figure S2C ) . The sensitivity of Mrx1-roGFP2 in Msm was further investigated upon exposure to lower concentrations of H2O2 . Addition of 100 µM , 500 μM and 1 mM of H2O2 resulted in rapid , but short-lived ( ∼5 min ) increases in Mrx1-roGFP2 ratio , suggesting efficient mobilization of anti-oxidant response mechanisms in Msm ( Figure 3B ) . In contrast , Msm expressing either uncoupled roGFP2 ( Figure 3C ) or Mrx1 ( AGYC ) -roGFP2 ( Figure 3D ) responded slowly to lower concentrations of H2O2 and did not display an anti-oxidative response . The poor response shown by uncoupled roGFP2 and Mrx1 ( AGYC ) -roGFP2 could either be due to their non-specific interaction with other mycobacterial redox systems ( e . g . TRX , ERG etc ) or suboptimal equilibration with the MSH/MSSM couple through mediation by endogenous Msm Mrx1 . Finally , Mrx1 ( CGYA ) -roGFP2 retained transient responses similar to Mrx1-roGFP2 ( Figure 3D ) , further substantiating the crucial role of N-terminal Cys residue of Mrx1 in promoting a rapid and reversible equilibration of biosensor with the intracellular MSH/MSSM redox buffer . To decisively show that Mrx1-roGFP2 is capable of detecting dynamic changes in EMSH , we exploited another Msm mutant lacking MSH disulfide reductase ( Mtr ) activity ( MsmΔmtr ) . The Mtr enzyme maintains intramycobacterial MSSM/MSH ratios by reducing MSSM to MSH upon oxidative stress , consequently its absence results in the depletion of MSH [37] . Exposure to H2O2 led to a ∼2-fold increase in the ratio of oxidized Mrx1-roGFP2 in MsmΔmtr as compared to wt Msm ( Figure 3E ) . Importantly , the short-lived oxidative deflections of EMSH in response to limiting amounts of H2O2 were significantly extended in the MsmΔmtr mutant , thereby implicating Mtr in orchestrating an efficient anti-oxidative response in Msm ( Figure 3F ) . On this basis , we propose a biochemical mechanism of sensing mycobacterial EMSH using Mrx1-roGFP2 bioprobe ( Figure 3G ) . It can be argued that the overexpression of a redox-based enzyme in our biosensor can influence cytoplasmic MSH/MSSM redox state to compromise redox measurements . However , we found that the absolute concentration of Mrx1-roGFP2 inside Msm cells is 1000–10000 fold lower ( 1 µM/cell; Figure S2D ) as compared to the high millimolar concentrations of mycothiol ( 1–10 mM ) present in mycobacteria . This shows that reducing equivalents ( thiols ) introduced by the expression of biosensor are significantly less than the combined pool of MSH and other thiols present in mycobacteria . Furthermore , ambient EMSH of Msm cells overexpressing either Mrx1-roGFP2 or its catalytically inactive derivative [Mrx1 ( AGYC ) -roGFP2] was found to be comparable i . e −300±2 mV and −298±3 mV , respectively , indicating that Mrx1 activity does not perturb steady state EMSH . Lastly , Mrx1-roGFP2 harboring Msm , MsmΔmshA , and MsmΔmtr strains showed similar survival profile upon exposure to H2O2 as compared to control strains , suggesting no adverse influence of intracellular levels of biosensor on resistance to oxidative stress ( Figure S2E ) . Taken together , we demonstrate that by integrating Mrx1 with roGFP2 , the biosensor becomes catalytically self-sufficient in establishing a rapid and specific equilibration with the MSH redox buffer . To obtain information about the basal redox potential differences between various species and strains of mycobacteria , we expressed Mrx1-roGFP2 in vaccine strain ( M . bovis BCG ) , virulent laboratory strain ( Mtb H37Rv ) , and several Indian clinical isolates of Mtb including single-drug resistant ( BND 320 ) , multi-drug resistant ( MDR - Jal 2261 , 1934 , Jal 2287 ) , and extensively-drug resistant ( XDR - MYC 431 ) . First , we confirmed that overexpression of Mrx1-roGFP2 does not affect metabolic activity and growth of Mtb using metabolic indicator dye , Alamar blue and by measuring culture absorbance ( Figure S3A and S3B ) . Any downstream imaging analysis of the BSL3 class pathogens requires their chemical fixation by paraformaldehyde ( PFA ) , which we found to oxidize the biosensor ( Figure S3C ) . To circumvent PFA-mediated oxidation artifacts , we alkylated the thiols of Mrx1-roGFP2 using the cell permeable fast-acting thiol-modifier , N-ethyl maleimide ( NEM ) . Control experiments clearly show that NEM treatment efficiently prevents oxidation during PFA-fixation of Mtb cells ( Figure S3C ) . A similar chemical-fixation strategy was successfully exploited to measure the redox potential of glutathione ( EGSH ) in HeLa cells [24] , and in the sub-cellular compartments and tissues of Drosophila using Grx1-roGFP2 [38] . With this system in hand , we confirmed that Mrx1-roGFP2 responds ratiometrically to oxidant ( cumene hydroperoxide; CHP ) and reductant ( DTT ) in Mtb H37Rv ( Figure S4A ) . A concentration and time-dependent oxidation of Mrx1-roGFP2 upon H2O2 exposure was also detected in Mtb H37Rv ( Figure S4B and S4C ) . Of note , induction of anti-oxidative response upon exposure to H2O2 was significantly delayed in Mtb ( ∼120 min ) as compared to rapid response observed earlier in Msm ( ∼5 min ) ( Figure S4C ) , suggesting important variations in sensing and responding to redox stress between the two species . Next , we evaluated the redox potential of various slow growing lab-adapted and clinical mycobacterial strains . The resulting data indicates that there is relatively little variation in the redox state within and between drug-resistant clinical ( MDR/XDR ) and drug-sensitive lab ( Mtb H37Rv , M bovis BCG ) strains , as exemplified by EMSH values around −273 mV to −280 mV ( Table S1 ) . This finding suggests that the steady-state EMSH is relatively unaffected by either genotypic or phenotypic variations within Mtb strains under laboratory growth conditions . However , EMSH in slow growing mycobacterial strains is notably oxidizing compared to Msm ( −300±2 mV ) , which is consistent with an earlier report showing higher MSH/MSSM ratio in Msm ( 200∶1 ) as compared to BCG ( 50∶1 ) [39] . Lastly , to rule out any contribution of the chemical fixation procedure to the observed variation in EMSH between Mtb and Msm , we treated Msm expressing Mrx1-roGFP2 with NEM-PFA and confirmed that Msm maintains EMSH of −300±2 mV . We next determined whether Mrx1-roGFP2 could be used to quantify redox changes that occur in the natural context of infection . To investigate this issue , we infected THP-1 macrophages with Mtb H37Rv expressing Mrx1-roGFP2 at a multiplicity of infection ( moi ) of 10 and monitored intramycobacterial EMSH . To do this , we performed NEM-PFA based fixation technique followed by ratiometric fluorescence analysis by flow cytometry ( see SI Materials and Methods ) . Since the flow cytometric based measurements are dependent on fixed wavelength lasers , we excited Mrx1-roGFP2 biosensor with the canonical 405 and 488 nm laser wavelengths at a fixed emission wavelength of 510 nm ( see SI Materials and Methods ) . We first confirmed that intramycobacterial Mrx1-roGFP2 responds ratiometrically to oxidant; CHP and reductant; DTT inside macrophages ( Figure 4A , 4B , 4C , and 4D ) . To measure changes in EMSH during infection , an in vitro redox calibration curve was generated by treating Mtb H37Rv with buffers of known redox potentials . By fitting Mrx1-roGFP2 ratio to the redox calibration curve , we precisely calculated the EMSH of Mtb inside macrophages ( Figure S5A , see SI Materials and Methods ) . Intriguingly , flow cytometric analyses of ∼30 , 000 infected macrophages demonstrated the presence of cells with a gradient of intramycobacterial EMSH . For the purpose of measurements , infected macrophages were gated into three subpopulations on the basis of their corresponding intramycobacterial EMSH ( Figure 4E and 4F ) . An EMSH-basal population with an intermediate EMSH of −275±5 mV , and two deflected populations were observed ( Figure 4E and 4F ) . Deflected cells with a mean EMSH of −240±3 mV represent an EMSH-oxidized subpopulation , based on the observation that CHP treatment of infected macrophages results in a significant fraction of these gated cells ( ∼98% ) ( Figure 4E and 4F ) . The population with an average EMSH of −300±6 mV represents an EMSH-reduced subpopulation , as treatment of infected macrophages with the DTT results in ∼96% of the cells gating into this subpopulation ( Figure 4E and 4F ) . Mtb cells present in media alone and analyzed in parallel did not show redox heterogeneity ( Figure 4G and 4H ) , suggesting that the intramacrophage environment perturbs redox homeostasis to induce redox variability in Mtb . Furthermore , we infected macrophages with BCG , and measured intramycobacterial EMSH with and without NEM-PFA treatment . Both conditions induce similar degree of heterogeneity in intramycobacterial EMSH ( Figure 4I , 4J , and 4K ) , demonstrating that redox heterogeneity has a biological basis and is not due to aberrant quenching of fluorescent signals during NEM-PFA treatment . With the flow cytometry workflow in hand , we measured time-resolved changes in intramycobacterial EMSH during infection of THP-1 macrophages . Our results indicated that the initial period ( 0–24 h post-infection [p . i . ] ) of infection was associated with a gradual increase in cells with reduced EMSH ( 60±7% ) followed by an oxidative shift ( 25±5% ) at 48 h p . i . and then a significant recovery from oxidative stress , as revealed by a decrease in the population with oxidized EMSH ( 7±3% ) at 72 h p . i . ( Figure 5A ) . The observed redox heterogeneity and oscillatory patterns were confirmed by repeating experiments at least six times in quadruplicate and data from biologically independent experiments were combined and presented as mean ± standard deviation . We also verified the presence of both time-dependent heterogeneity and oscillations in intramycobacterial EMSH upon infection of THP-1 macrophages at a low moi of 1 ( Figure S5B ) . However , we noticed that infection with lower moi induced higher proportion of bacteria with oxidized EMSH at each time point examined as compared to cells infected at a moi of 10 ( Figure S5B and S5C ) . Next , we investigated if the macrophage environment induces strain-specific variations in redox heterogeneity among various slow growing mycobacteria including BCG and Indian clinical MDR/XDR isolates . For this , we infected THP-1 macrophages with various strains of Mtb at a moi of 10 and time-resolved changes in intramycobacterial EMSH were measured as described in the earlier section . Interestingly , while heterogeneity in EMSH for BND 320 largely followed the reductive-oxidative-reductive oscillatory pattern of H37Rv ( Figure 5B ) , distinct redox deviations were displayed by other strains . For example , Jal 2287 , Jal 2261 and 1934 displayed overrepresentation of the EMSH-oxidized subpopulation at 48 h p . i . followed by a poor recovery at 48–72 h p . i . , as compared to H37Rv ( Figure 5C–5E ) . Noticeably , intramacrophage growth of MYC 431 displayed a loss of redox oscillatory pattern and showed a steady increase in EMSH-oxidized subpopulation over 48 h p . i . ( Figure 5F ) . Intriguingly , intramacrophage profile of BCG displayed temporal changes in EMSH comparable to Jal 2261 , 1934 , and MYC 431 . As shown in Figure 5G , infection with BCG showed a continuous decrease in EMSH-reduced subpopulation with a concomitant increase in EMSH-oxidized subpopulation over time ( Figure 5G ) . Together , these findings for the first time revealed that macrophage environment triggers heterogeneity in EMSH of Mtb and uncovered redox variance among clinical field isolates . In order to examine the biosensor response upon stimulation of oxidant-mediated antimycobacterial stresses , we performed additional experiments in immunologically activated murine macrophages ( RAW 264 . 7 ) . Activated murine macrophages are known to control mycobacterial proliferation by producing ROS and RNS [2] . RAW 264 . 7 were activated with IFN-γ and LPS prior to infection with Mtb H37Rv [11] . Ratiometric flow cytomteric analysis showed a significant and sustained oxidative shift in EMSH of Mtb H37Rv inside activated macrophages at each time point investigated , whereas intramycobacterial EMSH inside naïve macrophages showed redox oscillations similar to THP-1 cells ( Figure 5H and 5I ) . These results indicate that Mtb cells were able to recover from mild oxidative stress conditions inside naïve macrophages , whereas recovery was compromised in IFN-γ/LPS primed macrophages . Since nitric oxide ( NO ) generated via iNOS is considered to be one of the major contributors of redox stress in Mtb inside immune-activated murine macrophages [40] , we treated IFN-γ/LPS activated RAW 264 . 7 macrophages with a well established iNOS inhibitor NG-methyl-L-arginine ( NMLA;[41] ) and monitored intramycobacterial EMSH . Strikingly , a substantial reduction in subpopulation with oxidized EMSH was observed upon addition of NMLA ( Figure S6A ) . These results indicate that Mtb responds to host derived environmental cues by modulating EMSH , and further illustrates the utility of Mrx1-roGFP2 in dissecting redox signaling during infection . Intracellular Mtb exists in different vacuolar compartments , which may contribute to significant heterogeneity in mycobacterial gene expression , metabolic state and survival [42] . On this basis , we next asked whether trafficking into distinct vacuolar compartments could promote redox heterogeneity in the Mtb population using ratiometric confocal microscopy . First , we performed confocal imaging of Mtb cells inside THP-1 macrophages at 24 h p . i . Conforming to our flow cytometric findings , confocal analyses revealed that the intrabacterial EMSH varied markedly at a single-cell level . Similar to flow cytometry , this gradient in redox heterogeneity can be classified into EMSH-basal ( −277±5 mV , 26% ) , EMSH-oxidized ( −242±6 mV , 23% ) , and EMSH-reduced ( −304±10 mV , 51% ) sub-populations ( Figure 6A and 6B ) . Mtb grown in media indicated an overrepresentation of the cells with uniform EMSH ( Figure S6B ) , validating that both flow cytometry and confocal imaging revealed very similar ratiometric changes upon infection . Next , we measured intrabacterial EMSH within early endosomes , lysosomes , and autophagosomes by visualizing the co-localization of Mtb H37Rv expressing Mrx1-roGFP2 with compartment specific fluorescent markers at 24 h p . i . using confocal microscopy ( see SI Materials and Methods ) . For marking early endosomes , cells were stained with anti- early endosome autoantigen ( EEA1 ) and anti-Rab5 antibodies . To study lysosomes , we used acidotropic dye Lysotracker and anti-cathepsin D antibody , whereas autophagosomes were labeled with anti-LC3 antibody ( see SI Materials and Methods ) . The resulting data show that the majority of bacilli within early endosomes were likely to exhibit reduced ( ∼54% ) as compared to oxidized ( ∼22% ) or basal ( ∼24% ) EMSH ( Figure 6C and Figure S7A ) . Interestingly , in lysosomes , deflected subpopulations with EMSH-oxidized were clearly higher in proportion ( 58% ) , whereas EMSH-reduced ( 12% ) and EMSH-basal ( 30% ) subpopulations were underrepresented ( Figure 6D and Figure S7B ) . The percent distribution of subpopulations with EMSH-reduced and EMSH-oxidized were significantly different between early endosomes and lysosomes ( p<0 . 001 ) , while EMSH-basal subpopulation remained comparable within these compartments . Furthermore , 100% of the Mtb population inside autophagosomes displayed a maximal oxidative shift in EMSH ( Figure 6E ) . We also measured changes in EMSH during intramacrophage residence of drug-resistant strains Jal 2287 and MYC 431 . While Jal 2287 displayed redox deviations similar to Mtb H37Rv ( Figure 7A–7D ) , MYC 431 showed over-representation of subpopulations with EMSH-oxidized within the macrophage and sub-vacuolar compartments at 24 h p . i . ( Figure 7E–7H ) . Taken together , our results suggest that distinct sub-vacuolar environments lead to the generation of Mtb subpopulations with a gradient of redox potentials . Several studies have shown that MSH-deficient mycobacteria and other actinomycetes are sensitive to antibiotics [21] , [43] , [44] . On the other hand , MSH also contributes to susceptibility to INH and ETH [45] , and mutations in mycothiol biosynthesis genes were identified in drug-resistant clinical isolates of Mtb [46] . While these constitute an important foundation linking antibiotic action with mycothiol redox homeostasis in vitro , they provide little insight into how antibiotics modulate intramycobacterial EMSH in the physiological setting of infection . To examine this , we characterized the effect of anti-TB drugs on redox heterogeneity in Mtb cells during intramacrophage residence . To this end , infected macrophages were exposed to anti-TB drugs ( 5-fold the in vitro MIC ) with different modes of action ( e . g . isoniazid [INH; mycolic acid inhibition] , ethambutol [EMB; arabinogalactan inhibition] , rifampicin [RIF; inhibition of transcription] , and clofazimine [CFZ; redox cycling and ROS production; [47] ) and the redox response was measured by flow cytometry . Treatment with all antibiotics induced variable levels of oxidative shift in EMSH of Mtb subpopulations at 12 , 24 , and 48 h p . i . ( Figure 8A ) . The skew towards oxidizing EMSH was activated at an early time point ( 12 h p . i . ) and increased significantly at 48 h p . i . ( Figure 8A ) . Next , we examined if enhanced oxidizing EMSH correlated with the killing potential of anti-TB drugs during infection . At 12 h post-antibiotic treatment , the Mtb survival rate was comparable to the untreated control , as determined by colony forming unit ( CFU ) assay ( Figure 8B ) . However , a modest ( ∼1 . 5-fold ) to a significant reduction ( ∼5-fold ) in intramacrophage bacillary load as compared to untreated control was detected at 24 and 48 h p . i . , respectively ( Figure 8B ) . These findings show that bactericidal antibiotics with different mechanisms of action induce oxidative changes in intramycobacterial EMSH during infection . To further validate these results , we infected macrophages with INH resistant clinical strains ( BND 320 and Jal 2287 ) and measured intramycobacterial EMSH in response to INH at 48 h p . i . Since these strains are sensitive to CFZ , we used CFZ as a positive control in this experiment . Figure 8C clearly shows that the EMSH of strains genetically resistant to INH remained uninfluenced in response to INH . On the other hand , CFZ exposure generated considerable oxidative shift in EMSH of these strains ( Figure 8C ) . Lastly , to investigate if anti-TB drugs directly induce oxidative stress in vitro , we exposed Mtb cells grown in 7H9 medium supplemented with albumin , dextrose and sodium chloride ( 7H9-ADS ) to INH , CFZ , RIF , and ETH ( 5× MIC ) and tracked EMSH at 1 , 2 , 6 , and 24 h post-treatment . This study revealed that only CFZ induces significant oxidative shift in EMSH of Mtb ( Figure 8D ) . Other anti-TB drugs such as INH induce low levels of EMSH-oxidized at 24 h , post-treatment , while RIF and ETH do not influence the EMSH of Mtb ( Figure 8D ) . Our results suggest that antibiotics do not perturb EMSH of Mtb per se , but co-opt host cellular responses to stimulate excessive oxidative stress during infection . These findings underscore the importance of studying redox-based mechanisms of drugs action under physiologically relevant microenvironmental conditions such as those encountered during TB infection in macrophage . It has been suggested that long term anti-TB therapy is required because the mycobacterial population is functionally heterogeneous and harbors cells that are differentially sensitive to antibiotics [48] . However , the physiological determinants of phenotypic heterogeneity in Mtb population and its relation with antibiotic tolerance remains poorly characterized . Because macrophage environment quickly creates variability in mycobacterial cells to generate drug tolerant subpopulations [49] , we hypothesize that heterogeneity in intrabacterial EMSH may be one of the factors that underlies emergence of Mtb populations with differential antibiotic susceptibility . We therefore sought to determine the susceptibility of Mtb cells with basal , oxidized , and reduced EMSH to antibiotics during infection of THP-1 cells . To do this , we analyzed the membrane integrity of Mtb cells expressing biosensor by assessing their capacity to exclude fluorescent nucleic-acid binding dye , propidium iodide ( Pi ) , upon treatment with antibiotics during infection . The state of the bacterial membrane is a crucial physiological indicator , as Pi+ cells are considered damaged or dying [50] . Importantly , we found that NEM treatment of infected macrophages fixed the redox state of intracellular Mtb such that bacterial cells released from macrophages retained redox variations comparable to bacteria within macrophages ( Figure S8 ) . This allowed us to quantify bacterial viability by Pi staining of Mtb cells released from infected macrophages at various time points post antibiotic exposure . Infected THP-1 cells were exposed to anti-TB drugs ( 5× MIC ) and intracellular bacteria were fixed with NEM at 12 , 24 , and 48 h p . i . Infected macrophages were lysed; released bacteria were stained with Pi , and ∼30 , 000 bacilli were analyzed by multi-parameter flow cytometry to simultaneously profile EMSH and viability status . As shown earlier , all antibiotics induce significant oxidative shift in intramycobacterial EMSH during infection . Furthermore , bacilli with oxidized EMSH were more sensitive to killing as evident by a time-dependent increase in Pi staining across all antibiotic treatments within this subpopulation as compared to other two subpopulations ( Figure 9A , p<0 . 05 ) . At 48 h p . i . , 35–40% of EMSH-oxidized bacilli were Pi+ . The EMSH-basal subpopulation demonstrates a modest increase in Pi+ staining ( ∼5–10% ) at 24 and 48 h p . i . ( Figure 9A ) . Surprisingly , EMSH-reduced subpopulation remained completely unaffected by antibiotics as shown by the absence of Pi+ cells within this group ( Figure 9A ) . These results show that bacteria with lower EMSH are capable of excluding Pi and therefore maintain membrane integrity post-antibiotic treatment . Thus , we find that redox heterogeneous bacteria vary in their susceptibility to antibiotics , consistent with the model that macrophage induced heterogeneity in intrabacterial EMSH creates physiologically distinct subpopulations of cells . Since environment inside autophagosomes induces substantial oxidative shift in intrabacterial EMSH , we hypothesized that treatment of infected macrophages with a well established autophagy inducer ( rapamycin ) would increase the localization of Mtb to autophagosomes and shift redox heterogeneity towards EMSH-oxidized . This would allow us to examine the influence of host antibacterial mechanisms ( i . e autophagy ) on intrabacterial EMSH and drug tolerance during infection . To examine this , infected macrophages were treated with a low non-lethal concentration of rapamycin ( 200 nM ) and antibiotic-mediated redox stress and killing was monitored by assessing EMSH and Pi status . As shown in figure 9B , treatment of infected macrophages with rapamycin significantly increases fraction of Mtb cells exhibiting oxidizing EMSH as compared to untreated control ( P<0 . 05 ) . Furthermore exposure of infected macrophages to both rapamycin and antibiotics ( INH or CFZ ) induces oxidative shift in EMSH which supersedes that produced by either INH or CFZ or rapamycin alone ( Figure 9B , p<0 . 05 ) . Consistent with this , treatment of infected macrophages with rapamycin-INH or rapamycin-CFZ substantially increases the fraction of Pi+ Mtb cells , indicating augmented bacterial death by these combinations ( Figure 9C , p<0 . 05 ) . Lastly , to show that EMSH-reduced subpopulation contributes to drug tolerance , we measured the sensitivity of Mtb towards anti-TB drugs in the presence of reducing agent DTT in vitro . First , we confirmed that exogenous addition of 5 mM DTT maintained a reductive EMSH ( −320±2 . 9 mV ) equivalent to the EMSH of reducing subpopulation and is non-deleterious for Mtb . In the absence of DTT treatment , antibiotics exposure led to a significant reduction ( 6–12-fold ) in the survival of Mtb ( Figure 9D ) . By contrast , ∼80% of Mtb survived an exposure to INH or ETH and ∼50% in the case of CFZ in the presence of DTT ( Figure 9D ) . Collectively , our data suggest that oxidized EMSH potentiates antibiotic action , whereas EMSH-reduced promotes tolerance to anti-TB drugs and suggest that host-induced cell-to-cell variation in EMSH may be a novel mechanism by which Mtb resists antimicrobial treatment during infection .
Basic research on persistence and drug-tolerance in Mtb is hampered by the lack of tools to study bacterial physiology during infection . Here , we developed a new biosensor , Mrx1-roGFP2 , to image dynamic changes in the EMSH of Mtb during infection . Using confocal and flow cytometry , we quantified EMSH and unraveled mycothiol-linked redox heterogeneity in Mtb at the single-cell level during infection and highlighted the utility of bioprobes in exploring new mechanisms of drug action in Mtb bacilli . In Mrx1-roGFP2 , the genetic coupling of roGFP2 with the mycothiol-specific oxidoreductase ( Mrx1 ) ensures that roGFP2 functions as a physiological substrate for Mrx1 and therefore dynamically oxidizes and reduces in response to EMSH . Since cytoplasmic levels of chromosomally encoded Mrx1 may differ between mycobacterial species/strains or under diverse environmental conditions , one of the main advantages of coupling Mrx1 with roGFP2 is to facilitate rapid and continuous equilibration of biosensor with MSH/MSSM redox couple independent of endogenous Mrx1 pool . Our results agree with a recent study which showed that the homologue of Mtb Mrx1 in Msm specifically interacts with the mycothiol redox system in vitro [25] . Related non-disruptive approaches have been employed to develop GSH-specific and H2O2-specific in vivo bioprobes [24] , [51] . The utility of Mrx1-roGFP2 in investigating mycobacterial physiology during infection comes from our flow cytometric and confocal data showing that the environment inside macrophage induces redox heterogeneity and oscillations in intrabacterial EMSH . The reductive-oxidative-reductive oscillations in EMSH during intramacrophage growth corresponds to the early induction of genes linked to reductive stress ( e . g . , whiB3 , whiB7 , and dosR ) [10] , [42] , followed by extensive bacterial killing during intermediate phase of relative increase in subpopulation with oxidized EMSH at 48 h p . i as compared to 24 h p . i . , and progressive increase in replication upon anti-oxidative shifts in EMSH at later stages of infection [42] . Interestingly , we observed some variations between confocal microscopy and flow cytometry based measurements of intrabacterial EMSH . For example , a higher percentage of subpopulation with oxidized EMSH was detected using confocal microscopy as compared to flow cytometry at 24 h p . i . ( Figure 5A and 6A ) . However , although flow cytometry allows monitoring of large number of cells at multiple time points with high statistical power , the read out is derived from averaging Mtb cells inside infected macrophages . Therefore , confocal microscopy is a much more accurate indicator of intrabacterial EMSH at the level of individual bacteria inside macrophages . In agreement to this , the moi dependent variations in the distribution of subpopulations with different EMSH could also be due to averaging relatively higher number of bacteria present inside macrophages infected at a moi of 10 than 1 by flow cytometry . Nonetheless , both techniques were in reasonable agreement and complement one another to generate a highly resolved view of intrabacterial EMSH during infection . We also discovered that sub-vacuolar compartments such as endosomes , lysosomes and autophagosomes are the source of redox variability in Mtb populations . While lysosomes and autophagosomes enrich EMSH-oxidized bacteria , early endosomes induce a reductive shift in EMSH of Mtb . Similarly , a greater fraction of bacteria displayed oxidized EMSH at a moi of 1 than 10 , which is consistent with the reported increase in phagosomal maturation and intracellular trafficking of Mtb to lysosomes at low moi [52] . Finally , our results showing a substantial oxidative shift in EMSH inside activated macrophages and its reversal upon treatment with iNOS inhibitor ( NMLA ) are in agreement with studies implicating the role of vacuolar NOX2 and iNOS systems in creating overwhelming oxidative stress within lysosomes and autolysosomes [2] . It is well known that Mtb actively remodels endosomal/phagosomal pathways during infection [53] to induce variability among phagosomes during infection [54] . This suggests that Mtb-induced alterations within sub-vacuolar compartments can generate a range of environmental conditions for the evolution of redox deviations in Mtb population . Intriguingly , we found that redox heterogeneity is uniformly present in H37Rv and MDR/XDR strains inside macrophages . However , distinct strain-dependent variations in redox heterogeneity were also evident . Because macrophage compartments distinctly manipulate EMSH of Mtb , varied redox oscillatory behavior could simply be a consequence of differences in co-localization kinetics of Mtb strains within sub-vacuolar compartments over time . Alternatively , phenotypic variations such as uptake rate and intramacrophage growth kinetics may influence the EMSH of these strains . While the molecular mechanisms behind these redox variations and their influence on evolution of drug-resistance and fitness await further investigation , several studies have documented strain-specific differences in bacterial and host gene expression during infection [55] , [56] , and resistance to redox stress [57] , [58] . Numerous studies using oxidant-sensitive dyes have demonstrated that bactericidal antibiotics , regardless of their target , exert toxicity by stimulating Fenton-catalyzed ROS production [59]–[61] . However , two recent studies demonstrate that measurements using dyes may be inconclusive owing to their non-specificity and that the bactericidal potential of antibiotics does not correlate with ROS generation in vitro [62] , [63] . Furthermore , to the best of our knowledge , the contribution of antibiotics-stimulated oxidative stress in the physiological context of infection has not been evaluated . In this context , Mrx1-roGFP2 allowed us to investigate the redox-basis of drug action in Mtb . We show that Mtb maintains EMSH in response to antibiotic exposure ( except in case of CFZ ) during in vitro growth , whereas a significant oxidative shift was induced by all drugs during intramacrophage growth . This data along with the antibiotic sensitivity displayed by the MSH-deficient strains [21] suggest that MSH biosynthesis or recycling efficiently buffers toxicity associated with drugs during normal growing conditions . In this regard , mycothiol-S-conjugate detoxification system ( Mca ) maintains cytoplasmic MSH levels upon antibiotic exposure by rapidly converting MSH-antibiotic adducts to mercapturic acids and excreting them into the culture media [44] . Another Mtb antioxidant , ERG , was found to be over-expressed in MSH-mutants , however , it does not provide protection against antibiotics [64] . Importantly , we show that the antibiotic-mediated increase in EMSH-oxidized precedes bacterial death inside infected macrophages . Oxidative shift in EMSH was substantial at time points earlier than those at which drug antimycobacterial activities were achieved . Moreover , INH-resistant clinical strains remained uninfluenced by INH-mediated oxidative changes in EMSH during infection , thus supporting our conclusions . Our results suggest that an active cooperation between host factors and antibiotics could disrupt intramycobacterial EMSH during chemotherapy . This is in agreement with a recent study demonstrating the role of anti-TB drugs in inducing host ROS production and potentiating mycobactericidal activity by delivering Mtb into lysosomal and autophagosomal compartments [65] . Since lysosomes and autophagosomes predominantly contain Mtb cells with an oxidized EMSH , our findings present a novel insight into the mechanism of antibiotic action during infection . Together , our data suggest that direct effects of antibiotics on Mtb physiology and on host innate immune mechanisms such as autophagy and phagosomal maturation , jointly eliminate Mtb bacilli by stimulating overwhelming intramycobacterial oxidative stress in vivo . In line with this hypothesis , we show that treatment with autophagy inducer ( rapamycin ) significantly augments mycobactericidal activities of antibiotics during infection . While MSH has been shown to be dispensable for growth of Mtb in mice [66] , future experiments should target MSH levels during antibiotic treatment to understand its function in modulating bacterial killing during antimicrobial therapy in vivo , where the heterogeneity in EMSH may be one of the critical determinants of persistence and drug tolerance . These exciting hypotheses can now be investigated by integrating Mrx1-roGFP2 technology with high-resolution live cell profiling of intramycobacterial EMSH at the single-cell level during infection . How do these findings relate to human TB ? Within human host , Mtb persists in a state of drug unresponsiveness in oxygen-depleted and lipid-rich granulomas [67] . The center of TB granuloma is hypoxic ( 3 mM Hg ) and contains lipid-laden foamy macrophages and free fatty acids released from necrotic macrophages [67] , [68] . Recent studies suggest that β-oxidation of host fatty acids by Mtb as the primary carbon source in an O2-deficient environment leads to massive accumulation of NADH/NADPH , which generates intrabacterial reductive stress in persisting cells during infection [10] , [69] . Work presented here demonstrates that drug tolerance observed during persistence may be mediated by increased reductive capacity . Further strengthening this connection are recent findings demonstrating elimination of Mtb persisters by drugs which become active under reductive stress ( e . g . , metranidazole ) or generate overwhelming ROS ( e . g . , CFZ ) [70] , [71] . Another common clinical observation is that Mtb genetically resistant to only a subset of anti-TB drugs resists clearance from all other antibiotics and thus , survive combination drug therapy [72] . One interesting possibility revealed by this work is that redox heterogeneity within genetically drug-resistant clinical isolates ( MDR/XDR ) provides a subpopulation that tolerates antibiotics against which bacteria are genetically susceptible . These understandings shed new light on drug resistant mechanisms and suggest that novel approaches that disrupt redox metabolism in Mtb may significantly impact eradication of both genetic and phenotypic drug resistant populations . In summary , we have developed a genetically encoded , fluorescent reporter capable of monitoring intrabacterial EMSH within macrophages . We anticipate that Mrx1-roGFP2 will play an important role in high content screening of small-molecule inhibitors of intrabacterial redox homeostasis . Based on this work , one can readily imagine development of new biosensors to measure the redox state of specific and unusual redox thiols , such as BSH , trypanothione , ovothiol A found in a variety of pathogenic organisms . Finally , macrophage-induced redox heterogeneity and its connection to drug sensitivity may be relevant to other intracellular pathogens . For example , the macrophage environment induces expression of genes responsible for antioxidant production and drug-tolerance in Legionella pneumophilla [73] . Thus , our findings may have relevance to several intracellular pathogens causing chronic and relapsing infections where persistence and tolerance pose challenges for treatment .
The roGFP2 containing vector was obtained from Tobias P . Dick [24] . The roGFP2 open reading frame ( ORF ) was released using NcoI and HindIII and was cloned downstream of hsp60 promoter into similarly digested E . coli-mycobacterial shuttle vector , pMV762 [74] to generate pMV762-roGFP2 . Mrx1-roGFP2 biosensor construct was generated by fusing the coding sequence of Mtb Mrx1 ( Rv3198A ) with roGFP2 having a 30-amino acid linker ( GGSGG ) 6 between the two genes . Mrx1 coding sequence was amplified from Mtb genome ( Forward primer: 5′ ATGCCCATGGTGATCACCGCTGCG 3′ . Reverse primer: 5′ ATGCACTAGTACCCGCGATCTTTAC 3′ ) . Cysteine mutations in Mrx1 coding sequence were introduced by site-directed mutagenesis as described [11] . Primers used were: Mrx1 ( AGYC ) forward primer: 5′ CTATACGACATCATGGGCTGGCTATTGCCTTCGAC 3′ , reverse primer: 5′ GTCGAAGGCAATAGCCAGCCCATGATGTCGTATAG 3′ , Mrx1 ( CGYA ) forward primer: 5′ CATCATGGTGTGGCTATGCCCTTCGACTCAAAACAG 3′ , reverse primer: 5′ CTGTTTTGAGTCGAAGGGCATAGCCACACCATGATG 3′ . All the fusion constructs were sub cloned into the expression vector pET28b ( Novagen ) , expressed in the E . coli strain BL21 DE3 ( Stratagene ) , and fusion proteins were purified via hexahistidine affinity chromatography as described [11] . Aerobically purified Mrx1-roGFP2 and ro-GFP2 were found to be in the fully oxidized state . To study the effect of various oxidants in vitro , the roGFP2 fusion proteins ( 1 µM ) were first reduced with 10 mM DTT for 30 min on ice and desalted with Zeba Desalt spin columns ( Pierce Biotechnology ) . In vitro measurements using various roGFP2 variants were performed on SpectraMax M3 microplate reader . Mtb mtr ORF ( Rv3198A ) was cloned into pET28b ( Novagen ) and expressed in the E . coli strain BL21 DE3 . Purification of Mtr protein was performed as described in the earlier section . Mycothiol is purchased from JEMA Biosciences , San Diego , CA , USA . To perform Mtr electron transfer assay , a mixture of 2 . 5 µM purified Mtr , 250 µM MSSM and 500 µM NADPH was prepared in 50 mM HEPES pH 8 . 0 in a 96-well plate . In the control reaction , Mtr was absent . The mixture was incubated at 37°C and consumption of NADPH was monitored at 340 nm for 60 min . To check the specificity of roGFP2 fusion proteins towards MSH , Mtr assay mixture was prepared as described above . After incubation at 37°C for 30 min , oxidized roGFP2 fusion proteins ( 1 µM ) were added to the mix . Ratiometric sensor response was monitored for 200 min . A control reaction without MSSM was included . Pre-reduced uncoupled roGFP2 and Mrx1-roGFP2 ( 1 µM ) were incubated with mycothiol solutions ( 1 mM total ) containing increasing fractions of MSSM . The total concentration of MSH ( MSH total ) refers to MSH equivalents i . e MSHtotal = [MSH]+2[MSSM] . OxDMSH is the fraction of MSH total that exists as [MSSM] and can be conveniently calculated using the following formula:Reduced from of mycothiol ( MSH ) was obtained by reducing MSSM with immobilized TCEP disulfide reducing gel ( Thermo Scientific ) under anaerobic conditions as per manufacturer's instructions . Detailed Materials and Methods are provided in the supporting information .
|
Approximately 30% of the global population is infected with Mycobacterium tuberculosis ( Mtb ) . Persistence of Mtb in host phagocytes depends on its ability to resist oxidant-mediated antibacterial responses . Mycothiol ( MSH ) is the main antioxidant that provides an abundant source of reducing equivalent , which protects Mtb from oxidative stress encountered during infection . The majority of research into redox signaling in Mtb has relied on chemical analysis of MSH in whole cell extract , which creates oxidation artifacts and prohibits dynamic imaging of MSH redox state during infection . We have successfully developed a novel and noninvasive tool based on genetically encoded redox sensitive fluorescent probes to perform real-time measurement of mycothiol redox potential ( EMSH ) in Mtb during infection . For the first time we reveal the EMSH of virulent and avirulent mycobacterial strains , including drug-resistant clinical isolates . We used this technology and came to the surprising conclusion that within a single infected macrophage there is heterogeneity in the redox signature of individual Mtb bacilli . Importantly , we show that anti-TB drugs accelerate oxidative stress in Mtb within infected macrophages and redox heterogeneity can contribute to emergence of drug tolerant population . These findings have implications for mycobacterial persistence following treatment with anti-TB drugs .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biotechnology",
"medicine",
"biochemistry",
"infectious",
"diseases",
"biology",
"microbiology"
] |
2014
|
Reengineering Redox Sensitive GFP to Measure Mycothiol Redox Potential of Mycobacterium tuberculosis during Infection
|
Biological diversity on Earth depends on the multiplication of species or speciation , which is the evolution of reproductive isolation such as hybrid sterility between two new species . An unsolved puzzle is the exact mechanism ( s ) that causes two genomes to diverge from their common ancestor so that some divergent genes no longer function properly in the hybrids . Here we report genetic analyses of divergent genes controlling male fertility and sex ratio in two very young fruitfly species , Drosophila albomicans and D . nasuta . A majority of the genetic divergence for both traits is mapped to the same regions by quantitative trait loci mappings . With introgressions , six major loci are found to contribute to both traits . This genetic colocalization implicates that genes for hybrid male sterility have evolved primarily for controlling sex ratio . We propose that genetic conflicts over sex ratio may operate as a perpetual dynamo for genome divergence . This particular evolutionary mechanism may largely contribute to the rapid evolution of hybrid male sterility and the disproportionate enrichment of its underlying genes on the X chromosome – two patterns widely observed across animals .
Intrinsic reproductive isolations ( RI ) between two newly evolved species can take the forms of hybrid male sterility ( HMS ) , hybrid female sterility ( HFS ) and hybrid inviability ( HI ) , all manifestations of genetic incompatibilities between two genomes [1] . Speciation genetics studies typically start with genetic analysis of divergent reproductive traits between two species . Numerous genes underlying interspecific divergence have been identified [2 , 3] , but they cannot be automatically qualified as “speciation genes” because some interspecific divergence may have evolved only after speciation was complete . The identification of genes underlying HMS , HFS and HI—also called Dobzhansky-Muller incompatibility ( DMI ) genes—by themselves , even with their biological functions well understood , can rarely answer which DMI genes are involved in establishing the initial RI , and what adaptive phenotypes of these genes are responsible for their fixations in one but not the other lineage [2] . Thus the evolutionary mechanism ( s ) for evolving DMI at the initial stage of speciation still remains a mystery . Nevertheless , two patterns have emerged from extensive speciation genetic studies in the last three decades . The first is the “faster male” evolution in that HMS evolves at a rate an order of magnitude higher than HFS and HI [4] , presumably caused by sexual selection [5] . The second is the “large X” evolution in that HMS genes are enriched on the X chromosomes [6–8] , presumably caused by more efficient fixation of mutations on the X than on autosomes [9] . However , sexual selection would also make hybrid ZZ males more likely to be sterile than hybrid ZW females , but this prediction is not supported by empirical observations [4] . Similarly , efficient selection of X-linked genes would also predict the “large X” pattern for the HI genes but no empirical support has been garnered either [10] . Thus , neither the “faster male” nor the “large X” pattern has been sufficiently accounted for by any evolutionary theories as well as the associated empirical evidence . The above two patterns can be better explained by the “conflict theory” in that genomic divergence is driven by selfish genes , prominently by sex ratio distortion ( SRD ) , also called sex chromosome meiotic drive [11–13] . Meiotic drive distorter breaches Mendel’s first law of genetics by gaining more than 50% transmission while quenching its homolog’s share in the gene pool of next generation . The distorter , however , does not commit suicide because of the tightly linked insensitive responder , while its homolog is linked to the sensitive responder . Meiotic drive is generally harmful to a genome , thus suppressors to silence the distorter are under strong selection to evolve and make the meiotic drive cryptic [14] . A tight linkage between the distorter and the responder is a key requirement for a meiotic drive system to evolve [15] . This prerequisite is readily satisfied on the two heteromorphic sex chromosomes , between which recombination is generally absent . Sex chromosome meiotic drive manifests as unequal sex ratio . For a typical XY male , the optimum sex ratio is all females for the X-linked genes but all males for the Y-linked genes , and 50% females for all autosomal genes . Therefore , the optimum sex ratios are at odds from the perspectives of various portions within a genome [16] . If SRD arises repeatedly on the X chromosome , counter evolution on the Y and the autosomes is anticipated , so much so that the SRD operates as a perpetual dynamo for genome evolution and bouts of this distortion-suppression process eventually lead to speciation [13] . The “conflict theory” can readily account for the “faster male” evolution because SRD occurs in XY male , and the “larger X” evolution because this chromosome contributes about half of the genetic changes in the evolution caused by SRD [13] . The “conflict theory” also predicts “faster female” in ZW females [4] , and a faster pace of RI evolution in taxa with heteromorphic sex chromosomes than those without . The best evidence for the “conflict theory” comes from two HMS genes with dual functions of SRD and HMS: tmy mapped between D . simulans and D . mauritiana [17] , and Ovd identified between D . pseudoobscura USA and D . p . Bogota [18] . However , these SRD systems could have evolved after speciation . Many other HMS genes are also mapped in these species but they do not have the SRD phenotype [19 , 20] , so are almost all the other known HMS genes across all taxa . Therefore HMS seems to have evolved by mechanisms generally unrelated to SRD . On the other hand , absence of SRD phenotypes in hybrids can be explained by the absence of idiosyncratic genetic background required for SRD expression , gene silencing and loss of function in cryptic SRD systems , or sterility of hybrids . Indeed , there might be an intrinsic difficulty to test the “conflict theory” because the SRD expression is usually transient . We reasoned that an ideal empirical system for identifying the bona fide “speciation genes” , to test the “conflict theory” or any other theories of speciation for that matter , would be a pair of species at the very incipient stage of speciation , when the HMS just starts to evolve and is directly responsible for establishing the initial RI . Two Drosophila species , D . albomicans and D . nasuta , appear to be such a system because of their young age of ∼120 kyrs [21] . D . albomicans is distributed from Okinawa of Japan through South China , Indochina to Northeast India , while D . nasuta is found in East Africa , Madagascar , Seychelles , Mauritius , Sri Lanka , and the India subcontinent [22] . These two species are not distinguishable in morphology but have distinct karyotypes . D . nasuta has the ancestral karyotype ( 2n = 8 ) , but the acrocentric 3rd chromosomes are fused to the X and Y to form a pair of new sex chromosomes ( X-3/Y-3 ) in D . albomicans ( 2n = 6 ) ( Fig . 1 ) . There is almost no pre-mating isolation between these two species [23] , and only weak hybrid breakdown was observed in the hybrids of advanced generations [24 , 25] . SRD is expressed in the F1 males produced by females of certain strains of D . albomicans crossed to D . nasuta males [24–27] . The sex ratio ( k , proportion of female ) is skewed ( k = ∼0 . 90 ) if the D . albomicans strains are from Okinawa but normal ( k = ∼0 . 50 ) if the strains are from Southeast Asia . There is an apparently increasing cline of SRD strength from SE Asia to Japan [24 , 26] . The “conflict theory” will be strongly supported if most HMS genes have contemporary or historical functions of SRD . The incipient species pair D . albomicans and D . nasuta qualifies as an excellent empirical system for testing the “conflict theory” because both SRD and HMS are expressed in their hybrids . For that , we mapped the genes of HMS and SRD simultaneously through three QTL mappings and multiple lines of introgressions . These genes are polymorphic within D . albomicans . A majority of the genes controlling both traits are colocalized to the same six regions . These findings implicate a contemporarily active SRD system that may have an evolutionary causal link to hybrid male sterility , thus lending strong support to the “conflict theory” of speciation .
For the genetic dissection of SRD and HMS in the species pair D . albomicans and D . nasuta , we first constructed three inbred lines , two from D . albomicans ( alb2—Okinawa; shl2—NE India ) and one from D . nasuta ( nas3—Mauritius ) ( Materials and Methods ) . We then surveyed male and female fertilities of these stocks and various F1 genotypes with standard methods , in which single males or females were mated to three virgin testers for 7 days and the progeny size was regarded as the fertility of the tested males or females ( see Materials and Methods for details ) . By the standard methods , all interspecific F1 hybrids appeared to have normal or nearly normal fertility . As expected and consistent with previous studies [24–27] , SRD was expressed in the F1 males from alb2♀ × nas3♂ ( k = ∼0 . 9 ) but not in the F1 males from shl2♀ × nas3♂ and most of the other crosses ( k = ∼0 . 5 ) ( S1 Fig ) . Unfortunately , all three inbred stocks are still polymorphic for chromosomal inversions , thus are not ideal for genetic mapping , a major goal of this study . Two true-bred stocks , alb267 and alb215 , were then extracted from alb2 with the help of molecular markers ( S1 Dataset ) , so were nas314 and nas384 from nas3 . However , we failed to construct inversion-free stocks from shl2 , presumably due to recessive sterile mutations locked in the inversions on the two haplotypes ( shl2-hap1 and shl2-hap2 ) ( Fig . 1; Materials and Methods ) . Chromosomes from alb2 or shl2 are not homosequential to those of nas3 , thus regions in and around the inversions are not accessible to genetic mapping . However , alb267 and shl2-hap1 are homosequential and have the same standard polytene sequence ( S2 Dataset ) . The standard test is not powerful enough to detect HMS between these two species . Some subtle abnormalities in spermatogenesis can be revealed by cytological methods . We thus used transmission electron microscopy ( TEM ) to examine spermatogenesis in the F1 males from the interspecific crosses alb2♀ × nas3♂ , nas3♀ × alb2♂ and shl2♀ × nas3♂ , as well as that from the intraspecific cross shl2♀ × alb2♂ ( Figs . 2 , S2 ) . Sperm head development was normal in all the F1 males examined , even those expressing SRD . In contrast , sperm head condensation during spermatogenesis is disrupted in two well studied meiotic drive systems in Drosophila [28 , 29] . However , pairs of sperm tails were often fused as a characteristic abnormality after the stage of sperm head condensation in many of these males examined . These twin tail fusions were more frequent in the F1 males from alb2♀ × nas3♂ ( 77% of tails ) than those from shl2♀ × nas3♂ ( 28% ) , suggesting severer HMS effects contributed by alb2 than shl2 alleles . Unexpectedly , frequent twin fusions ( 10% ) were also seen in the F1 males from the intraspecific cross shl2♀ × alb2♂ , tentatively suggesting that HMS has also evolved between these two strains of D . albomicans and possibly also a collateral effect of SRD evolution within the same species . But the intraspecific divergence needs further study with multiple strains of D . albomicans , a species with a very wide geographic distribution . On the other hand , no fusions were found in the hybrid F1 males from nas3♀ × alb2♂ in contrast to the F1 males from the reciprocal cross ( alb2♀ × nas3♂ ) , suggesting a lack of HMS loci residing on the X chromosome of nas3 and/or an enrichment of HMS loci on the X-3 chromosome of alb2 . Thus , the TEM studies provide evidence that slight HMS has evolved between these two species . In order to further quantify the weak HMS observed above , we developed a novel , exhaustive mating protocol with the assumption that all of functional gametes can fertilize eggs , so the sperm can be “counted” as progeny size ( S3 Fig , Materials and Methods ) . The results are summarized in Fig . 3 . To interpret the data , we posit that there were three antagonistic effects working simultaneously in the tested flies: inbreeding depression , hybrid vigor and outbreeding depression . Inbreeding depression caused much lower fertility of both sexes of the inbred stocks alb2 , shl2 and nas3 , while hybrid vigor increased the fertility of the F1 males from both reciprocal crosses of shl2 × alb2 ( Fig . 3A , C ) ; outbreeding depression , i . e . , DMI including HMS , brought down fertility in the F1 males from alb2♀ × nas3♂ and shl2♀ × nas3♂ , but not in the F1 males from their respective reciprocal crosses ( Fig . 3A ) . Somewhat consistent with the TEM studies , the fertility of the F1 males from alb2♀ × nas3♂ ( mean ± s . e . m = 352 ± 46 offspring per male ) was marginally worse than that from shl2♀ × nas3♂ ( 515 ± 91 , 1-tail t-test , P = 0 . 058 ) . Unlike males , fertility in hybrid females was largely not affected ( Fig . 3C ) . The latter contrast is expected because HMS evolves much faster than HFS [4 , 30] , and there might be only negligible HFS evolution between this pair of species at the very beginning of speciation . Strong SRD ( k = ∼0 . 92 ) was expressed in the F1 males but not the F1 females from alb2♀ × nas3♂ , consistent with previous interpretation that the observed sex ratio skew is caused by SRD rather than by other mechanisms such as male killing [27] . But unexpectedly , weak SRD ( k = ∼0 . 63 ) was also detected in the F1 males from shl2♀ × nas3♂ by the exhaustive mating protocol ( Fig . 3B ) . Like HMS , the SRD of this genotype was not detected by standard method ( S1C Fig ) . SRD expression might be affected by sperm storage or competition that must differ between these two mating test protocols . In sum , both HMS and SRD genes are polymorphic within D . albomicans and their effects are often subtle and difficult to assay . The HMS effects are slight and roughly amount to inbreeding depression suffered in the inbred parental lines . The asymmetry of HMS effects in the F1 males from reciprocal crosses suggests that only a few HMS loci are present [31] . Genotypes with stronger SRD appear to have severer HMS , suggesting a possible connection between these two traits . The HMS and SRD genes might have been enriched on the X-3 chromosome , consistent with a general prediction of the “conflict theory” of speciation . In contrast , D . nasuta might have barely evolved any HMS effects on its X chromosome . The last inference might suggest a lack of SRD activity in D . nasuta since it was split from D . albomicans . We took a quantitative trait loci ( QTL ) mapping approach to localize both the HMS and SRD loci divergent among the three chromosomal complements of alb267 , shl2-hap1 and nas314 in three separate experiments ( Exp1-3 ) ( Materials and Methods ) . The mapping population of males in Exp1 was produced from crossing the F1 females from alb267 ♀ × shl2 ♂ to nas314 males . Although these males were F2 , they actually had interspecific F1-like genetic constitution . The mapping is for genetic variations between these two strains of D . albomicans contributing to SRD and HMS between this species and D . nasuta . In contrast , the mapping populations in Exp2 and Exp3 were generated from backcrossing the F1 females from alb267 ♀ × nas3 ♂ and from shl2 ♀ × nas3 ♂ , respectively , to the parental nas314 males . The latter two mapping populations had the backcross 1 ( BC1 ) genetic constitution , and the mappings are for SRD and HMS genes divergent between D . albomicans and D . nasuta . All males of the three mapping populations were mating tested for fertility and sex ratio with standard method ( S4 Fig; Materials and Methods ) . In QTL analyses , we measured male fertility simply as the raw offspring count ( T ) . In addition , we also transformed T by log10 ( T+1 ) or treated it as a binary variable ( 1 for fertile and 0 for sterile ) . These three treatments have different biological implications ( See Materials and Methods ) . Consistent with polytene evidence , the third chromosome was almost totally refractory from recombination between alb267 and nas314 , as well as between shl2-hap1 and nas314 ( S1 Table ) . Not surprisingly , genetic divergence is low for HMS between alb267 and shl2-hap1 ( H2 = ∼20% , Exp1 ) , so is it for SRD between shl2-hap1 and nas314 ( H2 = ∼13% , Exp3 ) ( Table 1; S1 and S4 Tables ) . In contrast , genetic divergence is much higher for HMS between alb267 and nas314 , as well as between shl2-hap1 and nas314 ( H2 = ∼42–92% , Exp2 and Exp3 ) , and so is it for SRD between alb267 and nas314 , as well as between alb267 and shl2-hap1 ( H2 = ∼78% in Exp2 and H2 = ∼48% in Exp1 , respectively ) ( Table 1; S2 and S3 Tables ) . For all mappings , a majority ( ∼66–100% ) of the H2 is additive ( h2 ) ( Table 2 ) . This is somewhat unexpected because the interactions between distorters and suppressors would suggest otherwise . The mapping results are incongruent for a few “tentative” QTL where the statistic inferences are not robust ( S5 Fig ) . On the other hand , many QTL are “good” because they are stable with various data transformations and analytical methods ( Materials and Methods ) . All QTL of total offspring ( T ) and sex ratio with their positions and phenotypic contributions are synopsized in Fig . 4 . The nomenclatures of QTL imply their functions: distorter ( D ) , suppressor ( S ) and hybrid male sterility ( HMS ) . Exp1 maps intraspecific genetic variations of SRD and HMS genes between the two A . albomicans complements alb267 and shl2-hap1 . We found five “good” ( D1–D4 , S1 ) and one “tentative” QTL ( S2 ) for sex ratio and two “good” QTL for male fertility ( HMS1 , HMS2 ) . Except D1 and HMS1 that are colocalized , all the other QTL have only one phenotype . Exp3 maps SRD and HMS between shl2-hap1 and nas314 with two “good” ( D8 , S5 ) and three “tentative” QTL ( D7 , S6 , S7 ) for sex ratio , and two “good” ( HMS9 , HMS12 ) and four “tentative” QTL ( HMS10 , HMS11 , HMS13 , HMS14 ) for HMS . Three pairs of loci ( D7/HMS9 , D8/HMS11 , S7/HMS13 ) are colocalized , but the other five loci have only one phenotype . In these two experiments , the loci with only one phenotype might not genuinely have the alternate phenotype; or , more likely , the other phenotype falls short of detection because of the low H2 and thus low power in QTL mapping . The latter interpretation is supported by introgression studies described in next section . Since both SRD and HMS have high H2 in Exp2 , we expect that the mapping power would be more balanced between these two phenotypes . Indeed , all three “good” ( D5 , D6 and S4 ) and one “tentative” ( S3 ) sex ratio QTL from Exp2 are located to regions also harboring HMS QTL . The only exception is the tentative HMS4 without SRD locus nearby ( Fig . 4 ) . QTL mapping is known for its lack of resolution , finer mapping is needed as we will show in the next section . All together , four regions ( R1-R4 ) harbor >90% of additive genetic variance ( h2 ) of SRD and HMS across the three QTL mappings with the only exception of SRD mapping in Exp3 , where the “good” S5 and “tentative” S7 outside these four regions contribute 24 . 4% and 12 . 2% of h2 , respectively ( Fig . 4 , S4 Table ) . Because of the low H2 for SRD in Exp3 ( 13% , Table 1 ) , the robustness of detecting the SRD QTL from Exp3 is questionable; even worse in the case of S5 because of the sparse markers nearby . Nevertheless , the overall colocalization of SRD and HMS suggests that these two traits have evolutionary connection . Because of the limited power and resolution of QTL mapping , more definite evidence can be reached by introgression studies as presented below . We used a marker-assisted introgression approach to further increase mapping resolution of both SRD and HMS loci by testing the phenotypes of alb267 alleles in the nas314 background ( Materials and Methods ) . Because SRD and HMS are oligogenic systems , the penetrance of the constituent elements depends on appropriate genetic context . Therefore the phenotypes of individual QTL can be best assayed by contrasting two introgression genotypes with and without the focal alb267 alleles ( S6 Fig ) . In addition to the regions of R1 ( D5/HMS3 = D1 ) , R3 ( S3/HMS6 = S1 ) and R4 ( S4 ) , individual loci of D2 , D3 and D4 in the R2 region were also assayed after rare recombinants had been obtained on the 3rd chromosome between alb267 and nas314 ( Table 3 ) . All the introgressed alleles are either hemizygous ( D1 and D2 ) or heterozygous ( all the other loci ) . To avoid unnecessary complexities , we ignore the background nas314 allele in genotype nomenclatures throughout . When the introgressed alleles are made homozygous in some genotypes , both copies are included in the genotype nomenclature ( e . g . , S7 Fig ) . We assayed the functions of D1 by contrasting several genotype pairs ( Table 4; S8 Fig ) . In some genetic backgrounds , D1 had strong sterilizing effects while in others it had the dual functions of SRD and HMS . Similar contrasts were made for D2 , D3 , D4 , S3 and S4 , as well as S3 and S4 together ( S3S4 ) . Each of these loci expressed both HMS and SRD from at least one contrast . The phenotypes of all these loci are obviously sensitive to genetic background . One illustrative example is the R3 region . The shl2-hap1 allele at the R3 region ( S1 ) is a stronger SRD suppressor in Exp1 ( Fig . 4 ) , but its SRD suppressing effect was not detected in Exp3 ( HMS12 ) , apparently caused by the lack of strong distorter in the hybrids . Similarly , the HMS functions of D2 , D3 and D4 were not detected in Exp1 but they were readily detected in some introgressions when alb267 alleles were put into a largely nas314 background . The varying penetrance might have reduced the power of QTL mapping as we noticed earlier . Colocalization of D1 ( D5 ) and HMS1 ( HMS3 ) was more evident by introgressions than QTL mappings ( Table 4; cf . Fig . 4 ) , so were the dual functions of the S3/HMS6 , S4/HMS7 and HMS12 regions with additional introgressions . When we collected additional mapping data from introgression of shl2-hap1 into nas314 background , even the HMS12 locus was readily detected to express SRD suppressing effect ( S9 Fig ) The dual functions might be contributed by separate SRD and HMS genes , but the probability of the four HMS genes with negative effects are each colocalized with one distorter is only 4 ! ∏i=14Ui = 0 . 0002 , where Ui is the 95% confidence interval for the ith QTL , as measured in a fraction of the X-3 chromosome ( Materials and Methods ) . Similarly , the probability is 0 . 02 for the colocalizations of HMS and SRD genes in the S3 and S4 regions . The overall probability is only 4 × 10–6 if distinct genes control the dual functions in all six QTL intervals . This calculation , albeit rough , strongly argues that the dual functions are most likely to be pleiotropic effects of the same genes . Notably , some escapers of sterile males sired nearly all female offspring ( e . g . , 13♀: 1♂ or k = 93% , Table 3 ) , leading us to speculate that many sterile hybrid males may have been potentiated to express extreme SRD . We also assayed the dominance of S3 by crossing D2D3D4;S3 males and females to generate four types of offspring ( S7 Fig ) . When S3 had two copies in the background , the sex ratio was reduced to 0 . 601 of D2D3D4;S3/S3 from 0 . 695 of D2D3D4;S3 . When S3 was absent , D2D3D4 males can only sire an average of ∼3 males with sex ratio 0 . 864 . Thus S3 is a semidominant SRD suppressor ( cf . ref . 27 ) . This explains why this currently silenced SRD system can be reactivated if one complement of suppressors is absent , as clearly shown by SRD ( k = 0 . 628 ) expressed in one BC1 genotype that differs from alb267 males by only one 2nd chromosome ( S10 Fig ) . It can also be inferred that the Y-3 chromosome of D . albomicans still hosts sensitive responder .
We have uncovered a cryptic SRD system within D . albomicans that appears to have a direct causal link to HMS and thus to speciation . This conclusion is based on the increasing genetic association of SRD and HMS with increasing mapping resolution . We reached the highest resolution in this study with a large collection of introgressions , yet we were unable to separate the SRD and HMS functions to distinct genes at all six major loci . It is much more likely that the dual functions of these loci are pleiotropic effects of the same genes . Importantly , the phenotypes of distorters ( D1—D4 ) and suppressors ( S3 , S4 ) follow the “conflict theory”: all distorters are X-linked and reduce male fertility while all suppressors are autosomal and increase male fertility . This is not the case between an “older” species pair D . mauritiana and D . simulans where the introgressed heterospecific alleles always augment male sterility regardless of their locations [7 , 19 , 32] . We interpret the male fertility functions of D1—D4 vis-à-vis S3 and S4 as the former have primarily evolved as SRD distorters while the latter as suppressors . In chronological order , distorters might most likely evolve earlier than suppressors . Under the above evolutionary scenario , the intraspecific variation of SRD and HMS genes between alb267 and shl2-hap1 can be interpreted in the following way: because Exp1 clearly shows that alb267 has stronger distorter ( positive effects of the D1-4 loci ) but weaker suppressor ( positive effect of the S1-2 loci ) ( Exp1 of Fig . 4; S2 Table ) , the SRD system might have become cryptic in alb2 more recently than in shl2 , while in the latter the distorter function has been degraded to become residual but the evolution of SRD left permanent footprint on spermatogenesis , so the HMS function such as that of the loci HMS9 and HMS12 stays . In light of this interpretation , the positive effect of HMS1 in Exp1 no longer appear contradictory to the negative effects of HMS3 in Exp2 and HMS9 in Exp3 , because the shl2-hap1 allele of D1 expresses weaker SRD but stronger HMS than the alb267 allele . We must point out , however , comparing the magnitudes of HMS effects across HMS1 , HMS3 and HMS9 might not be justified because they were measured in very different genetic backgrounds . Furthermore , the shl2-hap1 allele at the R3 region has a stronger suppressing power on SRD than the alb267 allele ( Exp1 in Fig . 4 ) . These two different SRD suppressor alleles might also differ in their power to rescue male fertility . Taken together , the difference in HMS effects between alb267 and shl2 as observed earlier ( Figs . 2 and 3 ) can be readily accounted by the SRD system divergence within D . albomicans . Numerous so-called “speciation genes” have been identified by mapping and positional cloning in the last three decades but little evidence has been gathered for their roles in establishing the initial RI . Though studies suggest several DMI genes as relics of genomic conflicts [3] , or indirectly implicate SRD as the primary evolutionary cause of DMI in fly [17 , 18] and mouse [33 , 34] , this study is the first one to catch SRD in action and the first account that SRD is driving the evolution of most HMS-causing genetic divergence between two newly formed species . We have also shown the difficulty of simultaneously detecting both SRD and HMS phenotypes , because SRD expression is very sensitive to genetic background ( Table 4 ) . This difficulty might explain why so little empirical evidence has been accumulated so far for the “conflict theory” of speciation . One QTL might contain multiple loci with less effect [32]; the dual functions might be caused by closely linked QTL each with one function only . Even if these possibilities turn out to be true under finer genetic analyses , a weaker version of the “conflict theory” could still be valid because HMS genes can hitchhike with the fixation of SRD genes . This possibility is best demonstrated in a classic case of RI between Mimulus guttatus ecotypes previously thought to be a pleiotropic by-product of adaptive evolution to copper contamination in soil . However , HI and copper tolerance are each controlled by tightly linked but distinct genes [35] . Unlike a scenario that RI is driven by ecological adaptation [36 , 37] , the primary driving force emphasized by the “conflict theory” is intragenomic conflicts . In a broader sense , our study sheds new light on the relationship between adaptive evolution—conventionally attributed to external biotic or abiotic factors—and speciation , which is generally regarded as a consequence of anagenesis under adaptive evolution [38] . Our study emphasizes that non-adaptive evolution out of intragenomic conflicts might be an important mechanism for evolution [39] . In addition to speciation , evolution of several biological traits might also be driven by intragenomic conflicts , such as mating behavior in some insects and epigenetic regulation of the sex chromosomes [13 , 40–42] . It would be extremely interesting to see how ubiquitous this mechanism would be in the evolution of many other biological traits . The simplicity of the genetic architecture of SRD/HMS between D . albomicans and D . nasuta opens the door for future studies to fine map and positionally clone all key genes , and to study their population genetics and genomics as well as biogeography of the speciation process [43 , 44] . An elucidation of the mystery shrouding the speciation problem appears to be reachable at least for these two species . Lastly , our study might help to address one long-standing controversy over the role of chromosomal rearrangement in speciation . Chromosomal rearrangements like Robertsonian fusions are often found among closely related species , thus are believed by some evolutionists to have played a major role in RI evolution because the F1 heterozygotes of two karyotypes are often less viable or fertile then the parents [45 , 46] . The difficulty of this theory is that the less fit heterozygotes would have prevented the new karyotype from spreading in a population , let alone founding new species [47] . D . albomicans with the fused X-3 chromosome has evolved from D . nasuta-like ancestor with separate X and 3rd chromosome . Our study has shown that meiotic drive might indeed have helped the spread of the X-3 fusion and meiotic drive can play an important role in karyotype evolution . However , we have also shown that the current RI between D . albomicans and D . nasuta might actually be caused by genic factors , not necessarily by the chromosomal rearrangements per se . Therefore , we revise the original thesis on the role of chromosomal rearrangement in speciation to emphasize meiotic drive as the means to spread new karyotypes—as M . J . D . White speculated [46]—but karyotypic changes might not be directly causing RI .
Three inbred lines were constructed by sib pair matings for 15 generations from outbred stocks: D . albomicans alb2—from the strain E-10802/MYH01-05 , Miyakojima , Okinawa , Japan , 2001; D . albomicans shl2—from the strain E-10815/SHL48 , Shillong , India , 1981; and D . nasuta nas3—from the strain G86 , Mauritius , 1979 . Dr . M . Watada , Ehime University , Japan , kindly provided us these three stocks . For brevity , these three inbred stocks are named alb2 , shl2 and nas3 . These stocks were crossed to generate the F1 , F2 and BC1 hybrids , which were tested for fertility and sex ratio and the result is consistent with previous work [24–27] ( S1 Fig ) . Based on polytene chromosomes and molecular markers ( frequent double peaks in the Sanger sequencing chromatograms ) , we found all three stocks were still polymorphic for inversions . Multiple single pair matings were set up from alb2 , nas3 and shl2 . Inversion-free parents were identified based on sequencing select markers . We constructed the stocks alb267 and alb215 , free of inversions , from alb2 . We produced a standard , more accessible and better quality photograph polytene map from alb267 , as compared to the same map published before [48] ( S2 Dataset ) . Similarly , we constructed true-bred stocks nas314 and nas384 from nas3 , with different polytene sequences on the third chromosome . We failed to construct true-bred stocks from shl2 , presumably due to the recessive sterile mutations located in the inversions . All inversions in the three inbred stocks were identified based on polytenes prepared from various stocks and their hybrids , as summarized in Fig . 1 . Flies were reared on standard Cornmeal-Molasses-Agar food in plastic vials ( ϕ2 . 6 × h9 . 4 cm ) . For all crosses , virgin tester females were aged to 5 days before setting up crosses at room temperature ( 22 ± 1°C ) . A pair of salivary glands were dissected out from a wandering third-instar larva—sex determined if necessary by the translucent gonads—in a drop of 45% acetic acid and quickly transferred to a second drop of 45% acetic acid for approximately 3 minutes . Individual glands were transferred to a drop of 2% lactic-acetic-orcein solution and stained for ∼5 minutes , then transferred again to a fresh drop of 2% lactic-acetic-orcein solution on a clean slide . The preparation was covered with a siliconized cover slip . The chromosomes were spread by gentle but firm tapping or pressing . The cover slips were sealed with nail polish . The preparations were stored up to 10 days at room temperature prior to examination under a 100× objective of an Olympus BX51 microscope . All cytological images were documented with an Olympus DP30BW digital camera . Further processing was done with Photoshop CS4 ver11 . 0 . 2 . PCR primers of molecular markers were designed based on ( 1 ) cDNA sequences prepared from D . albomicans male [21] and ( 2 ) their alignments with the annotated homologs from D . pseudoobscura , D . virilis and D . mojavensis ( http://flybase . org/ ) . The predicted PCR products fall in the size range of 500–1000 bp and span intron ( s ) if possible . PCR products amplified from alb2 , nas3 and shl2 were Sanger sequenced by Beckman Coulter Genomics ( Danvers , MA ) . Fixed nucleotide differences among stocks were used to develop allele-specific oligonucleotide ( ASO ) probes [6] . We have developed a total of 62 ASO markers between alb2 and shl2-hap1 , 67 markers between alb267 and nas314 , and 54 markers between shl2-hap1 and nas314 . Many of these markers were also typed by restriction fragment length polymorphism ( RFLP ) . Technical details of the probes , including PCR primers , ASO probes and wash temperatures , can be found in S1 Dataset . To prepare DNA from single flies , an individual was quickly ground in a 1 . 5-ml Eppendorf tube with 200 μl extraction buffer ( 10 mM Tris pH 8 . 2 , 1 mM EDTA , 25 mM NaCl , 0 . 4 mg/ml Proteinase K ) . After a 20-min digestion at 65°C , the tube was incubated at 95°C for 5 min and then chilled on ice . The extracted DNA was spun down briefly before being stored at -20°C . PCR amplification was performed in a total volume of 10 μl reaction mixture ( 1× buffer , 0 . 2 μM forward and reverse primer mix , 0 . 25 units of Taq polymerase , 150 μM dNTP , and 1 μl DNA template ) . The amplified PCR products were genotyped by RFLP or ASO probes as previously described [6] . Testes and accessory glands were dissected out from young males ( 2–3 day old ) with a fine tungsten needle and were transferred immediately to 2% glutaraldehyde in 0 . 067 M phosphate buffer on ice . The specimens were fixed for 2 hrs at 4°C in 1% paraformaldehyde and 2% glutaraldehyde in 0 . 067 M phosphate buffer , followed by a post fixation of 1 hr in 2% OsO4 at 4°C . The specimens were treated with 1% uranyl acetate at room temperature and were then dehydrated through ethanol grades ( 30% to 100% ) . Only one of each pair of testes was embedded . Each testis was cut into 4–5 segments with a fine tungsten needle and these segments were then aligned on the bottom of a mold with the apical tip facing out to one end . Sections were cut on a Reichert ultracut-S microtome , followed by staining with uranyl acetate and lead citrate . The grids were observed with HITACHI H-7500 electron microscope at Emory University Apkarian IE Microscopy Core . Two methods were used to measure fertility and sex ratio: Standard method . Individual males ( females ) were crossed to 3 virgin females ( males ) in a vial for 7 days before the mating parents were discarded or kept for genotyping if necessary . The offspring were sexed and counted 4–5 times until the 19th day after setup . Preliminary tests have shown that F1 hybrids between D . albomicans and D . nasuta produced normal or nearly normal numbers of progeny by this method ( S1 Fig ) , as also suggested by previous work [24 , 25] . The carrying capacity of the food vial is ∼200 flies so the standard method might not be sensitive enough to measure slightly or even moderately reduced fertility . Exhaustive mating protocol . We designated a method more sensitive than the standard one to quantify fertility . Throughout the experiments we used 5-day old virgin males or females of the same genotype ( alb2 ) as the tester and controlled the temperature at 22 ± 1°C , because preliminary tests had shown that temperature and tester females had small but significant effects on male fertility of some genotypes ( S1 Fig ) . For male fertility assay , individual 1-day old males were mated to three tester females for 24 hrs ( day 1 ) . The males were subsequently transferred to fresh vials supplied with three virgin females on day 2 and day 3 , after which the males were transferred to vials with 12 virgin females to stay in days 4–7 and then to vials with three virgin females for day 8 . The 4 + 1 days transfer regime was repeated until the individual males were dead or sterile . To prevent crowding in vials the mated tester females in 1-day vials ( days 1 , 2 , 3 , 8 , 13 , etc . ) were transferred to fresh vials every 7 days until they no longer laid fertilized eggs . To reduce the labor cost ( by ∼80% ) we only sexed and counted offspring from 1-day vials ( days 1 , 2 , 3 , 8 , 13 , etc . ) to the 19th day after vial setup , while the offspring from the 4-day vials ( days 4–7 , 9–12 , etc . ) were not counted and their numbers were interpolated from the flanking two 1-day vials , assuming the 4-day vials produced twice as many offspring from these two 1-day vials . Towards the end of the protocol , male fertility dropped to only a few offspring per day so all offspring were usually counted from both types of vials . The above protocol for quantifying male fertility was designed under the assumptions that ( 1 ) all functional sperm fertilize eggs , and ( 2 ) the interpolation was accurate . In a pilot study we found male mating latency was more than 12 hrs , and the progeny size from the second mating within the same day was much smaller than the first mating . Therefore the first assumption is likely to be valid . The second assumption was shown to be valid also by two pilot experiments in which the alb2 and nas3 males were tested by the above protocol , with additional transfers of 4-day vial females to fresh vials and counting of their offspring . The actual counting and interpolation converges remarkably well ( S3 Fig ) . Therefore the exhaustive mating protocol can be used to “count” the functional sperm produced by a male . For female fecundity assay , single females were mated to three tester males in a vial and the flies were transferred to a fresh vial every 4 days until the female became sterile or died . Any dead male was replaced with fresh one during the experiment . All offspring were sexed and counted . Because of the fixed inversions between alb267 ( shl2-hap1 ) and nas314 on the 3rd chromosome , ∼40% of the genome is refractory from meiotic mapping ( Fig . 1 ) . On the other hand , the two D . albomicans complements , alb267 and shl2-hap1 , are homosequential so that meiotic mapping can cover the whole genome . With these considerations and to maximize the power of QTL mapping from the available lines , three QTL mappings were executed . In the first QTL experiment ( Exp1 ) , we generated a population of 459 males by crossing individual F1 females ( alb267/shl2-hap1 from alb267♀ × shl2♂ ) to nas314 males . After the vials were established , the mated F1 females of the genotype alb267/shl2-hap1 were distinguished from that of the genotype alb267/shl2-hap2 by molecular markers . Each male of the mapping population was phenotyped by crossing to alb2 females per standard method . These 459 males were genotyped for 62 ASO markers that can distinguish the alb267 and the shl2-hap1 alleles ( S1 Dataset ) . The other two QTL mappings were similarly executed . In Exp2 , a population of 442 males was generated by backcrossing the F1 females from alb267♀ × nas314♂ to nas314 males , and was genotyped for 67 ASO markers . In Exp3 , a population of 470 males was generated by crossing the F1 females ( shl2-hap1/nas314 from shl2♀ × nas314♂ ) to nas314 males , and was genotyped for 39 ASO markers out of the 54 markers available because only three out of the 18 markers ( M8 , M24 and M30 ) on the non-recombining 3rd chromosome were genotyped ( S1 Table ) . The phenotypes ( male fertility and sex ratio ) of all three QTL mapping populations are summarized in S4 Fig . Because it is not reliable to calculate sex ratio from small progeny size , we only use the males that sired at least 30 offspring . Thus the sample sizes of sex ratio for these three QTL mappings are reduced to 440 , 227 and 340 , respectively . Overall , males from Exp2 and Exp3 suffered much greater sterility than from Exp1 , while SRD is almost absent from Exp3 . This pattern is consistent with earlier observations that the F1 males from alb2♀ × nas3♂ and shl2♀ × nas3♂ were very infertile , while shl2 had only weak SRD alleles genes ( Fig . 3 ) . We first applied the R/qtl package ( v1 . 26 ) to construct three genetic maps separately from the three QTL mappings [49] . As expected , the 3rd chromosome had normal recombination only in Exp1 but hardly any in the other two mappings ( S5 Fig , S1 Table ) . M68 is not linked to the 2nd linkage group in Exp2 , while M41 and M45 are not informative in Exp3 . In the end , the genetic maps of Exp2 and Exp3 are far less complete as compared to that of Exp1 . Interestingly , there seems to be a cluster of markers around the centromere region on the 2nd chromosome , suggesting the existence of chromosomal rearrangements in that area but we did not detect any polytene evidence for that suggestion . To map QTL for HMS and SRD , we applied the composite interval mapping ( CIM ) method implemented in Windows QTL Cartographer ( v2 . 5_008 ) [50 , 51] . Male fertility was treated as a continuous variable as either the raw counts ( T ) or transformed by log10 ( T+1 ) , or as a binary variable of 1 ( fertile ) and 0 ( sterile ) , with different biological implications . For example , the difference between sterile ( T = 0 ) and subfertile ( say T = 10 ) is definitely more profound in terms of spermatogenetic defects than difference in fertility , say , of T = 100 and 110; thus the log10 or binary transformation might be closer to biological reality than the raw count T . Sex ratio ( k ) was also treated as continuous variable . The threshold for significant QTL was determined by 500 times of permutations of the datasets at the level α = 0 . 05 . The QTL mapping results are plotted in S5 Fig . The presence , location and magnitude of the HMS QTL are often sensitive to data transformation methods . We also applied the multiple interval mapping ( MIM ) method to evaluate the QTL flagged by CIM and the epistasis , if any , among them [52] . The total genetic components ( H2 ) and their additive parts ( h2 ) of all QTL were also obtained from MIM , as summarized in S2–S4 Tables . A synopsis of QTL mappings by different methods is presented in Fig . 4 and Tables 1 and 2 . For a much improved signal/noise ratio than that from QTL mapping , we thus wished to test the effects of the flagged individual QTL in a uniform and clean background . We used an introgression method to isolate a few chromosomal segments , each containing individual QTL , in a largely nas314 background including the Y chromosome . A typical scheme was shown in S6 Fig . The six QTL , the markers used to monitor their transmission and the approximate sizes of each interval ( proportion of the X-3 or 2nd chromosomes based on genetic distance ) are: D1 ( M105 – M57 , ∼3 . 7% ) , D2 ( M107 – M20 , ∼3 . 8% ) , D3 ( M98 – M157 , ∼11 . 1% ) , and D4 ( M30 , ∼5 . 4% ) on the X-3 , S3 ( M72 – M136 , ∼4 . 0% ) and S4 ( M66 – M63 , ∼25 . 5% ) on the 2nd chromosome . The estimated interval sizes might have large errors because of unequal cross-over frequencies along the chromosomes . Sex ratio was treated as continuous variable with Gaussian distribution if all the progeny sizes were at least 30; otherwise logistic regression was applied on male and female counts . For summary statistics ( mean and s . e . m . ) of sex ratio obtained from sub-fertile males that often had progeny < 30 , a bootstrapping method was used to avoid spurious results . Other methods were standard as indicated in the text .
|
Millions of species live on Earth , thanks to an evolutionary process that splits one species to two or more new species . The formation of new species is benchmarked by the evolution of reproductive isolation ( RI ) such as hybrid sterility between new species . The fundamental question of how RI evolves , however , remains largely unknown . In a pair of very young fruitfly species , we localized six loci expressing dual functions of hybrid male sterility ( HMS ) and sex ratio distortion , implicating an evolutionary causal link between these two traits . The rapid evolution of HMS widely observed across animal taxa can be attributed to the rapid evolution of genes controlling sex chromosome segregation . All genes in a genome are not equal . This study suggests that conflicts among various parts of a genome might confer strong evolutionary pressure—a mechanism that has hitherto been regarded as rare and could actually be more ubiquitous than currently appreciated .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Sex Ratio Meiotic Drive as a Plausible Evolutionary Mechanism for Hybrid Male Sterility
|
We have previously reported on the functional interaction of Lipid II with human alpha-defensins , a class of antimicrobial peptides . Lipid II is an essential precursor for bacterial cell wall biosynthesis and an ideal and validated target for natural antibiotic compounds . Using a combination of structural , functional and in silico analyses , we present here the molecular basis for defensin-Lipid II binding . Based on the complex of Lipid II with Human Neutrophil peptide-1 , we could identify and characterize chemically diverse low-molecular weight compounds that mimic the interactions between HNP-1 and Lipid II . Lead compound BAS00127538 was further characterized structurally and functionally; it specifically interacts with the N-acetyl muramic acid moiety and isoprenyl tail of Lipid II , targets cell wall synthesis and was protective in an in vivo model for sepsis . For the first time , we have identified and characterized low molecular weight synthetic compounds that target Lipid II with high specificity and affinity . Optimization of these compounds may allow for their development as novel , next generation therapeutic agents for the treatment of Gram-positive pathogenic infections .
The ever-increasing emergence of many pathogenic bacterial strains resistant to commonly used antibiotics is a rapidly growing concern in public health . Patients with weakened immunity because of chemotherapy , AIDS or organ transplantation or patients undergoing acute care in hospitals are significantly and increasingly at risk for acquiring opportunistic bacterial infections [1] . Seven leading groups of pathogens account for the increased risk for such infections , including four Gram-positive bacteria: Staphylococcus aureus , Enterococcus faecium , streptococci and coagulase-negative staphylococci [2] . Resistance against commonly used classical antibiotics has emerged in all of these pathogens . The discovery and development of novel antibiotic compounds has been slow and our arsenal of effective antibiotics is dwindling . Resistant bacteria spread and cause infections at increasing rates , and thus there is an urgent need to develop novel classes of potent antibiotics against established molecular targets , such as Lipid II . Lipid II is an essential precursor in cell wall biosynthesis . It is comprised of a hydrophilic head group that includes a peptidoglycan subunit composed of N-acetylglucosamine ( GlcNAc ) and N-acetylmuramic acid ( MurNAc ) coupled to a short pentapeptide moiety . This headgroup is coupled to a long bactoprenol chain via a pyrophosphate group . The amount of Lipid II that can be synthesized is limited and the Lipid II molecule has a high turnover rate , making it an ideal and established molecular target for antibiotics [3] , [4] . Four different classes of peptide antibiotics that target Lipid II have been described: ( 1 ) the glycopeptides , including vancomycin and teicoplanin; ( 2 ) the depsipeptide antibiotics , including ramoplanin and enduracidins; ( 3 ) the lantibiotics , including nisin and mersacidin and ( 4 ) cyclic peptides , including mannopeptimycins , plusbacin and katanosin B [5]–[11] . Recently , defensins were also found to target Lipid II . Defensins represent a major class of antimicrobial peptides found in almost all eukaryotes and prominently present in mammals [12]–[16] . Since our initial report on the functional interaction of the human defensin peptide HNP1 with Lipid II [17] , several studies on defensins from other species has firmly established Lipid II as a target for these peptides . Most notably , Schneider et al [18] characterized the Lipid II binding site of the fungal defensin plectasin in molecular detail , putting defensins on the map as clinically relevant antimicrobial peptides . Two additional fungal defensins , oryzeacin ( from Aspergillus oryzae ) and eurocin ( from Eurotium amstelodami ) as well as two invertebrate defensins , lucifensin ( from the blowfly Lucilia sericata ) and gallicin ( from the mussel Mytilus galloprovinciali ) , were shown to bind Lipid II in that study [18] . More recently , the spectrum of defensins binding Lipid II was widened further to include Human β-Defensin-3 [19] and three oyster defensins [20] . Strikingly , glycopeptides , defensins , depsipeptides , lantibiotics and cyclic peptides do not share any obvious sequence- or structural homology , yet all are able to specifically interact with Lipid II in the bacterial membrane environment . Here , we report on the unique interaction of HNP-1 with Lipid II . Based on molecular details of this interaction we further identify small compounds as defensin mimetics and report on their potential as novel antibiotic agents to fight against Gram-positive pathogens . The identified compounds represent the first non-natural , synthetic compounds that bind Lipid II and represent a novel class of molecules that have the potential to be developed into antibiotics that target Lipid II .
Chemicals used for solid phase peptide synthesis were obtained as described [21] . Staphylococcus aureus ATCC 29213 and Escherichia coli ATCC 25922 were obtained from Microbiologics ( St . Cloud , MN ) . DiAcetyl-Lys-D-Alanine-D-Alanine ( D-Ala ) , DiAcetyl-Lys-D-Alanine-D-Lac ( D-Lac ) and vancomycin were purchased from Sigma . Defensin mimetic compounds were obtained from various suppliers as listed in Table S1 . Chemical synthesis and folding of defensins was carried out as described [21] , [22] . The molecular mass of the peptides was verified by electrospray ionization mass spectrometry ( ESI-MS ) as described [21] . Peptide stock solutions prepared with water were quantified spectroscopically using molar extinction coefficients at 280 nm calculated according to the algorithm of Pace et al [23] . Lipid II was essentially generated as described [24] . Short-chain water-soluble Lipid II containing a lipid tail of three isoprene units ( 3-Lipid II or farnesyl-Lipid II ) was generated and purified essentially as described [25] . Surface Plasmon Resonance binding experiments were carried out on a BIAcore T100 system ( BIAcore Inc . , Piscataway , NY ) at 25°C . The assay buffer was 10 mM HEPES , 150 mM NaCl , 0 . 05% surfactant P20 , pH 7 . 4 ( ±3 mM EDTA ) supplemented with 10% DMSO . 3-Lipid II ( 50 RUs ) was immobilized on CM5 sensor chips using the amine-coupling chemistry recommended by the manufacturer . For initial determination of binding , defensin mimetics were introduced into the flow-cells ( 30 µl/min ) in the running buffer at 10 µM . Resonance signals were corrected for nonspecific binding by subtracting the background of the control flow-cell . After each analysis , the sensor chip surfaces were regenerated with 50 mM NaOH for 30 s at a flow rate 100 µl/min , and equilibrated with the buffer prior to next injection . For binding kinetics studies , binding isotherms were analyzed with manufacturer-supplied software for BIAcore T100 . The antibacterial activity of defensin mimetics against Staphylococcus aureus ATCC 29213 and Escherichia coli 25922 was carried out in a 96-well turbidimetric assay essentially as described previously [26] with the following modifications: bacteria were exposed for 30 min to compounds in 10 mM phosphate buffer containing 5% DMSO prior to addition of 2× Muller-Hinton medium . Bacterial growth was monitored for 12 hours and data were analyzed as described [26] . Determination of MICs was performed by Micromyx , LLC ( Kalamazoo , Michigan ) according to CLSI standards [27] . Antagonization of the antibacterial activity of defensins against Staphylococcus aureus ATCC 29213 was carried out in a 96-well turbidimetric assay essentially as described previously [26] . Defensins ( 50 µM final concentration ) were pre-incubated with 3-Lipid II at 1∶1 , 1∶2 . 5 and 1∶5 defensin: Lipid II molar ratios for 30 min at RT . Following incubation , solutions were diluted two-fold in ten steps and bacteria were added . Defensin activity was neutralized by the addition of Mueller Hinton broth . Bacterial growth was monitored for 12 hours and data were analyzed as described [26] . Crystals were obtained using the hanging-drop vapor diffusion method at room temperature . Each drop contained 1 µl of HNP-1-Lipid II at ∼equimolar ratio and 1 µl of reservoir solution consisting of 0 . 2 M Sodium citrate tribasic dehydrate , 0 . 1 M HEPES sodium pH 7 . 5 , 20% v/v 2-Propanol . Crystals grew typically in one week and were shaped as square plates of dimensions of approximately 0 . 2×0 . 2×0 . 1 mm . They belonged to the I432 space group , and each asymmetric unit contained 2 molecules of the complex . Data collection and refinement statistics are described in detail in Table S1 . The partial crystal structure of the complex , in which Lipid II could not be built entirely due to a lack of electron density , was subsequently used for generating a model of the complex by data-driven docking using the HADDOCK program ( 2 . 1 version ) [28] , [29] . The observed electron density around Ile20 of chain A , Leu25 of both chains and Arg15 of chain B was used to define ambiguous interaction restraints ( AIRs ) with an upper distance bound of 2 Å between the side chains of those residues and the soluble part of Lipid II ( peptidic tail , oligosaccharide and pyrophosphate group ) . Random removal of restraints was turned off . One lipid II molecule was docked onto the HNP1 dimer with C2 symmetry restraints defined between the two HNP1 monomers . Topology and parameters for Lipid II were taken from [30] . Lipid II was treated as fully flexible during the refinement stage of HADDOCK . The docking was performed with default parameters , except for an increased number of models , 2000 at the rigid-body docking stage and 400 for subsequent flexible and explicit solvent refinement . The resulting models were clustered using a 7 . 5 Å RMSD cutoff and the clusters ranked based on the default HADDOCK score . Identification of Defensin mimetics involved two steps: 1 ) a 3D pharmacophore fingerprint typed atom triangles ( TAT ) [31] search and 2 ) a chemical/physiochemical similarity search with MACCS [32] and MPMFP [33] fingerprints performed using the program MOE ( Chemical Computing Group Inc . ) [31] . The first step was performed to find compounds that can mimic the chemical characteristic and relative spatial arrangement of the HNP-1 residue side chains that are important for binding with Lipid II . The full side chains of Ile20 , Leu25 of monomer A and Arg15 , Ile20 and Leu25 of monomer B from the experimentally solved complex structure were used as the reference for the pharmacophore search . As only the nitrogens of the Arg side chain serve as hydrogen-bond donors that interact with Lipid II , another reference structure with only the C- ( NH2 ) 2 moiety of the Arg15 side chain along with the full aliphatic side chains of other four key residues was also used for the pharmacophore search . To prepare compound databases for searching , 3D structures of low-molecular weight compounds were generated from 2D structures obtained from three large commercial databases; Maybridge ( Thermo Fisher Scientific Inc . , Wattham , MA ) , ChemBridge ( San Diego , CA ) , and ChemDiv ( San Diego , CA ) , which contain 59676 , 482276 , and 533143 compounds , respectively . The compounds were converted into 3D structures using MOE and subsequently minimized with the MMFF94 force field [34] to a root-mean-square ( RMS ) gradient of 0 . 05 kcal/mol/Å , followed by the assignment of 3D pharmacophore fingerprints for similarity searching . Pharmacophore searching was performed by comparing the small molecule 3D fingerprints with the HNP-1 dimer 3D pharmacophores with the extent of overlap calculated based on the Tanimoto similarity indices [35] . Database compounds with a Tanimoto index over selected cutoff values , with physiochemical properties that maximize bioavailability [36] and with unique chemical scaffolds were selected for the first round of biological experiments . A second round of in silico searching was performed to find analogs of the five active compounds identified in the first round of pharmacophore searching and experimental testing . For each active compound , two individual similarity searches were performed to find compounds that are either structurally similar or physiochemically similar to the query compound , using MACCS or MPMFP fingerprints , respectively . An in-house database in the University of Maryland Computer-Aided Drug Design Center with 5 . 04 million compounds was used for searching . Database compounds with a Tanimoto index over selected cutoff values and with drug-like characteristics that maximize bioavailability [36] were selected for the second round of biological experiments . Macromolecular synthesis inhibition by BAS00127538 and 1499-1221 were investigated using S . aureus MMX100 ( ATCC 29213 ) . Cells were grown at 35°C overnight on Tryptic Soy Agar Broth ( Remel , Lenexa , KS ) , and growth from the plate was used to inoculate 15 ml of Mueller Hinton Broth . The culture was grown to early exponential growth phase ( OD600 = 0 . 2 to 0 . 3 ) while incubating in a shaker at 35°C and 150 rpm . For each macromolecular assay , the test agents 1499-1221 and BAS00127538 were added at either 0 , 0 . 25 , 0 . 5 , 1 , 2 , or 4 , -fold their respective MIC values for S . aureus ATCC 29213 . As positive control drugs , the following antibiotics were added at 8× MIC in order to validate each assay: Vancomycin ( cell wall synthesis ) ; ciprofloxacin ( DNA synthesis ) , rifampin ( RNA synthesis ) , cerulenin ( lipid synthesis ) , and linezolid ( protein synthesis ) . For DNA and protein synthesis , 100 µl of cell culture reaching early exponential phase was added to triplicate wells containing various concentrations of test compound or control antibiotics ( 2 . 5 µl ) at 40× the final concentration in 100% DMSO ( 0 . 1% methanol in water for Rifampicin ) . A 2 . 5% DMSO treated culture served as the “no drug” control for all experiments . Cells were added in 1 . 25× strength MHB to account for the volume of drug added to each reaction , or in M9 minimal medium for protein synthesis reactions . Following a 5 min incubation at room temperature either [3H]Thymidine ( DNA synthesis ) or [3H]Leucine ( protein synthesis ) was added at 0 . 5–1 . 0 µCi per reaction , depending on the experiment . Reactions were allowed to proceed at room temperature for 15–40 min and then stopped by adding 12 µl of cold 5% trichloroacetic acid ( TCA ) or 5% TCA/2% casamino acids ( protein synthesis ) . Reactions were incubated on ice for 30 min and the TCA precipitated material was collected on a 25 mm GF/1 . 2 µm PES 96 well filter plate ( Corning ) . After washing five times with 200 µl per well of cold 5% TCA , the filters were allowed to dry , and then counted using a Packard Top Count microplate scintillation counter . For cell wall synthesis , bacterial cells in early exponential growth phase were transferred to M9 minimal medium and added to 1 . 5 ml eppendorf tubes ( 100 µl/tube ) containing various concentrations of test compound or control antibiotics ( 2 . 5 µl ) at 40× the final concentration in 100% DMSO as described above . Following a 5 min incubation at 37°C , [14C] N-acetyl-glucosamine ( 0 . 4 µCi/reaction ) was added to each tube and incubated for 45 min in a 37°C heating block . Reactions were stopped through the addition of 100 µl of 8% SDS to each tube . Reactions were then heated at 95°C for 30 min in a heating block , cooled , briefly centrifuged , and spotted onto pre-wet HA filters ( 0 . 45 µM ) . After washing three times with 5 ml of 0 . 1% SDS , the filters were rinsed two times with 5 ml of deionized water , allowed to dry , and then counted using a Beckman LS3801 liquid scintillation counter . For lipid synthesis , bacterial cells were grown to early exponential growth phase in MHB and 100 µl was added to 1 . 5 ml Eppendorf tubes ( in triplicate ) containing various concentrations of test compound or control antibiotics as described above . Following a 5 min incubation at RT , [3H] glycerol was added at 0 . 5 µCi per reaction . Reactions were allowed to proceed at room temperature for 40 min and then stopped through the addition of 375 µl of chloroform/methanol ( 1∶2 ) followed by vortexing for 20 sec after . Chloroform ( 125 µl ) was then added to each reaction and vortexed , followed by the addition of 125 µl dH2O and vortexing . Reactions were centrifuged at 13 , 000 rpm for 10 min , and then 150 µl of the organic phase was transferred to a scintillation vial and allowed to dry in a fume hood for at least 1 hr . Samples were then counted via liquid scintillation counting . Analyses were performed by Micromyx , LLC ( Kalamazoo , Michigan ) . Large unilamellar vesicles were prepared by the extrusion technique [37] . Vesicles were made of Di-Palmoyl-Phosphatidyl Choline ( DPPC , Avanti Polar Lipids ) with or without 0 . 1 mol % Lipid II . Vesicles were prepared with 50 mM rhodamine , washed with saline solution and purified by G25 Sephadex column ( GE healthcare ) . Compounds were diluted in saline solution in 96-well plate format and vesicles were added to each well . The increase of fluorescence intensity was measured at 612 nm ( excitation at 544 nm ) on a Molecular Dynamics spectrophotometer at 20 °C . Compound-induced leakage was expressed relative to the total amount of rhodamine released after lysis of the vesicles by addition of 10 µl of 20% Triton X-100 . The method described by Stasiuk et al . [38] was followed in general . Defibrinated human blood ( Valley BioMedical ) was washed three times with buffer ( 10 mM Tris-HCl , pH 7 . 4 , 0 . 9% NaCl ) and resuspended to a final concentration of 3% RBCs immediately prior to performing the assay . One hundred eighty microliters of RBCs were added to 1020 µl buffer containing various concentrations of the investigational compound . A total of 4 different compound concentrations ( based upon the broth dilution MIC ) were tested in duplicate at the following multiples of the MIC values: 0 , 1× MIC , 4× MIC , 8× MIC , and 16× MIC . DMSO alone ( 5% final concentration ) served as the negative control to subtract the background , while a reaction with water substituted for buffer served as a positive control that completely lyses the RBCs . Vancomycin served as assay validation ( negative control ) and was used 0 , 1× MIC , 4× MIC , 8× MIC , and 16× MIC also . Incubation of compounds and RBCs proceeded for 30 minutes at room temperature , followed by centrifugation at 1 , 300× g for 5 minutes to pellet the RBCs . Finally , 300 µl of the supernatant was removed to a 96-well plate and the released hemoglobin was measured at A540 using a SpectraMax ( Molecular Dynamics ) plate reader . Prism ( GraphPad ) software was used for data analysis . Results were expressed as percent lysis compared to treatment of RBCs with deionized water , which completely ruptures the membrane . Analyses were performed by Micromyx , LLC ( Kalamazoo , Michigan ) . The NMR samples consisted of 0 . 15 mM Lipid II , 0 . 15 mM BAS00127538 compound , or 0 . 15 mM Lipid II+0 . 15 mM BAS00127538 compound . All samples were dissolved in 90% double distilled H20+10% DMSO , incubated for 30 minutes , freeze-dried , and then dissolved in 300 µL of d6-DMSO . NMR experiments were carried out at 25°C on an 800 MHz Bruker Avance NMR spectrometer ( 800 . 27 MHz for protons ) equipped with a pulse-field gradient unit , four frequency channels , and a triple resonance TXI cryoprobe ( Bruker Biospin , Billerica , MA ) . 1D proton experiments were collected to probe for chemical shift changes and 2D TOCSY ( 30 , 60 , and 90 msec spinlock times ) , 2D NOESY ( 150 and 300 msec mixing times ) , and natural abundance 13C-HSQC experiments were collected to determine proton and carbon chemical shift assignments . A model of the BAS00127538-Lipid II complex was generated based on the experimental data followed by molecular dynamics ( MD ) simulations . Lipid II , which consists of a pentapeptide ( L-Ala-D-γ-Glu-L-Lys-D-Ala-D-Ala ) , two cyclic sugars , N-acetylglucosamine ( GlcNAc ) and N-acetylmuramic acid ( MurNAc ) , and a di-phosphate prenyl chain was generated in the program CHARMM [39] using the additive CHARMM force field for proteins and carbohydrates [40]–[43] . This involved creation of new topology files for MurNac , D-γ-Glu and the di-phosphate prenyl chain with missing parameters assigned by analogy . BAS00127538 was generated with the CHARMM general force field ( CGenFF ) [44] . The starting conformation of Lipid II was obtained from the experimental NMR structure of the nisin-Lipid II complex ( pdb code: 1WCO ) [30] followed by a 2000 step steepest descent ( SD ) minimization and then a 200 step adopted basis Newton-Raphson ( ABNR ) minimization yielding a conformation with a root-mean-square ( RMS ) difference of 4 . 7 Å for all non-hydrogen atoms as compared with the experimental NMR structure . The inhibitor-Lipid II model was built by orienting the inhibitor adjacent to Lipid II based on data from the NMR experiments . This involved manually placing one of the inhibitor benzene rings and MurNac ring in Lipid II adjacent to each other . Harmonic restraints , k ( r-r0 ) 2 , were placed between the geometric centers of the above groups , where k = 50 kcal/ ( mol Å2 ) , r0 = 3 Å and r is the distance between those geometric centers . The system was then subjected to a 2000 step SD energy minimization followed by a 1 ns gas phase Langevin simulation in the presence of the restraints followed by an additional 1 ns gas phase Langevin simulation in the absence of the restraints . The resulting complex was then solvated in a 48*48*48 Å3 pre-equilibrated [45] water box for condensed phase simulations . All water molecules within 2 . 8 Å of the non-hydrogen atoms of the complex are removed , and two sodium ions were added to neutralize the system , which contained 10385 atoms . While all nonbonded interactions were evaluated for gas phase simulations , nonbonded interactions were truncated at 12 Å for condensed phase simulations , with a force switch smoothing from 10 to 12 Å . Simulations were performed using periodic boundary conditions with the particle mesh Ewald summation method [46] used to treat the electrostatic interactions with a real space cutoff of 12 Å . The system was minimized for 2000 SD steps and subjected to an isobaric , isothermal ( NPT ) MD simulation at 300 K and 1 atm . Simulations were extended for 2 ns during which the inhibitor remains in close contact with Lipid II .
We have previously determined the crystal structure of chemically synthesized , wild-type HNP-1 at 1 . 6 Å resolution [47] . We attempted co-crystallization of a HNP-1/Lipid II complex . HNP-1 and soluble 3-Lipid II were mixed in a 1∶1 molar ratio . Crystals were observed in three separate crystallization conditions and all belonged to the same space group . Importantly , both crystallization conditions and space group were different from those for HNP1 alone . Models based on the monomer of HNP-1 ( PDB:1GNY , [48] ) and lipid II ( PDB: 1WCO , [49] ) as a probes were initially used in molecular replacement experiments to define a structure of the complex from the crystals growing from the solution containing HNP-1-Lipid II complex . Matthews coefficient analysis of protein crystal solvent content of I432 crystals indicated three molecules of HNP-1 monomer or HNP-1-Lipid II complex composed of two HNP-1 molecules and one lipid molecule in the crystallographic asymmetric unit . With Phaser we were unambiguously able to define two HNP-1 , but not three HNP-1 molecules which were arranged into wild-type HNP-1 dimer . Calculated 2Fo − Fc electron density maps for this model clearly indicated the presence of additional density in proximity of the R15 , Ile20 and Leu25 side chains ( Figure S1 ) . Pairwise superimposition analysis of HNP-1 alone or HNP-1 in complex with Lipid II revealed very close similarity as shown by an average RMSD value of 0 . 8 Å for 60 aligned Cα atoms . Although the overall structure of the dimers is the same , their pairwise superimposition indicates an apparent shift of the monomer B backbone forming β1/β2 and β2/β3 connecting loops and the β3 strand ( Figure S2 ) . In the dimer of crystals grown from HNP1-lipid II mixture the backbone atom of La25 and Ra15 identified by HADDOCK to be involved in Lipid II interaction ( see below ) do show positional shifts of around 2 . 0 and 1 . 3 Å , respectively ( Figure S3 ) . To visualize the complex between HNP1 and Lipid II , X-ray directed docking studies using the HADDOCK program [28] were performed . Our partial complex crystal structure , together with the availability of the HNP-1 and Lipid II experimental 3D structures , made such modeling feasible . Based on the X-ray data , the amino acid side-chains of Ile20 and Leu25 of monomer A and R15 , Ile20 and Leu25 of monomer B of HNP1 form the primary Lipid II binding site of HNP1 and this information was used to drive the docking ( see Material and Methods ) . Two clusters of solutions were obtained ( Table S2 ) . A view of the top ranking solution from the best scoring and most populated cluster is shown in Figure 1 and contact residues are listed in Table 1 . The interaction between HNP-1 and Lipid II involves mainly van der Waals ( vDW ) interactions and one main chain-side chain hydrogen bond between Arg15 of HNP-1 Monomer B and D-Ala at position four of the Lipid-II pentapeptide . Ile20 of Monomer A forms vDW interactions with three residues of the Lipid II pentapeptide as well as the N-acetyl muramic acid ( NAM ) moiety . The leucines at positions 25 of both monomers interact with the NAM moiety as well . Residues Gly23 and Arg24 of the HNP-1 A monomer are involved in additional interactions . Since our docking model predicts Arg15 , Ile20 , Gly23 , Arg24 and Leu25 to form the Lipid II binding site , we would expect that replacement of these residues by alanine will affect Lipid II binding and bacterial killing directly . We therefore assayed for Lipid II binding directly by Surface Plasmon Resonance using single alanine mutants of HNP-1 [50] . As expected , replacement of the most critical residues forming the predicted Lipid II binding site by alanine ( Arg15 , Ile20 and Leu25 ) resulted in significant reduction of binding to Lipid II as compared to the wild-type HNP-1 ( Figure 2 ) . In contrast , replacement of Arg5 , Ile10 or Gly23 by alanine did not affect binding to Lipid II , indicating that these residues are not important for Lipid II binding . The HNP-1 R24A mutant maintained significant binding to Lipid II , suggesting that this residue contributes , but does not make a critical contribution to Lipid II binding . Next , we examined whether the antibacterial activity of HNP-1 could be antagonized by soluble Lipid II as a measure for functional interaction . HNP-1 ( 50 µM ) was pre-incubated with 3-Lipid II at varying molar ratios and killing of S . aureus was determined using the vCC protocol [26] ( Figure 3 ) . The bactericidal activity of the HNP-1 peptide appeared partly antagonized by one order of magnitude by the presence of Lipid II in a 1∶1 molar ratio ( one order of magnitude ) , suggesting additional mechanism are involved in bacterial killing for this defensin . Given the antimicrobial activity of HNP-1 , we reasoned that compounds that mimic the interaction between HNP-1 and Lipid II could have potential antibiotic use . To identify low molecular weight compounds that can mimic the spatial orientation of the side chains in the HNP-1 dimer that bind Lipid II , a search of commercially available drug-like compounds was undertaken . 3D TAT pharmacophore fingerprints were used to describe the physical properties and spatial relationships of residues Ile20 and Leu25 of monomer A , and Arg15 , Ile20 and Leu25 of monomer B in the HNP-1 dimer . This information was then used in a pharmacophore search to identify compounds with the desired features . After the first round of biological testing , five active compounds were identified and two types of similarity searching were conducted . The first method is based on chemical similarity and may potentially identify compounds with improved activity as well as produce data allowing for a structure-activity relationship for the compounds to be developed that may be of utility of subsequent ligand design . Searching was also performed based on physiochemical properties that may lead to the identification of novel chemical structures that represent new lead compounds [51] . In total , 75 compounds from the two rounds of similarity searches were selected . All compounds were tested for antibacterial activity , binding to Lipid II by Surface Plasmon Resonance and for cytotoxicity against two human cell lines . Out of 75 compounds , 28 ( 37 . 6% ) were identified that showed specific killing against S . aureus over E . coli . Seventeen compounds ( 22 . 6% ) showed significant binding to Lipid II . 6 . 6% of all compounds were equally active against S . aureus and E . coli ( 5/75 ) and 46% ( 42/75 ) showed no activity ( 42/75 ) . Results for all compounds are summarized in Table S3 . Based on the assays described in Supplementary Table S3 , the low-molecular weight compounds selected as potential defensin mimetics were classified based on chemical structures , Lipid II binding , cytotoxicity and preferential Gram-positive killing ( Table 2 ) . Figure 4 shows the results for lead compound BAS00127538 as an example . This compound most strongly bound to Lipid II as measured by SPR and potently killed S . aureus bacteria . To confirm the antibacterial killing assays , Minimal Inhibitory Concentrations ( MICs , µg/ml ) were determined for lead compounds against clinically relevant bacterial strains ( Table 3 ) . In agreement with the killing assays , lead defensin mimetics tested were potently active against Gram-positive isolates , and generally no activity was apparent against Gram-negative isolates , with the exception of BAS00127538 , which had MICs of 4 µg/ml when tested against E . coli . There was no significant difference for any compound when evaluated against clinically relevant strains ( e . g . MRSA , VRE , PRSP ) . We next used mechanism of action studies to determine the mode of bacterial killing by BAS00127538 ( Figure 5 ) . At 1× MIC , BAS00127538 significantly inhibited cell wall synthesis , but not DNA , lipid or protein synthesis , indicating that cell wall synthesis is the primary target . At elevated concentrations , DNA , protein and lipid synthesis were reduced also , suggesting that the compound acts through a secondary mechanism , the most likely of which is membrane perturbation . We therefore determined membrane perturbation of BAS00127538 by examining its ability to cause lysis of Large Unilamellar Vesicles ( LUVs ) or red blood cells ( RBCs ) . We find that BAS00127538 induces significant leakage of LUVs at 8 µg/ml ( equates to 16× MIC for S . aureus ) , but does not induce lysis of red blood cells ( Figure 6 ) . Further , we find that lysis of LUVs induced by BAS00127538 is reduced by the presence of Lipid II . We also used a second mimetic compound , 1499-1221 , in these studies . This compound is structurally related to BAS00127538 ( see Figure 6 ) and potently kills Gram-positive organisms , however , Lipid II binding by NMR could not be confirmed for this compound using the approaches that were successful for BAS00127538 . Compound 1499-1221 induced significant membrane rupture in LUVs irrespective of Lipid II as well as RBCs . Mechanism of action studies for this compound indicate that membrane perturbation is the likely primary mechanism for this compound ( Figure S4 ) . To confirm the binding of defensins mimetic BAS00127538 to Lipid II we observed by SPR , their interaction was studied directly by NMR ( Figure 7 ) . Specifically , we used 1D proton NMR spectra to determine if any chemical shift changes occur when the compound was added to 3-Lipid II . BAS00127538 was found to interact and was analyzed further by 2D TOCSY , NOESY , and natural abundance 13C HSQC analyses ( Figure 7 , upper panel ) . Large chemical shifts were observed on the face of this compound that contains two aromatic rings ( Figure 5 ) . No chemical shifts were observed for the methyl groups on the opposite side of the molecule ( not shown ) . Analysis of 3-Lipid II NMR spectra with and without compound allowed the interaction to be pinpointed to the N-Acetylmuramic acid moiety ( MurNAc ) of lipid II ( Figure 7 , lower panel ) . No chemical shifts for the pentapeptide Alanine residues were observed . Based on the NMR data , MD simulations were used to model the BAS00127538-3-Lipid II complex . This involved initially restraining each aromatic ring to be adjacent to MurNAc followed by explicit solvent MD simulations in which the restraint was removed following an equilibration period . The resulting model , which was stable in the explicit solvent MD simulation , is shown in Figure 8 . One aromatic ring of BAS00127538 lies over the MurNAc moiety ( green ) of Lipid II ( bond , atom color except for MurNAc in green ) consistent with the NMR data with the positively charged pyran ring of the inhibitor between the phosphate and acid moieties of Lipid II . In addition , the isoprenyl tail of lipid II forms a hydrophobic pocket that interacts with the second aromatic ring and the linker to indolylene ring . We established a murine model for sepsis to evaluate the efficacy of our lead defensin mimetics as antibiotic agents in vivo . Preliminary experiments indicated that the lead compounds listed in Table 2 were effective at 5 mg/kg in clearing non-lethal doses of S . aureus 29213 bacteria when administered intraperitoneally ( not shown ) . Lead compound BAS00127538 proved most efficacious and was selected for further experimentation . Mice ( n = 5 ) were inoculated intraperitoneally with S . aureus 29213 and treated 1 h and 4 h post-infection with compound BAS00127538 at 2 . 5 mg/kg intraperitoneally . Animals were monitored for survival and blood and spleen samples were collected . Bacterial counts were determined and compared to control treatment with vancomycin/lysostaphin as measures of efficacy ( Figure 9 ) . Animals treated with vehicle did not survive after 24 h . Animals treated with vancomycin/lysostaphin survived the length of the experiment and bacterial counts in blood and spleen were in accordance with published data [52] . Treatment with BAS00127538 resulted in survival of 4 out of five animals and significantly reduced bacterial counts in spleen and blood , indicative of in vivo antibiotic efficacy .
The view on how antimicrobial peptides kill micro-organisms has been nuanced in the last few years . The broad traditional view of killing comprises an initial phase of electrostatic attraction of mostly cationic peptides to negatively charged molecules on the surface of micro-organisms [53] . Following the initial interaction , antimicrobial peptides disrupt membrane integrity , causing leakage of cellular content and cell death . In fact , synthetic compounds that cause membrane disruption are effective antimicrobials and have been extensively studied [54] , [55] . Their killing mechanism depends on a distribution of positive charge and hydrophobicity , is largely species-independent and does not involve a specific bacterial target molecule . A functional interaction between defensins and Lipid II has only recently been described as a novel way by which these versatile peptides act against bacteria [17] , [18] . In their landmark report , Schneider et al reported on the fungal defensin plectasin binding to Lipid II . The study identified interactions between plectasin and the solvent-exposed pyrophosphate region of Lipid II [18] . These interactions involved residues Phe2 , Glu3 , Cys4 and C27 as well as the N-terminus and His18 side chain of this defensin . Importantly , the binding sites of plectasin to Lipid II do not overlap with the vancomycin binding site on Lipid II . The mechanism of resistance to vancomycin involves specific modifications of the amino acid composition of the pentapeptide in the Lipid II molecule [56] . Such modifications often occur rapidly within bacterial populations , likely due to the high degree of flexibility and variability of amino acid synthesis and incorporation [56] . Lipid II is a validated , yet underutilized target for natural antibiotic compounds , with only vancomycin approved for clinical use . This is surprising , since compounds representing four different classes of natural compounds with no apparent structural similarity bind to Lipid II . Another class of natural compounds that bind to Lipid II has now been added with defensins . Even within defensins , there are seemingly no apparent structural or sequence motifs that determine Lipid II interactions . Defensins of both the alpha- and beta-families , classified by differences in cysteine connectivity and fold , reportedly bind Lipid II . Vertebrate as well as invertebrate defensins interact with Lipid II , as do defensins that are highly cationic ( Human β-defensin 3 , +11 ) [18] , [19] . In this study , we have reported on the unique interaction of HNP-1 with Lipid II . Isoleucine at position 20 is found to bind Lipid II via multiple interactions . Recently , the structure of I20A has been solved and shows the usual HNP-1 fold ( Zao et . al . , submitted for publication ) . The asymmetric unit of I20A-HNP1 crystal contains 2 monomers , but they were not arranged into dimers . Analysis of intermolecular contacts within the I20A-HNP1 crystal unambiguously ruled out the formation of any quaternary structure for the I20A-HNP1 mutant . This further supports the notion that for its Lipid II binding activity , HNP-1 functionally acts as a dimer , and , more importantly , that Isoleucine at position 20 is critical for Lipid II binding , since Lipid II binding and S . aureus killing , but not GP120 binding , anthrax lethal factor binding [50] or E . coli killing ( unpublished ) , is abrogated by this mutation . Crystallographic analysis of the HNP-1-Lipid II complex unambiguously defined two HNP-1 , but not three HNP-1 molecules which were arranged into wild-type HNP-1 dimer . Further , one , Lipid II molecule could be defined . Since interactions are described between Lipid II and residues of both monomers in the functional HNP-1 dimer , the stoichiometry between the two molecules of HNP-1 and one molecule of 3-Lipid II is arguably a 1∶1 , and not a 2∶1 binding event . We did however observe that full inhibition of bacterial killing by Lipid II was not achieved at 1∶1 molar ratios . In these experiments , water-soluble 3-Lipid II was used for inhibition . It is conceivable that full inhibition of HNP-1 occurs in the context of the membrane environment , i . e . that the affinity of HNP-1 for membrane-bound Lipid II is higher than for its soluble form . In that case , one would need more than 1∶1 of soluble Lipid II for full functional inhibition . Besides that , alternative mechanisms cannot be excluded . Elevated concentrations of HNP-1 were reported in plasma , blood and body fluids such as pleural fluid , bronchoalveolar lavage fluid , urine , and cerebrospinal fluid from patients with a variety of infections including bacterial and non-bacterial infections and pulmonary tuberculosis [57] . More recently , lower than normal levels of HNPs or inactivation of the peptides have been linked to an increased risk of caries in the oral cavity [58] as well as infections of the airways including cystic fibrosis [59] , [60] . Although the concentration of HNP-1 inside granules of neutrophils has been estimated in the mg/ml range , its concentration in serum is only detectable in ng/ml ranges in these studies . Combined with inhibition of the antimicrobial activity of HNP-1 by the presence of salts or serum , it was never likely that HNP-1 , and perhaps other defensins , could be practically developed as natural antibiotics . Remarkably , no synthetic compounds that interfere with Lipid II have yet been developed . That is why we used the Lipid II binding footprint of HNP-1 to guide our search to identify low-molecular weight drug-like molecules that act as defensin mimics using CADD . Subsequent experimental characterization of these compounds showed several that show preferential activity against Gram-positive organisms while being non-toxic to host cells at comparable concentrations . One promising compound , BAS00127538 , was further characterized . Defensin mimetic BAS00127538 targets the aminosugar moiety of the Lipid II molecule , thus making cross-resistance with vancomycin unlikely . In addition to modification of the pentapeptide , modifications in the aminosugar residues in Lipid II that make up the peptidoglycan subunit can cause resistance also for many Gram-positive pathogens [61] . Such modifications often involve chemical modifications such as acetylation or de-acetylation . Further , BAS00127538 primarily affects cell wall synthesis and shows in vivo protection of sepsis . A second compound identified in our search , 1499-1221 , primarily disrupts the membrane as its mechanism-of-action . Results obtained with compounds like 1499-1221 and other defensin mimetic compounds will prove invaluable both for validation and optimization of leads such as BAS00127538 . Studies like these on defensin mimetics and on plectasin may provide insight for future development , design and synthesis of efficient , defensin-derived compounds specifically targeting Lipid II as promising therapeutic leads . To our knowledge , BAS00127538 is the first low-molecular weight compound that targets Lipid II that has been identified .
|
Every year , an increasing number of people are at risk for bacterial infections that cannot be effectively treated . This is because many bacteria are becoming more resistant to antibiotics . Of particular concern is the rise in hospital-acquired infections . Infection caused by the methicillin-resistant Staphylococcus aureus bacterium or MRSA is the cause of many fatalities and puts a burden on health care systems in many countries . The antibiotic of choice for treatment of S . aureus infections is vancomycin , an antimicrobial peptide that kills bacteria by binding to the bacterial cell wall component Lipid II . Here , we have identified for the first time , small synthetic compounds that also bind Lipid II with the aim to develop new antibiotic drugs to fight against bacterial infections .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2013
|
Turning Defense into Offense: Defensin Mimetics as Novel Antibiotics Targeting Lipid II
|
The expansion of urban ecosystems and climate change , both outcomes of massive lifestyle changes , contribute to a series of side effects such as environmental deterioration , spread of diseases , increased greenhouse gas emissions and introduction of invasive species . In the case of the Athens metropolitan area , an invasive mosquito species—the Asian tiger mosquito ( Aedes albopictus ) –has spread widely in the last decade . This spread is favoured within urban environments and is also affected by changing climatic trends . The Asian tiger mosquito is accompanied by risks of mosquito-borne diseases , greater nuisance levels , and increased expenses incurring for its confrontation . The main aims of this paper are ( i ) to estimate the various costs associated with the control of this invasive species , as well as its health and nuisance impacts , ( ii ) to evaluate the level of citizens’ well-being from averting these impacts and ( iii ) to record citizens’ and experts’ perceptions regarding alternative control measures . Evidence shows that experts tend to place a high value on mosquito control when associated with serious health risks , while citizens are more sensitive and concerned about the environmental impacts of control methods . The synthesis of results produced by the current study could act as a preliminary guide for the estimation of societal welfare from the confrontation of similar problems in the context of a complex ecosystem .
Recent reports highlight the impacts and risks to human and natural systems linked to global warming of 1 . 5°C compared to temperatures in the pre-industrial period [1] . The implications of rising temperatures for human health around the globe include changes in disease vector survival and pathogen development , and the emerging new sanitary and environmental risks are directly related to various socioeconomic impacts . Recent studies indicate that intense urbanization favours the spread of vector-borne diseases , which may also flourish due to the higher density of both people and animals ( both domestic and peridomestic ones ) , as well as due to various environmental and socioeconomic modifications [2–4] . In addition , the globalization of trade and travel has facilitated the spread and establishment of invasive alien species ( IAS ) . Insects predominate among non-native terrestrial invertebrates in Europe: of 1 , 522 established species , 1 , 306 ( 86% ) are insects [5] . The IAS inadvertently introduced into Europe include several invasive mosquito species ( IMS ) , which have found environmental and climatic conditions favourable for the establishment of permanent populations . These IMS are recognised as responsible for the emergence or reappearance of mosquito-borne diseases such as chikungunya , dengue and West Nile virus ( WNV ) . One IMS of major public health concern in Southern Europe is Aedes albopictus , the Asian tiger mosquito , which arrived in Europe in Albania in 1979 and then Italy in the early 1990s , through the trade in used tires . Ae . albopictus is already established in large areas of Greece and Southern Europe [6–8] and studies indicate that its rate of expansion in Greece is quite rapid [9–11] . Ae . albopictus has already been responsible for transmitting both dengue and chikungunya viruses in continental Europe , including over 200 laboratory-confirmed cases of the latter in Italy ( Region of Emilia Romagna ) in 2007 [12 , 13] and local dengue transmission in Croatia and France [14 , 15] . The IMS problem may affect the economy and society in various ways , through impacts on human and animal health , as well as on various services and activities . These impacts generate certain economic costs related to control strategies , public health measures , treatment of illness , productivity losses , information and awareness campaigns , and losses in tourism and other sectors . Economic impacts can be direct or indirect . Direct economic impacts are usually expressed as the net increase in public health spending as a result of the appearance of IMS and include , among other things , control-and-surveillance programs , private expenditures and direct medical costs . Direct impacts are the most clearly defined impacts as they can be explicitly expressed in monetary values . On the other hand , indirect impacts include the costs associated with new research and management services ( in both the public and private sectors of the economy ) , as well as the effects of IMS on tourism , etc [16–19] . Thus , the gradual establishment of higher IMS populations in Greece has been accompanied by greater risks of mosquito-borne diseases , increased costs of implementing prevention measures , higher nuisance levels and side-effects on tourism and other economic sectors . The aim of this paper is thus to present the main categories of costs related to the aforementioned problem , to evaluate the potential benefit of enhanced prevention measures and to examine citizens’ and experts’ opinions concerning the various socioeconomic aspects of the problem . In this framework , the present study offers a chance to consider the evaluation and selection of strategies for similar socio-ecological problems , by various interest groups , under the prism of different institutional approaches in an ecosystemic context .
The annual public control and prevention costs examined in this study consist mainly of: ( a ) annual mosquito vector control activities , ( b ) contingency costs incurred in response to the WNV epidemic by the responsible national agency , the National Public Health Organization ( NPHO ) , and ( c ) costs of the additional screening of blood donations that is imposed because of the risk of transmission of WNV through blood transfusion . Market prices for vector control activities were provided directly by regional and municipal authorities and private companies . The annual costs incurred by the NPHO from 2010 to 2013 were extracted from official reports and databases . The cost of additional blood safety testing was provided by the Hellenic National Blood Centre . Health impact costs were assessed in two ways . First of all , we estimated the medical costs for all imported cases of dengue , chikungunya and Zika virus in Greece for the period 2013–2017 ( Table 1 ) . This calculation was based on anonymized data on the duration of hospitalization of reported cases , including intensive care treatment , provided through the official records of NPHO . It should be noted that the reported cases consisted mainly of infected travelers from chikungunya , dengue and Zika endemic countries who presented symptoms of these diseases upon their return to Greece . These estimates of medical costs are similar to those in the recent literature regarding the imported chikungunya cases in Italy ( based on 2015 data ) , and the 2005–2006 chikungunya epidemic in La Reunion [22 , 23] . On the other hand , in order to evaluate a proxy estimate of the health burden of mosquito species in public health we also used the WNV costs induced by other mosquito species . Specifically , we present in Table 2 the medical costs of the 2010 WNV outbreak in Central Macedonia , Greece and their associated public health prevention and control strategies’ costs [24 , 25] . In this epidemic , which occured mainly in this Region , a total of 260 cases were hospitalized during the first year . In the following three years of transmission the number of hospitalized cases fell to 30 , 18 and 22 ( Table 2 ) . It should be underlined that enhanced surveillance and control measures , were implemented during the first year of the outbreak and particularly during the peak months of transmission ( from June to October ) . It might seem that costs associated with WNV ( which is spread mainly by Culex mosquitoes ) may be a rather poor approximation to health impacts of the Asian tiger mosquito . However , even though the costs for the WNV outbreak cannot be directly attributed to IMS , they represent an up-to-date indicator of regular public expenses incurred against the spread of mosquito-borne diseases in Greece . This indicator is directly comparable to other relevant estimates in South Europe [26] , thus enabling better adjustment for country-level effects ( variation ) in the cost-of-illness assessment . In addition , it is also interesting to note that the annual regional surveillance program for arbovirsuses in the Emilia Romagna Region of Italy [27] is designed for the common surveillance of various vector borne diseases such as WNV , chikungunya and dengue , highlighting thus the importance of applying integrated approaches against all mosquito-borne diseases . The estimation of health impact costs , ( Tables 1 and 2 ) comprising medical costs and productivity losses , were based on the cost-of-illness ( COI ) analysis [28] in which the burden of a disease on society is estimated in financial terms using both direct and indirect measures . Direct costs consist mainly of medical care , both inpatient and outpatient , and are estimated using market prices . According to the National DRG ( Diagnosis Related Groups ) Indicators published in the 3054/18-11-2012 Official Government Gazette of the Hellenic Parliament , the average daily hospital care cost in Greek public hospitals is approximately 207€/day; this was multiplied by the total inpatient care days . In addition , indirect costs represent the loss of productivity due to morbidity . These costs were estimated only for earnings lost during the reported days of sickness among people older than 18 years of age; the value of a lost working day was then multiplied by the total number of sick-days . The cost of a lost working day for people in the 18 to 65 years age range was calculated according to the per capita net income equivalent for the reference years ( 2011–2013 ) [29] , divided by 220 working days . For people aged 65 years and over , the cost of a lost working day was calculated from the country’s median hourly earnings [30] for 2010 , adjusted for inflation by the Consumer Price Index and then multiplied by 8 working hours . Due to lack of data on the age of patients , productivity losses for the imported cases of chikungunya , dengue and Zika virus , were calculated based from the median hourly earnings [30] for 2014 , adjusted for inflation by the Consumer Price Index for each indicative year and then multiplied by 8 working hours . We attempted to elicit household preferences for controlling IMS through a choice experiment approach [21] . Specifically , this stated preference method was implemented in order to examine household preferences regarding various attributes of mosquito control programs in relation to mosquito impacts ( i . e . in order to assess the influence of these attributes in choosing a program ) . The initial selection of attributes was based on feedback from experts and on previous relevant studies . These attributes and their levels were then reduced to only those that were found to have a clear relationship with mosquito control programs , and this relationship was articulated in operational terms easily comprehended by citizens . It should be noted that the control of mosquitoes is mainly carried out through annual activities which include monitoring and surveillance of the mosquito larvae population , implementation of larvicidal , adulticidal and surface residual ground treatments , and application of larvicidal and small scale adulticidal treatments by aerial spraying . On the other hand , controlling the Asian tiger mosquito calls for a more complex management plan and coordinated actions which have only recently been designed by the LIFE CONOPS research initiative ( http://www . conops . gr/management-plan-for-aedes-albopictus-in-greece/ ? lang=en ) . The actions in this plan include ( among others ) : standardized quantitative monitoring by special ovitraps , recording of mosquito population density data , involvement of the local population in the control campaign in private areas , residual door-to-door control interventions and use of larvicides in the road drains of public areas throughout the whole breeding season . The control methods and management plans according to the type of mosquitoes were described to all respondents at the beginning of the interview [21] . When selecting the attributes , two main categories of benefits that may be derived from improved mosquito control programs were identified: less nuisance and reduced risks to health . Another distinction was drawn between benefits from controlling native mosquitoes ( principally of the Culex and Anopheles genera ) and benefits from controlling invasive mosquitoes ( the Asian tiger mosquito ) . For this purpose , two health risk attributes were used: ( a ) one related to the health risks that are mainly associated with native mosquitoes , such as WNV , and ( b ) another one related to the health risks due only to the Asian tiger mosquito ( such as chikungunya fever ) . The nuisance attributes were likewise separated into: ( a ) nuisance during the day-time , which is a problem caused mainly by the Asian tiger mosquito , an “aggressive day-time biting mosquito” [31] , and ( b ) nuisance at night , mainly associated with the native mosquito species . A cost attribute was included in order to elicit welfare effects , as determined by individuals’ preferences between alternative mosquito control programs . Interviews were conducted from mid-June 2015 to the end of October 2015 in several districts within the Athens Metropolitan Area selected in order to represent the socioeconomic diversity of the city and the different degrees of exposure to the mosquito problem ( either the Asian tiger mosquito or the native species ) . The survey was administered in face-to-face interviews by three trained interviewers and the average duration of each interview was 15 minutes . Although it was not possible to draw a strictly random sample from a sampling frame , the sample achieved a high degree of representativeness concerning geographical location and it was stratified based on location , sex and age ( according to the 2011 Census ) . A total of 495 completed interviews were collected . The socioeconomic evaluation of the mosquito control strategies was enhanced by conducting a survey of experts on the issue of the overall mosquito problem . This survey was designed to evaluate the socioeconomic impacts of the mosquito control plans by interviewing key stakeholders , public policy makers , medical practitioners , public health experts and regional administrators . The questions were formulated in order to evaluate the results of the preceding studies ( especially the choice experiment ) and provide qualitative evaluation of specific policy-related decisions ( ecosystem services , adequacy of control programs , etc . ) . The questionnaire was distributed to a pool of 100 experts all over the country , selected on the basis of their experience and involvement in the design and implementation of mosquito control strategies . The survey was conducted through telephone interviews from May 2016 to May 2017 in collaboration with a member staff of the Ministry of Health and a total of 59 responses were collected . Apart from the survey in Athens , another questionnaire was also designed for a nationwide web-based survey aimed at eliciting citizens’ opinions regarding certain socioeconomic aspects of the mosquito problem . Its particular focus was to examine and then to validate at the national level a set of parameters related to: a ) private prevention costs for IMS , and b ) individual preferences among various mosquito control programs . The questionnaire was distributed through a popular meteorological data website ( www . meteo . gr ) with a high daily number of visitors [32] . For the purpose of our survey , a special banner appeared on the home page , from which visitors followed a link to the web survey . The banner appeared randomly to visitors , but a selection bias could arise due to ( i ) the non-representative nature of the internet population , and ( ii ) self-selection of participants ( the `volunteer effect' ) which was possibly related to their interest in mosquito control . The first set of questions focused on the respondents’ knowledge of the Asian tiger mosquito . Subsequent questions concerned: ( a ) the current perceived level of nuisance during the day and separately at night , both rated using a 5-point Likert scale ( nuisance impacts from the Asian tiger mosquito and from other species were estimated by attributing the nuisance during the morning and late afternoon hours to the former and the nuisance during the evening and night hours to native mosquito species ) , ( b ) the period ( months/year ) with significant mosquito nuisance , ( c ) the monthly household expenditure for private prevention measures , and ( d ) the main reasons for taking individual prevention measures ( i . e . they had to choose between health risk reduction and nuisance reduction ) . The survey took place in September and October 2016 with a total of 1 , 220 responses from all over the country .
According to national data published online on the governmental Greek Transparency Program Initiative ( http://diavgeia . gov . gr ) , the average annual public mosquito control costs in the Athens Metropolitan area range from approximately 800 , 000 € to 1 , 330 , 000 € per year . This represents an average annual cost of about 0 . 6 € to 0 . 9 € per household . These programs consist mainly of adulticide and larvicide activities , mainly with the use of specific chemical larvicides currently available or undergoing the revision process in the EU [33] , such as Diflubenzuron; these are designed for the control of Culex and Anopheles species and therefore target the elimination of their associated diseases ( such as WNV ) . In other words , the implementation of these programs is not specifically tailored to the control of the Asian tiger mosquito and the prevention of chikungunya and dengue fever , even though Diflubenzuron also has high efficacy rates for the Aedes species [34] . It should be noted that some surveillance activities for the Ae . albopictus are currently implemented in most parts of Greece including the Athens metropolitan area [35] , however , data are insufficient for calculating the cost of these as yet . In contrast to Greece , some other European countries are implementing programs specifically aimed at the control of the Asian tiger mosquito , such as the "Italian Plan of the Emilia-Romagna Regional Health Authority for the fight against the Asian tiger mosquito and the prevention of Chikungunya and Dengue fever" [36] . The current Greek management plan differs from these chiefly in that because it is not focused on combating the Asian tiger mosquito , larvicide activities mainly take place in public spaces without considering specific urban ( residential ) areas with high breeding activity of Aedes albopictus . The lack of a regional or national plan aimed specifically at controlling the Asian tiger mosquito makes the measurement of the efficacy of larvicide activities against Aedes albopictus difficult . What is more , due to high domestic breeding patterns of the Aedes species , information and communication activities can have a very high impact on the control of mosquito populations . According to recent estimates [36] , the annual total expenditure for information activities in Emilia-Romagna during the years 2009–2011 ranged from 150 , 000 €/year to 0 . 6 mil € , significantly lower rate than the costs for regular anti-larval treatments which ranged from 3 . 6 to 4 . 4 million €/year . As previously noted , the annual integrated surveillance plan for arboviral diseases in Emilia Romagna is designed for the common surveillance of various vector-borne diseases such as WNV , chikungunya and dengue , while recent studies also emphasize the effectiveness of community participation also concerning the elimination of Culex species [37] . The overall public control and prevention costs associated with the WNV epidemic have already been presented in Table 2 . Higher costs in the first year of application of measures are justified as a contingent response to the expansion of the outbreak . However , it appears that costs fell significantly during the following years . This could be also interpreted as a result of the epidemic being partially controlled; however , there are inadequate data within this study to support this argument . According to the results Table 1 , the average health cost for an imported case of Dengue was estimated to be 1 , 170 € for chikungunya , 2 , 774 € for dengue and almost 3 , 500 € for Zika virus . Even though the overall socioeconomic costs in the case of epidemic outbreaks for these diseases cannot be estimated with high precision ( due to the limited number of disease cases ) , it is possible that in the scenario of future epidemics , disease complications could outweigh the present costs of treating the diagnosed imported cases . The total cost of illness ( COI ) in the first year of the WNV outbreak ( 2010 ) was estimated at about 900 , 000 € [20] . This includes the cost of hospitalization for 260 recorded WNV cases , 25 of whom needed further hospitalization in intensive care at an extra cost of about 160 , 000 € . The total COI in the following year was estimated to be nearly 120 , 000 € for the hospitalization of 30 cases ( two of whom required treatment in intensive care units ) . Subsequently , 18 cases were recorded and treated in 2012 with only one case requiring intensive care . The total COI for this year amounted to 71 , 000 € . Finally , in 2013 , 22 cases were diagnosed ( two in intensive care ) and the COI was correspondingly slightly higher at 77 , 000 € ( Table 2 ) . Even though it is very difficult to provide precise estimates of the total costs and the total social benefits of mosquito control programs , the results of our previous study [21] permit us to conclude that the benefits of mosquito control in terms of reduced nuisance and reduced health risks are likely to exceed the associated implementation costs . Under our most conservative scenario ( i . e . a medium prevention scenario , effective only against the native mosquito species ) , the estimated aggregate benefits from improved control programs can reach up to 11 . 2 million €/year , thus corresponding to a net benefit of 7 . 40 €/household/year ( see Table 3 ) . These results provide an order of magnitude estimate of the economic feasibility of improved mosquito control programs in the study area ( Athens Metropolitan area ) . Specifically , the benefit-cost ratio of any program which is expected to achieve the selected target levels at a cost less than 13 times the cost of the current mosquito control program ( 800 , 000 €/year ) will be greater than one ( i . e . would be economically profitable ) . This cost could further increase ( up to 31 . 3 €/household/year ) if a high prevention scenario , effective against all mosquito species , were implemented . On the other hand , the expected added value of taking measures not only against native but also against the Asian tiger mosquito was found to be substantial , representing on average an additional benefit of about 15€/household/year [21] . As shown in Table 3 , this benefit can be attributed mainly to the high health risks posed by the introduction of new invasive species into the study area . In the survey of experts , 48% of the respondents considered the financial budget allocated to control programs to be adequate for confronting the problem while 34% suggested that an increase in public spending would be necessary . In addition , experts judge that the current control programs achieve balance between cost and effectiveness in their design and implementation . With respect to the potential negative impact of prevention measures on relevant ecosystem services , 65% of the experts stated that there are no ( significant ) negative impacts from these measures . Regarding the means of obtaining extra funds for supporting mosquito management , experts indicated that: ( a ) a redistribution of public resources would be necessary , ( b ) a reallocation of funds within national and regional budgets could improve the financing of mosquito control programs , and that ( c ) a financial contribution by citizens is equally important for the confrontation of the problem . It should be noted that the Asian tiger mosquito can exploit water containers in private apartments for its breeding . Therefore , according to the experts , private prevention activities could contribute significantly to the reduction of the problem at a much lower cost , especially if supported by public information activities which , as shown in the case of Emilia Romagna in Italy , could be more cost-effective . Lastly , regarding the prioritization of the objectives of future control programs ( Table 4 ) , experts stated that the health impacts should be considered as the primary objective of these programs . Specifically , they consider the health threats of native and invasive mosquito-borne diseases as almost equally important , whereas they treat nuisance from mosquito species as a less important impact factor . In the web survey of private citizens , 83% of respondents stated that the current prevention and control measures are insufficient or inadequate for dealing with the mosquito problems and therefore there is a need for further measures to be taken . The average private prevention costs of the sample were approximately 16€ per month in the period when mosquitoes are active , which amount to about 100 €/year . There was significant regional variation in these estimates , ranging from below 80 € ( e . g . Region Thessaly and Region of North Aegean ) to over 125 € ( e . g . Region of Eastern Macedonia and Thrace , and Region of Central Greece ) . This variation may be an indirect indicator of the magnitude of the mosquito problem , which is strongly associated with the nuisance conditions in each area . It should be also noted that this revealed behavior concerning prevention costs can be used as a lower-bound proxy of individuals’ potential benefits from improved control measures in each region . The results from this survey concerning the preferences of individuals for the diverse mosquito control programs are shown in Table 5 . Concerning the main targets of these measures ( Table 5 ) , health impacts were considered to be more important than nuisance impacts , confirming the findings of previous surveys in Greece [20 , 21] . Furthermore , as in the other two studies , health risks from invasive species were considered to be a serious threat . Therefore , both group , ( experts and citizens ) , appear to rate the health risks higher compared to the nuisance and cost factors of mosquito control programs . Finally , an important finding of this survey was that citizens seem to be aware of the environmental consequences of mosquito control measures . In particular , about 74% of respondents stated their disagreement with measures that may potentially affect the physical environment and the ecosystems .
The present paper aims at an overview of the socioeconomic aspects of the problem of invasive mosquitoes as recorded by the main interest groups in society ( citizens and experts ) . It provides substantive indicators regarding the citizens’ perceived benefit derived from the implementation of improved mosquito control programs , as well as experts’ evaluation of the socioeconomic effectiveness of current and future programs for controlling the problem of invasive mosquitoes . In contrast to other studies [17 , 38 , 39] findings from both perspectives show a higher priority for the prevention and reduction of health risks as opposed to nuisance control . Furthermore , based on the results of the survey conducted in Athens , citizens are willing to pay a considerable premium for effective protection against the spread of unfamiliar diseases , thus implying a risk-averting behavior against invasive mosquito threats . In other words , citizens are willing to pay today for improved control programs that will be able to eliminate potential future impacts and risks . The fact that climate change trends may worsen the mosquito problem and increase the risks of the transmission of new diseases ( such as Zika virus ) is likely to provide ever increasing potential individual and social benefits from implementing more efficient mosquito control management plans in the coming years [40] . The cost estimates extracted from the current study allow for a comparison with recent similar estimates in Southern Europe . According to our findings the costs of public mosquito control programs range from approximately 0 . 6 € to 0 . 9 € per household in Athens , while the public costs of informed arbovirus plans in the Region of Emilia Romagna in Italy reach almost 1 . 2 € per household [36] . Using the Purchasing Power Parity Index [41] this figure translates to an equivalent of 1 . 04 €/year per household in Athens , indicating that a small per capita increase in public costs could justify the design and implementation of more targeted programs in Greece , in terms of perceived citizens’ benefits levels as already presented in the current analysis . What is more , a recent study estimated that the implementation of public intervention strategies against the spread of Aedes related arboviruses in Italy since 2007 may have saved up to 13 . 5 million € indicating the cost-effectiveness of these interventions both from an economic and a health perspective [23] . It should be noted that our analysis indicated an annual benefit of up to 11 million € from the implementation of optimal mosquito control programs in the Athens area intended to achieve public health targets similar to those of Italy . With regard to health impact costs , the medical costs for an average cost of illness of an imported chikungunya virus case in Italy reaches approximately 3 , 500 € [23] , while the average in Greece , based on our limited sample of imported disease cases estimated to be about 1 , 100 € . The average cost of illness for all three types of imported diseases in Greece ( chikungunya , dengue , Zika virus ) was found to be approximately 2 , 500 € . According to another recent study in La Reunion [22] , the mean cost of illness per inpatient case of chikungunya reached approximately 2 , 000 € . Estimates of the cost of illness of recent WNV epidemics indicate a cost of about 3 , 500 € per case in Greece , while in Italy cost data are only available for the mean cost of illness for a WNV case with neuroinvasive complications ( WNND ) , which reaches approximately 15 , 000 € per case [26] . The above estimates offer an important range of socioeconomic figures relevant to mosquito-related diseases in Southern Europe which could act as significant indicators for evaluating the societal benefits of integrated public control programs against the spread of arbovirus diseases under turbulent climatic and societal conditions . The establishment of invasive species is usually associated with increased economic costs . For example , a study in the USA [42] estimated environmental damages and losses of almost $120 billion per year . According to a European Commission Impact Assessment [43] , Invasive Alien Species are estimated to have cost the EU at least €12 billion/year over the past 20 years and the damage costs continue to increase . It is predicted that , due to the trends in climate change , the invasive mosquito problem will intensify in the immediate future [44] . Therefore , the evaluation of the socioeconomic costs of invasive mosquitoes is a vital but highly challenging task made even more complex by changing climatic conditions , as well as by globalization and urbanization trends that may call for the adoption of multi-disciplinary and more holistic approaches in order to evaluate the effectiveness of the expenses incurred in improving public health and social welfare [45] .
|
This paper is based on several years’ collaboration among researchers from various disciplines , key health policy makers and stakeholders in an attempt to evaluate the economic dimensions related to the presence of the Asian Tiger Mosquito ( Aedes albopictus ) and the challenges of tackling mosquito-borne disease outbreaks in Greece and Southern Europe . Similar studies have been conducted and continue to be published in Europe and the USA examining the socioeconomic benefit from the implementation of relevant control and prevention strategies . These studies conclude that there are significant benefits related both to the reduction of nuisance levels and the reduction of the health risks posed by various mosquito species . In our case , the application of an updated economic analysis on the effectiveness of relevant public control and prevention programs provides essential information for public health decision-making , bearing in mind the significant restructuring of the public sector and the fiscal crisis apparent in the European South .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"invertebrates",
"species",
"colonization",
"greece",
"medicine",
"and",
"health",
"sciences",
"ecology",
"and",
"environmental",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"european",
"union",
"invasive",
"species",
"chikungunya",
"infection",
"pathogens",
"geographical",
"locations",
"microbiology",
"social",
"sciences",
"tropical",
"diseases",
"animals",
"health",
"care",
"viruses",
"rna",
"viruses",
"neglected",
"tropical",
"diseases",
"insect",
"vectors",
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"health",
"economics",
"medical",
"microbiology",
"microbial",
"pathogens",
"economics",
"disease",
"vectors",
"insects",
"arthropoda",
"people",
"and",
"places",
"socioeconomic",
"aspects",
"of",
"health",
"mosquitoes",
"eukaryota",
"west",
"nile",
"virus",
"flaviviruses",
"viral",
"pathogens",
"biology",
"and",
"life",
"sciences",
"species",
"interactions",
"viral",
"diseases",
"europe",
"organisms"
] |
2019
|
On lifestyle trends, health and mosquitoes: Formulating welfare levels for control of the Asian tiger mosquito in Greece
|
During spermatogenesis , mRNA localization and translation are believed to be regulated in a stage-specific manner . We report here that the Protamine2 ( Prm2 ) mRNA transits through chromatoid bodies of round spermatids and localizes to cytosol of elongating spermatids for translation . The transacting factor CBF-A , also termed Hnrnpab , contributes to temporal regulation of Prm2 translation . We found that CBF-A co-localizes with the Prm2 mRNA during spermatogenesis , directly binding to the A2RE/RTS element in the 3′ UTR . Although both p37 and p42 CBF-A isoforms interacted with RTS , they associated with translationally repressed and de-repressed Prm2 mRNA , respectively . Only p42 was found to interact with the 5′cap complex , and to co-sediment with the Prm2 mRNA in polysomes . In CBF-A knockout mice , expression of protamine 2 ( PRM2 ) was reduced and the Prm2 mRNA was prematurely translated in a subset of elongating spermatids . Moreover , a high percentage of sperm from the CBF-A knockout mouse showed abnormal DNA morphology . We suggest that CBF-A plays an important role in spermatogenesis by regulating stage-specific translation of testicular mRNAs .
In eukaryotic cells , nascent precursor ( pre ) -mRNAs are co-transcriptionally assembled into ribonucleoprotein particles ( RNP ) . RNP assembly is mediated by heterogeneous nuclear ribonucleoproteins ( hnRNPs ) , which associate with the transcripts , remain incorporated in mature RNPs , and in many cases , accompany newly synthesized transcripts from gene to polysomes 1–3 . In the cytoplasm , certain RNPs are transported to specific cellular locations for translation and some hnRNPs play a key role , binding to specific elements within transported mRNAs [4]–[6] . In cultured oligodendrocytes , hnRNP A2 interacts with the cis-acting element of the myelin basic protein ( MBP ) mRNA , termed A2RE ( hnRNP A2 response element ) or RNA trafficking sequence ( RTS ) , located in the 3′ untranslated region ( UTR ) of the transcript [7] , [8] . RTS recognition by hnRNP A2 has been correlated with MBP mRNA trafficking towards myelin-forming processes and with stimulation of cap-dependent translation [9] , [10] . Recently , we discovered that the RTS of the MBP mRNA is also targeted by the CArG box binding factor A ( CBF-A ) [11] , also referred to as Hnrnpab . Recognition of the MBP mRNA RTS by CBF-A is important for MBP mRNA localization to the myelin compartment [11] , which altogether suggests that RNA trafficking mechanisms are likely to be modulated by multiple transacting factors . CBF-A binding to RTS-like sequences of certain dendritic mRNAs was also found to be a requirement for activity-dependent transport to neuronal synapses [12] . How these mechanisms work and whether the two known CBF-A splice variants p42 ( Hnrnpab1 ) and p37 ( Hnrnpab2 ) synergize is not known [13] , [14] . Nonetheless , the above observations and similar findings in Xenopus laevis [15]–[17] suggest that CBF-A plays a conserved function which can sort transcripts that are competent for cytoplasmic transport and local translation at specific subcellular compartments . During the development of mammalian germ cells , the above mechanisms are important since expression of testicular transcripts is believed to be both spatially and temporally regulated ( reviewed in ref . 18 ) . The Prm2 mRNA , encoding an essential nuclear protein expressed in mature sperm , is known to be stored as translation-incompetent mRNPs for 2 to 7 days before translation occurs [18]–[23] . Temporary storage of the translationally repressed haploid transcript may be coordinated by chromatoid bodies , perinuclear structures that are evident in round spermatids [24]–[26] . Subsequent translational de-repression often entails alterations in the length of the poly ( A ) tail [27] . In the case of the Prm2 mRNA , poly ( A ) tail shortening represents a hallmark of the translationally active transcript [28] . What triggers poly ( A ) tail shortening and subsequent targeting of the transcript to the translation machinery is not fully understood but remodeling of the 3′ UTR of the transcript may play a key role in this transition since it is known to be targeted by many potential transacting factors [29] , [30] . The Prm2 mRNA has an RTS cis-acting element in the 3′ UTR , which displays high homology to the RTS in the MBP mRNA 3′UTR [7] . In the present study we therefore investigated whether CBF-A binds to the Prm2 mRNA RTS and regulates the transcript during spermatogenesis . We discovered that both p37 and p42 CBF-A isoforms target the Prm2 mRNA RTS in the 3′UTR . We found that p37 can interact with a translationally silenced form of the transcript . In contrast , in the translationally active Prm2 mRNA , p37 is replaced by the p42 variant which interacts with the RTS element and directly targets the 5′ cap binding complex . Importantly , the CBF-A knockout mouse showed reduced levels and abnormal timing of Prm2 mRNA translation . Furthermore , we found poor DNA compaction in the CBF-A-deficient sperm . We propose that the relay mechanism between p37 and p42 contributes to the Prm2 mRNA translation regulation . This mechanism is important for spermatogenesis and may be conserved in other cell types .
To evaluate steady state expression of CBF-A , immunoblots of total lysates from adult mouse tissues were analyzed with the anti-CBF-A antibody SAK22 that targets a conserved N-terminal epitope found in the p37 and p42 splice variants ( see also Figure S1A ) [12] . Both CBF-A variants were ubiquitously expressed in similar proportions with slight differences in a tissue-specific manner ( Figure 1A ) . Analysis of the cytoplasmic fractions showed a much larger variation in the p37 to p42 ratios ( Figure 1B ) . p37 is more abundant than p42 in the cytoplasmic fractions in brain , consistent with a previous study [31] , and we observed the same pattern in both ovary and testis ( Figure 1B ) , suggesting a possible common function of CBF-A among these three tissues . To address the involvement of CBF-A in spermatogenesis , we next examined expressions of CBF-A in the mouse testis in more details . Immunoblots of mouse testis lysates from mice 8 dpp ( days postpartum ) , 12 dpp , 17 dpp , 20 dpp , 32 dpp , in which Type B spermatogonia , zygotene spermatocytes , pachytene spermatocytes , round spermatids , and elongating spermatids start to appear , respectively [32] , confirmed that both CBF-A isoforms are expressed from at least 8 dpp ( Figure 1C ) . To study in vivo localization of CBF-A in spermatogenic cells , frozen testis sections were immunostained for CBF-A with the anti-CBF-A antibodies SAK22 ( see Figure S1B ) and ICCI ( Figure 1D–J ) . SAK22 recognizes both splice variants , while ICCI targets a unique exon 7 found in the p42 CBF-A splice variant ( Figure S1A ) [11] , [12] . Both antibodies confirmed that CBF-A is expressed in spermatogenic cells at all developmental stages ( Figure 1D–F; Figure S1B ) . Throughout spermatogenesis , the CBF-A signal was detected both in nucleus and cytoplasm , however , it predominantly localized to nuclei until the round spermatid stage and to cytosol in elongating and elongated spermatids ( Figure 1D–F ) . Furthermore , immunofluorescence staining of testis sections subjected to microwave-enhanced antigens retrieval [33] showed that CBF-A accumulates into perinuclear structures reminiscent of chromatoid bodies in round spermatids ( Figure 1G–I ) . To further examine this peculiar distribution , we made squash preparations of seminiferous tubules , in which testicular cells are dissociated into single cells with preserved cellular structure [34] . When these preparations were analyzed by co-immunofluorescence staining with antibodies against CBF-A and the chromatoid body marker protein MVH ( Mouse Vasa Homolog/DDX4 ) [35] , [36] , we found considerable overlap within chromatoid bodies ( Figure 1J ) . Similar distributions to CBF-A were revealed for hnRNP A2 ( Figure S2 ) , demonstrating that both RTS binding proteins are in chromatoid bodies . These results raised a possibility that CBF-A is involved in the regulation of mRNA ( s ) that are translocated to the chromatoid bodies during spermatogenesis . Since the 3′UTR of the Prm2 mRNA exhibits an RTS element [7] , we next asked whether the Prm2 mRNA is a target transcript of CBF-A in spermatogenic cells . For this purpose , we first analyzed the in vivo distribution of the Prm2 mRNA by fluorescence in situ hybridization on cryosections of adult mouse testis . A specific signal using a Prm2 mRNA antisense probe was detected in post-meiotic cells , as from later step round spermatids all the way to elongating and elongated spermatids ( Figure 2A–B ) . Control incubations with a Prm2 mRNA sense probe gave no significant signal ( Figure S3 ) . When analyzing the Prm2 mRNA intracellular localization , we found that in elongating and elongated spermatids , the Prm2 mRNA is diffusely localized in the cytoplasm ( Figure 2C ) . In contrast , in round spermatids ( in stage VII–VIII ) , the Prm2 transcript was highly enriched in perinuclear structures ( Figure 2C ) . These structures were found to be positive when co-immunostained with the MVH antibody to mark chromatoid bodies ( Figure 2C ) . On squash preparations of stage-selected segments of seminiferous tubules , we found that the Prm2 mRNA is not expressed in step 1–6 spermatids . The Prm2 mRNA is beginning to be expressed in step 7–8 spermatids where it is localized in chromatoid bodies and diffusely in the cytoplasm . The signal in the cytoplasm gradually increased until step 13–15 elongated spermatids where PRM2 synthesis occurs ( Figures 3; Figure S4 ) . Altogether these findings suggest that newly synthesized Prm2 mRNA is translocated in chromatoid bodies and cytoplasm where it is stored until it is translated . The in vivo distribution of Prm2 mRNA strikingly correlates with that of CBF-A , suggesting an involvement of CBF-A in the regulation of the Prm2 mRNA during spermatogenesis . We next fractionated mouse testis extracts into nuclear and cytoplasmic fractions , and analyzed the distribution of CBF-A . Immunoblotting with the SAK22 antibody revealed that CBF-A isoforms are in both subcellular fractions ( Figure 4A ) . hnRNP A2 showed a similar distribution ( Figure 4A ) . As expected , MVH and the mitochondrial protein Tom20 were detected in the cytoplasm , whereas fibrillarin was entirely restricted to the nuclear fraction ( Figure 4A ) . When the cytoplasmic lysate was assayed by immunoprecipitations , SAK22 and ICCI antibodies respectively precipitated p37 as well as p42 , and p42 alone ( Figure 4B ) . Consistent with previous observations [11] , hnRNPA2 was co-precipitated with CBF-A by both antibodies in an RNA-dependent manner ( Figure 4B ) . Even though CBF-A is present in chromatoid bodies ( Figure 1 ) , neither of the two CBF-A antibodies co-precipitated MVH and the piRNA-binding protein MIWI , which localize to chromatoid bodies and are involved in germline development [37] , [38] ( Figure 4B ) . We next set out to perform RNA immunoprecipitations ( RIP ) to determine whether CBF-A associates with the endogenous Prm2 mRNA . Total RNA was isolated from the protein fractions co-immunoprecipitated with the SAK22 and ICCI antibodies from testicular lysates . The precipitated RNA fractions were reverse-transcribed with oligodT primers . The resulting cDNA was analyzed by semi-quantitative PCR using primers specifically amplifying the Prm2 cDNA . For comparison , α-tubulin and clusterin mRNAs , which are known to be translated immediately after transcription [39] , were also analyzed . The results show that the Prm2 mRNA was highly enriched in the fractions precipitated with both CBF-A antibodies ( Figure 4C , see lanes 8 and 9 ) , whereas α-tubulin and clusterin mRNAs were not significantly detected within the immunoprecipitated fractions ( Figure 4C , lanes 8 and 9 ) . In control RIPs performed in the absence of antibodies or in the presence of control IgGs , none of the transcripts analyzed was detected ( Figure 4C , lanes 6 and 7 ) . Densitometric quantifications over three independent experiments showed a specific increase in the amount of Prm2 mRNA co-precipitated with the CBF-A antibodies ( Figure 4D ) . We conclude that CBF-A interacts with the endogenous Prm2 transcript in testicular cells . We next studied whether CBF-A binds to the RTS element of the Prm2 mRNA by in vitro RNA pull down assays . For this purpose , a biotinylated RNA oligonucleotide encompassing wild-type ( wt ) Prm2 mRNA RTS ( wtRTS-Prm2 ) was conjugated to streptavidin beads ( see Figure 5A ) . The beads were incubated with recombinant p37 and p42 as well as hnRNP A2 for comparison . After incubation with the beads , bound and unbound proteins were resolved by SDS-PAGE and visualized on immunoblots . When p37 , p42 and hnRNP A2 were individually incubated with the RTS-beads , all proteins were detected in bound fractions ( Figure 5B , lane 1–2; Figure 5C , lane1–2; Figure 5D , lane 1–3 ) , indicating that p37 , p42 and hnRNP A2 all have intrinsic ability to bind the RTS . However , when either p42 or hnRNP A2 were co-incubated with p37 , we found that precipitations of p42 and hnRNP A2 by wtRTS-conjugated beads were significantly reduced ( Figure 5B , lane 3; Figure 5C , lane3; Figure 5D , lane 4 ) . On the contrary , when p42 and hnRNP A2 were co-incubated , both proteins were co-precipitated with wtRTS beads in similar amounts ( Figure 5D , lane 5 ) . To find out how endogenous proteins interact with the Prm2 mRNA , we next performed RNA-pull down assays on cytoplasmic lysates of adult mice testis . As probes , we synthesized biotinylated full length wt Prm2 mRNA and a 3′UTR-truncated form ( Δ3′ UTR Prm2 mRNA ) lacking the RTS by in vitro transcription ( Figure 5A ) , and also prepared wt Prm2 mRNA RTS and a short oligonucleotide encompassing a scrambled version of the RTS ( scr MBP mRNA RTS ) ( Figure 5A ) . When we analyzed bound fractions on immunoblots for p37 and p42 or hnRNP A2 , we found that the p37 CBF-A variant was precipitated with wtRTS Prm2 , but not significantly recovered in bound fractions with scrRTS beads or mock beads ( Figure 5E , lanes 2–4 ) . On the other hand , very low levels of p42 were co-precipitated with all RNA probes ( Figure 5E ) . hnRNPA2 was also specifically precipitated by wtRTS Prm2 . However , when analyzing the binding propensities of endogenous p37 and hnRNP A2 against the full-length Prm2 transcript , we found that p37 was precipitated with the wt transcript more efficiently than hnRNP A2 . The lack of RTS in the 3′UTR truncated Prm2 transcript led to up to 50% drop in the amount of p37 precipitated , whereas we did not observe significant reduction in the levels of hnRNP A2 bound to the transcript ( Figure 5E , lanes 5 and 6; Figure 5F–G ) . CBF-A therefore interacts primarily with the RTS of the Prm2 mRNA , although other sites may be contacted too . We conclude that in vitro , p37 is the primary RTS binding factor and that p42 and hnRNP A2 can associate with the same RTS element . Interestingly , although being associated with the endogenous Prm2 mRNA ( Figure 4D ) , p42 and hnRNP A2 do not efficiently interact with the full-length Prm2 mRNA synthesized by in vitro transcription , suggesting a fundamentally different mode of binding to the transcript in comparison to p37 . Since translation of the Prm2 mRNA is temporally regulated , we next investigated whether CBF-A has a role in the Prm2 mRNA translation , by monitoring the distribution of CBF-A in polysome profiles . Mouse testicular homogenates were fractionated in 15–50% continuous sucrose gradient , and each fraction was analyzed by Northern blotting for the Prm2 mRNA and by immunoblotting for CBF-A ( SAK22 ) and hnRNP A2 . The distribution of rRNA shows that polysomes were sedimented at the higher density fractions ( Figure 6A ) . As is the case for stored mRNAs [28] , [39] , the Prm2 mRNA was detected in both free ribonucleoproteins ( RNPs ) and polysome fractions ( Figure 6A ) . The molecular size of Prm2 mRNA in polysome fractions was smaller than that in RNP fractions ( Figure 6A ) , which is consistent with the fact that the translationally active Prm2 mRNA is partially de-adenylated [28] . When the same fractions were analyzed on immunoblots for CBF-A , we found that both p37 and p42 co-sedimented with the RNP-containing fractions but only p42 was in the polysomes-rich fractions ( Figure 6A ) . hnRNP A2 displayed a similar distribution to p42 , co-sedimenting with both RNPs and polysome fractions ( Figure 6A ) . A yet uncharacterized testis-specific hnRNP A2 variant was also found in the RNP fraction ( Figure 6A ) . To determine whether the sedimentation of p42 and hnRNP A2 in the polysome fractions is due to their true association with polysomes , we added EDTA in the homogenate to dissociate ribosomal subunits ( Figure 6B ) . EDTA eliminated rRNA and Prm2 mRNA from the fractions eluting at the bottom of the gradient . Remarkably , we observed shifts of p42 and hnRNP A2 towards lighter sucrose fractions ( Figure 6B ) . These results collectively show that p42 and hnRNP A2 associate with a translationally active form of the Prm2 transcript in polysomes . Juvenile mouse testes exhibit a very low proportion of elongating spermatids than adult mouse testes , and the majority of the Prm2 mRNA in the early developmental stages is kept in a translationally repressed form [28] . Indeed , when we analyzed developmental expression of the Prm2 mRNA and PRM2 in 20- , 28- , 30- and 32-dpp ( days postpartum ) mouse testes , we detected expression of the Prm2 mRNA as from 28-dpp mouse testis , but significant levels of PRM2 expression were detected as from 32-dpp mouse testes ( Figure 6C–D ) . We also confirmed that both CBF-A splice variants were present in the cytoplasmic fractions at all developmental stages ( Figure 6D ) . Based on these observations , we next performed RIP analysis using 30 d mouse testes , to evaluate whether p42 associates with the translationally active Prm2 mRNA . Testicular extracts from adult and 30-dpp mice were subjected to immunoprecipitations with the SAK22 and ICCI antibodies . Analysis on immunoblots confirmed that SAK22 co-precipitated p37 , p42 as well as hnRNP A2 ( Figure 6E , lanes 3 and 7 ) , and the p42-specific ICCI antibody precipitated p42 and hnRNP A2 ( Figure 6E , lanes 4 and 8 ) from both adult and 30-dpp testicular lysates . We next isolated total RNA from each of the immunoprecipitated fractions and reverse-transcribed with oligodT primers . The cDNA was analyzed by PCR with primers amplifying Prm2 , α-tubulin and clusterin cDNAs . We found that the Prm2 mRNA was co-immunoprecipitated with SAK22 from both adult and juvenile testicular cytoplasmic extracts ( Figure 6E , lanes 3 and 7 ) . In contrast , the p42-specific ICCI antibody co-precipitated the Prm2 transcript from adult mouse testicular lysates but not from the extracts prepared from 30-dpp mouse testes under the same amplification conditions ( Figure 6E , cf lanes 4 and 8 ) . Since the Prm2 mRNA was precipitated by SAK22 , but not ICCI , this suggests that in juvenile mouse testis only p37 is associated with the Prm2 mRNA while translationally inactive . Conversely , SAK22 precipitated the Prm2 mRNA from adult testes , but since p37 is not found in the polysome fraction , this suggests that only p42 is associated with the Prm2 mRNA engaged in translation . We next examined whether p37 , p42 and hnRNP A2 associate with the 5′ mRNA cap complex . For this purpose we incubated testicular cytoplasmic lysates with immobilized 7-methyl-GTP-cap analog beads ( m7GTP beads ) . Bound proteins were resolved by SDS-PAGE and analyzed on immunoblots . We found that both p42 and hnRNP A2 were recovered among the cap-associated proteins whereas p37 was not enriched in the bound fraction ( Figure 6F ) . As expected , the cap binding protein eIF4E was also among the cap-bound proteins ( Figure 6F ) . Tubulin was not detected in the m7GTP beads bound fraction and none of the proteins analyzed was co-precipitated with the control beads ( Figure 6F ) . To test whether the association of p42 is RNA-dependent , we treated testicular lysates with RNaseA prior to incubating with m7GTP beads . Analysis of bound fraction on immunoblots showed that co-precipitations of p42 and hnRNP A2 with m7GTP bead were not affected by the RNase treatment ( Figure 6G ) . These results suggest that both p42 and hnRNP A2 associate with the translation machinery through direct protein-protein interactions . Taken altogether , the above findings and previous results indicate that p37 targets the RTS of a translationally inactive form of the Prm2 mRNA , whereas p42 binds to the RTS and 5′ cap binding complex of a translationally active form of the transcript . In order to examine whether CBF-A plays a role in the Prm2 mRNA regulation in vivo , we analyzed the Prm2 mRNA and PRM2 expression in the testis of the recently established CBF-A knockout mouse , referred to as Hnrnpab−/− [31] . Using the SAK22 and ICCI antibodies , we confirmed that neither p37 nor p42 are present in the testes of homozygous mice ( Figures 7A–B; Figure S5 ) . Analysis of the PRM2 levels on immunoblots of testicular lysates from the Hnrnpab−/− mouse testis revealed considerable decrease in the PRM2 expression in comparison to testicular lysates from heterozygous mice ( Figure 7A ) . Quantification of the PRM2 levels relative to tubulin showed an average of 65% reduction in the expression of PRM2 in the Hnrnpab−/− testis ( n = 3 individuals per genotype , p<0 . 05 significant difference in the Student's t-test ) ( Figure 7A ) . We did not detect any significant differences in the progression of spermatogenesis in the seminiferous tubules or in the number of elongating and elongated spermatids , and epididymal sperm between Hnrnpab+/− and Hnrnpab−/− mice , suggesting that the reduction of PRM2 is not due to the arrest or cell death of spermatogenic cells ( Figure S6 ) . Next , we examined expression and localization of Prm2 mRNA in testicular cells of Hnrnpab−/− and Hnrnpab+/− mice . Northern blotting analysis showed that the Prm2 mRNA levels are not affected in the absence of CBF-A ( Figure 7C ) . In situ hybridization of Prm2 antisense probe revealed specific staining in the Hnrnpab−/− testis , with similar intensities and localization patterns as in the Hnrnpab+/− testis ( Figure 7D ) . Similar to the wild-type mice ( Figure 2 ) , the Prm2 mRNA was detected in chromatoid bodies of round spermatids ( insets in Figure 7D ) , and in the cytoplasm of elongating and elongated spermatids of both Hnrnpab−/− and Hnrnpab+/− mouse testis ( Figure 7D ) . These results raised the possibility that reduction of PRM2 in the CBF-A knockout mice is regulated at the translational level . This view is supported by polysome analysis performed on testicular lysates obtained from adult Hnrnpab−/− mouse testis where we revealed a significant reduction in the Prm2 mRNA levels in the translation-active fractions ( Figure 7E ) . Thus , it is suggested that CBF-A contributes to the efficiency of the Prm2 mRNA translation . We also investigated if CBF-A has a role in translational repression of Prm2 mRNA during spermatogenesis . Immunohistochemistry analysis performed on testicular sections from Hnrnpab+/− and Hnrnpab−/− mice with an anti-PRM2 antibody revealed that some spermatids express PRM2 at earlier stages of spermatogenesis . As shown in figure 7G–J , a subset of elongating spermatids in the seminiferous tubules of stage IX–XII are PRM2 positive . Further , the nuclei appeared more condensed than in PRM2-negative cells ( Figure 7H–I ) . This peculiar expression pattern for PRM2 was observed in 38% of the stage IX–XII tubules in the Hnrnpab−/− mice ( 11 out of 26 seminiferous tubules ) whereas the PRM2 signal was visible only from elongated spermatids in the seminiferous tubules stage I–VI in Hnrnpab+/− mice ( Figure 7F ) , as previously shown in wild-type mice [23] . These results suggest that CBF-A is required to maintain the translationally repressed status of the Prm2 mRNA in the cytoplasm of elongating spermatids during spermatogenesis . We conclude that repression of the Prm2 mRNA translation is impaired in Hnrnpab−/− mice . Immunoblots of testes lysates from Hnrnpab−/− mice revealed that the PRM1 and the Tnp2 protein levels are also down-regulated in the absence of CBF-A ( Figure S7 ) . On the other hand , Acrv1 , Tnp1 , ACT and tubulin levels were not affected ( Figure S7 ) . Consistent with these observations , RIP analysis with the CBF-A antibodies SAK22 and ICCI showed interactions of CBF-A with the Prm1 and Tnp2 transcripts but not with the Acrv1 , Tnp1 , ACT and tubulin transcripts which were not enriched in the immunoprecipitated fractions ( Figure S7 ) . These results suggest that CBF-A probably regulates testicular transcripts other than the Prm2 mRNA , in a gene-specific manner . PRM2 , PRM1 and TNP2 are essential proteins for DNA compaction in sperm nuclei [40] . To examine if the altered expression of these proteins in the Hnrnpab−/− mouse affects spermatogenesis , we performed electron microscopic analysis of the epididymal sperm of CBF-A-deficient mice ( Figure 8 ) . We observed that 12% of sperm from Hnrnpab−/− mice ( 14/117 ) display an abnormal structure with fibers that protrude out of the main black/dense DNA mass in the nucleus . This abnormality was detected in only 3% of sperm in the testis of Hnrnpab+/− mice ( 3/102 ) . The appearance of the fibers is similar to that of chromatin DNA observed in step10 spermatids , in which DNA compaction is under progress . This suggests that the abnormal sperms are defective in the compaction of DNA . We also detected a mild compaction defect in 55% ( 64/117 ) of Hnrnpab−/− sperm ( vs . 33% , 34/102 in Hnrnpab+/− sperm ) . We conclude that translation regulation of the Prm2 mRNA and possibly other testicular transcripts by CBF-A is required for DNA compaction in the sperm nucleus .
We report here that during spermatogenesis , the newly synthesized Prm2 mRNA translocates and localizes to the chromatoid body and cytoplasm of round and elongating spermatids until it is targeted to polysomes for translation . To the best of our knowledge , our data provide first direct evidence that a translationally regulated transcript transits through the chromatoid body during spermatogenesis , underscoring the importance of spatial and temporal regulation of the Prm2 mRNA throughout spermatogenesis . These results also emphasize that the translationally inactive Prm2 mRNA is not only present in chromatoid body but it is also diffusely localized in the cytosol of round and elongating spermatids 41 , 42 . Several arguments indicate that the hnRNP CBF-A plays a role in the regulation of Prm2 mRNA expression . We show in vivo evidence that CBF-A co-localizes with the Prm2 mRNA in chromatoid bodies . In elongating spermatids , when chromatoid bodies are structurally and functionally transformed [43] , CBF-A and the Prm2 mRNA are both dispersed in the cytosol . This result is compatible with the distribution of CBF-A in polysome profiles as CBF-A co-sedimented with the Prm2 mRNA in RNP and polysome fractions , enriched in chromatoid body and cytosol , respectively [44] . Furthermore , CBF-A was found to interact directly with Prm2 mRNA via the RTS located in the 3′ UTR . We detected differences between the p37 and p42 CBF-A variants in RTS binding , polysome distribution , and in their abilities to associate with 5′ mRNA cap binding complex . p37 co-eluted with the translationally inactive Prm2 mRNA and did not co-sediment with polysomes . In contrast to a p42-specific antibody , the pan CBF-A SAK22 antibody could co-immunoprecipitate the Prm2 mRNA from d30 testes lysates where the majority of the Prm2 mRNA is translationally repressed . Furthermore , although p37 , p42 and hnRNP A2 are all present in the RNP fraction , RTS recognition by p37 competes away p42 and hnRNP A2 from interacting with the RTS element . Therefore , our hypothesis is that among the three RTS binding proteins , p37 can function during translational repression of the Prm2 mRNA in round spermatids , contributing to keep the transcript in a translation inhibited state ( Figure 9 ) . In contrast to p37 , we discovered that p42 co-fractionates with polysomes and p42 is not co-precipitated with the Prm2 mRNA from d30 testis lysates where the Prm2 mRNA is non-translating . Furthermore , p42 was co-precipitated with m7GTP beads in an RNA-independent manner . These observations indicate that p42 associates with the translationally active form of Prm2 mRNA . Since in the absence of p37 , p42 binds to the Prm2 mRNA RTS , we propose that p42 contributes to establish a translationally active form of the transcript by primarily targeting 3′ UTR via the RTS element and 5′ mRNA cap binding complex ( Figure 9 ) . Selective association of p42 with the transcript may be facilitated by hnRNP A2 that does not compete with p42 for in vitro RTS binding ( see Figure 5D ) and enhances cap-dependent translation by interacting with the RTS [10] . Post-translational modifications on the CBF-A isoforms and hnRNP A2 may also contribute to modulate their recruitments to the transcript [45] . The analysis of testes from CBF-A knockout mice did not show alterations in the Prm2 mRNA levels . The Prm2 mRNA still associated with chromatoid bodies in testes lacking CBF-A , which suggests that CBF-A does not have a primary role in mRNA targeting to chromatoid bodies . However , we found significantly reduced PRM2 expression levels and early timing of Prm2 mRNA translation . We hypothesize that p37 keeps the RNP in a translation-incompetent form and that the reduced levels of PRM2 in the CBF-A knockout mouse testis are due to the absence of the p42 splice variants . p37 may stabilize the translation-incompetent Prm2 mRNPs for intranuclear transport , and translocation to the chromatoid body and cytoplasm by interacting with motor proteins or factors that bridge the RNA with motor proteins such as the testis-brain RNA binding protein ( TB-RBP ) , which is important for spermatogenesis and interacts with KIF17b [46] , [47] . Following p37 release from the RTS , in our working model the 3′UTR undergoes remodeling and the RNP becomes transport-incompetent . This mechanism occurs concomitantly with shortening of the poly ( A ) tail and association of translation factors that promote formation of the circularized , translationally active Prm2 mRNA [27] . The presence of p42 in polysomes and the direct interaction with both RTS and cap binding complex is consistent with this view . These results suggest that p42 recruitment to the transcript is required for the Prm2 mRNA to engage the translation machinery . mRNA-protein complexes are subjected to dynamic changes in protein composition until a distinct mRNP emerges in the cytoplasm to engage the translation machinery [3] , [48] . The different properties of the CBF-A isoforms with respect to the Prm2 mRNP biogenesis are consistent with this scenario . We therefore speculate that p42 recruitment mediated by the RTS occurs in response to yet unknown developmental cues , which affect the Prm2 mRNA maturation and stabilization , and lead to de-repression of the transcript for localized translation . Remodeling of the 3′UTR may be facilitated by specialized RNA helicases such as DDX25 , an essential posttranscriptional regulator of spermatogenesis [49] . Whether during spermatogenesis CBF-A regulates the Prm2 mRNA in tandem with microRNA-dependent mechanisms is an intriguing hypothesis [50] , [51] , but remains to be proven . The UTRs of several testicular transcripts have however proved critical for expression during spermatogenesis . Translation of the Prm1 mRNA is kept repressed by a specific translational control element ( TCE ) found in the 3′UTR [52] . Although we have not been able to identify conserved RTSs in the UTRs of the Prm1 and Tnp2 mRNAs , their translations are down-regulated in the CBF-A knockout mouse . We therefore speculate that both transcripts are targeted by CBF-A through potential RTS-like sequences . Overall , the lack of CBF-A represses translation of certain testicular transcripts and leads to abnormal sperms which are defective in DNA compaction . Even though there is a possibility of subinfertility , the Hnrnpab−/− can produce pups , which would possibly be due to heterogeneity of the phenotype among spermatids . Analysis of testis sections for PRM2 revealed a premature translation pattern in a subset of spermatids in the seminiferous tubules of the CBF-A knockout mouse . Altered PRM1 and PRM2 distributions were also observed in a mosaic pattern in a Pbrp knockout mouse [53] . An emerging scenario therefore suggests that CBF-A and Prbp may work in tandem for the regulation of both PRM2 and PRM1 during spermatogenesis . Although other regulatory functions at the protein level cannot be excluded , including the possibility that CBF-A contributes to the general stability of a subset of factors involved in spermatogenesis , we favor the model that both CBF-A splice variants are part of a novel relay mechanism that regulates translation of several testicular transcripts and it is required during spermatogenesis .
All experimental procedures on mice were performed according to Karolinska Institute and Stony Brook University animal core facility guidelines for the care and use of laboratory animals . The human Fab monoclonal antibody against MVH was purchased from BD Biosciences . The rabbit polyclonal antibody against MIWI is from Cell Signaling . The rabbit polyclonal anti-histone H3 , and mouse monoclonal anti-fibrillarin , rabbit polyclonal anti-ACT antibodies were from Abcam whereas the goat polyclonal anti-PRM2 , goat polyclonal anti-Tnp2 , rabbit polyclonal anti-Tnp1 , mouse monoclonal anti-hnRNP A2/B1 , rabbit polyclonal anti-eIF4E , rabbit polyclonal anti-Tom20 , and goat polyclonal ACRV1 antibodies were purchased from Santacruz Biotechnology . The mouse monoclonal anti-PRM1 and anti-PRM2 antibodies are from Briar Patch Biosciences . Both rabbit ( ICCI ) and guinea pig ( SAK22 ) polyclonal abs against CBF-A were previously described by Raju et al . ( 2008 , 2011 ) [11] , [12] . Control non-specific mouse IgGs were from Abcam . Digoxigenin ( DIG ) -labeled RNA probes were synthesized by in vitro transcription using DIG RNA labeling mix ( Roche ) . PCR products of Prm2 amplified from mouse testis cDNA ( forward primer , 5′- ATG GTT CGC TAC CGA ATG AG; reverse primer , 5′- GGC AGG TGG CTT TGC TC ) were cloned into pGEM-T vector ( Promega ) and used as a template for the in vitro transcription . For preparation of cryosections , mouse testes were fixed with a solution of 4% paraformaldehyde ( PFA ) in 1× PBS for 5 h at 4°C , incubated with 15% sucrose in 1× PBS for 5 h and 30% sucrose in 1× PBS overnight , and subsequently embedded in the OCT compound ( Sakura Finetek ) . Before the hybridization step , 10 µm thick sections were mounted on glass slides and post-fixed with 4% PFA in 1× PBS . The sections were subsequently treated with 1 µg/ml Protease K in 10 mM Tris-HCl ( pH . 7 . 5 ) at 37°C for 5 min and acetylated by incubating slide for 10 min with 0 . 25% Acetic Anhydride in 1 . 0 M Triethanol amine HCl ( pH . 8 . 0 ) . Slides were rinsed in 0 . 85% NaCl for 5 min , and then incubated overnight at 60°C with Prm2 anti-sense or sense RNA probes diluted in hybridization buffer containing 10 mM Tris-HCl , pH 7 . 0 , 50% formamide , 0 . 2 ng/ml tRNA , 10% dextran sulfate , 1× Denhardt's solution , 600 mM NaCl , 0 . 25% SDS and 5 mM EDTA . The sections were serially washed with 50% formamide in 2× SSC , 2× SSC and 0 . 2× SSC at 65°C , and incubated with anti-DIG antibody conjugated with Dylight568 ( Jackson laboratory ) after blocking with 1 . 5% blocking reagent ( Roche ) . The sections were counter-stained with DAPI . Squash testis samples for immunostaining in the chromatoid body ( Figure 1J; Figure S2B ) were prepared as described [34] . Immuno-FISH ( Figure 3 ) was performed on the tubule squash samples prepared as described in [54] . 10 µm cryosections were prepared as described above . For antigen retrieval [33] , cryosections and squash preparations were washed with 1× PBS , treated with microwave for 5 min in 10 mM sodium citrate buffer , pH 6 . 0 , and permeabilized with a solution of 0 . 3% Triton-X in 1× PBS for 5 min . Slides were blocked with a solution containing 2% BSA , 0 . 05% Triton-X in 1× PBS and then incubated for 1 hr with the anti-CBF-A antibodies ICCI or SAK22 and anti-MVH or anti-hnRNP A2 antibodies . After washing with a solution of containing 0 . 05% Tween in 1× PBS , slides were incubated with species-specific fluorophore-conjugated secondary antibodies ( FITC-Donkey anti rabbit , Cy3-Donkey anti rabbit , Cy5-Donkey anti human and Alexa568-Donkey anti mouse ) for 1 hr at room temperature . For analysis , slides of untreated or microwave-treated testis sections as well as squash testis samples were visualized by light microscopy or laser scanning microscopy using an LSM510 confocal microscope ( Zeiss ) . The full-length open reading frame encoding CBF-A p42 was amplified from mouse hnRNP A/B cDNA plasmid ( MR226335 , ORIGENE ) using primer pairs as follows: forward primer , 5′- GCGC ( Bgl II ) ATG TCG GAC GCG GCT GAG G - 3′ and reverse primer , 5′- GCGC ( EcoRI ) TCA GTA TGG CTT GTA GTT ATT CTG - 3′ . The PCR products were cloned into pCRII ( Invitrogen ) , sequenced , and subcloned between BamHI and EcoRI sites of pGEX-4T-3 ( GE Healthcare ) for expression as glutathione S-transferase ( GST ) -tagged CBF-A . For the CBF-A p37 isoform , full-length GST-tagged CBF-A ( gift of Tomas Leanderson , Lund University ) was expressed from a pGEX plasmid vector according to manufacturer's instruction protocols ( GE Healthcare ) . 1 adult mouse testis was homogenized in 1 ml lysis buffer [1× PBS containing 0 . 2% NP-40 , 40 U RnaseOut ( Roche ) and the cOmplete Protease Inhibitor ( Roche ) ] by 20 strokes in a Dounce homogenizer at 4°C . For fractionation , the homogenates were centrifuged for 10 min at 1000 g , and the supernatant was collected as cytoplasmic extract . For nuclei fraction , the pellet was washed once with lysis buffer and resuspended . After sonication cytosolic and nuclear fractions were centrifuged at 15000 g for 15 min . The supernatants were used in immunoblotting or RNA immunoprecipitation assays . Lysates were pre-cleared with Protein A-Sepharose 4B conjugate ( Zymed , Invitrogen ) , and incubated overnight with anti-CBF-A antibodies , control anti-mouse IgGs or without antibodies ( mock experiments ) . The antibodies were subsequently precipitated with Protein A-Sepharose 4B conjugate ( Zymed , Invitrogen ) for 1 h under continuous agitation . Where indicated , protein extracts were treated with 100 µg/ml RNase A for 15 min at room temperature before incubation with antibodies . Precipitated samples were resolved by SDS-PAGE and analyzed on immunoblots with antibodies against CBF-A or hnRNP A2 . For analysis of the RNA species associated with CBF-A , the RNA was extracted from both input and immunoprecipitated fractions using the TRI reagent as described in the manufacturer's protocol ( Sigma ) and reverse-transcribed using Superscript II ( Invitrogen ) and oligo ( dT ) primers ( Invitrogen ) . An equal volume of RNA incubated without Superscript II was used as a negative control ( RT- ) . The samples were then analyzed by semi-quantitative PCR with primers specific to Prm2 ( see above for primers sequences ) , α-tubulin ( forward primer , 5′- TTC GTA GAC CTG GAA CCC AC; reverse primer , 5′- TGG AAT TGT AGG GCT CAA CC ) and clusterin ( forward primer , 5′- CTG GAG CCA AGC CGC AGA CC; reverse primer , 5′- GCA CTC CTC CCA GAG GGC CA ) . Quantifications of PCR products were performed over 3 independent experiments using the ImageJ software . These assays were performed as in [44] . Briefly , adult mouse testes were homogenized in 1× PBS supplemented with 0 . 2% NP40 and a cocktail of protease inhibitors ( cOmplete™ , Roche ) . 40 µl of a 1∶1 suspension of either 7-methyl-GTP ( m7GTP ) -Sepharose 4B ( Amersham Biosciences ) or protein G-Sepharose 4B ( Zymed ) were blocked with 2% BSA for 1 h at 4°C . Beads were subsequently incubated with 400 µl of cytoplasmic testes lysates overnight at 4°C with rocking . Beads were washed 3× with a solution containing 1× PBS supplemented with 0 . 2% NP-40 . Bound proteins were eluted by heat denaturation in SDS-containing Laemmli buffer . Eluted proteins were resolved by SDS-PAGE and analyzed on immunoblots for CBF-A , hnRNP A2 , eIF4E and tubulin . Where indicated , testicular lysates were treated with 100 µg/ml RNaseA before incubation with the m7GTP-Sepharose 4B beads . For RNA affinity chromatography , biotinylated RNA oligonucleotides Prm2 wtRTS ( 5′- GCCCUGAGCUGCCAAGGAGCCGUACUGAG ) as well as the previously described scrambled MBP RTS sequence ( 5′- GGGAGCGGAGAAACAAGCACCGAACCCGCAACUGG ) [11] were purchased from Thermo Fisher Scientific . Full-length wt Prm2 mRNA and Δ3′ UTR Prm2 mRNA were synthesized by in vitro transcription using Biotin 11-UTP ( Roche ) . 10 µl of streptavidin-Sepharose ( GE Healthcare ) were incubated with 100 pmol of oligonucleotide or 2 µg of transcribed RNAs in 100 µl of 1× PBS containing 0 . 1% NP40 for 30 min , washed once with 1× PBS containing 0 . 1% NP40 , and then incubated with 200 µl of mouse testis lysates . Bound proteins were resolved by SDS-PAGE and analysed on immunoblots with antibodies to CBF-A ( SAK22 ) and hnRNP A2 . The Prm2 wtRTS conjugated to streptavidin beads was also used for in vitro pull-downs with recombinant p37 , p42 and hnRNP A2 . Briefly , 10 µl of the RNA-conjugated beads were incubated with purified recombinantly expressed p37 , p42 and/or hnRNP A2 at final concentrations of 150 nM . Incubations were performed in 100 µl volume and allowed for 60 min at 4°C under continuous agitation . Bound proteins were eluted by heat denaturation in SDS loading buffer , resolved by SDS-PAGE and analyzed on immunoblots with antibodies to CBF-A ( SAK22 ) or hnRNP A2 . Sucrose gradient fractionation was carried out according to Unhavaithaya et al ( 2009 ) [38] . Briefly , one testis from wild-type , Hnrnpab+/− or Hnrnpab−/− mice was homogenized in 1 ml lysis buffer [100 mM NaCl , 3 mM MgCl2 , 20 mM HEPES ( pH 7 . 5 ) , 0 . 1% Triton , 1 mM dithiothreitol ( DTT ) , cOmplet Protease Inhibitor ( Roche ) , 20 units/ml RNaseOu ( Invitrogen ) ] , by 20 strokes in a Dounce homogenizer . The lysates were treated with cycloheximide , a translational elongation inhibitor , at a final concentration of 200 µM to stabilize polysomes . The lysate was then centrifuged at 1300 g at 4°C for 2 min to pellet nuclei and cell debris . The supernatant was immediately layered onto the top of an 8 ml gradient of 15–50% ( w/w ) sucrose dissolved in the lysis buffer . For EDTA treatments , MgCl2 normally present in all buffers was replaced with 20 mM EDTA . The gradient was centrifuged for 3 h at 150000 g at 4°C , and collected in 14 fractions . RNA was extracted from 150 µl of each fraction , resolved by agarose gel electrophoresis , and analyzed by ethidium bromide or Northern blotting with probes hybridizing to the Prm2 mRNA ( see above ) . Alternatively , proteins in remaining fractions were concentrated to a volume of 100 µl by TCA precipitation , and 15 µl of each fraction was resolved by SDS-PAGE and immunoblotted for CBF-A and hnRNP A2 . Mice were perfused with PBS containing heparin and 0 . 1 mM PMSF , and tissues were placed on ice before dissecting into fresh ice cold buffer . Protein lysates were made from one testis , and total RNA was extracted by Trizol from the other one . For Western blotting , 16 µg of protein samples were heat-denatured in 2× Laemmli buffer , separated on a 15% SDS-containing gel and transferred to a PVDF membrane . For Northern blotting , 2 µg of total RNA were separated on a 1 . 2% agarose gel . For each analysis , materials were individually collected from 3 animals per genotype . Sperm was collected from caput epididymis , dispersed into 1× PBS and kept at 4°C for analysis . Samples were then fixed in cacodylate buffer containing 2 . 5% glutaraldehyde and 4% paraformaldehyde for 2 h at room temperature , rinsed three times with the same buffer and post-fixed with 2% osmium tetraoxide for 1 h at room temperature . Spermatozoa were pelleted stepwise at 9000 rpm in an ultracentrifuge . Pre-embedding staining was performed with 1% uranyl acetate followed by sample dehydration through graded ethanol solutions embedded in epoxic-resin durcupan and polymerized for 48 h at 60°C . 80 nm ultrathin sections were collected on formvar/carbon-coated one-slot cupper grids ( Agar Scientific ) , contrasted with uranyl acetate and lead citrate before examination in a transmission electron microscope at 100 kV .
|
During eukaryotic gene expression , a fraction of newly exported mRNA molecules is transported to the cellular periphery for translation . The underlying mechanisms are not fully understood even though they likely affect specialized functions in many cell types including oligodendrocyets , neurons and germ cells . We discovered that the heterogeneous nuclear ribonucleoprotein CBF-A , interacts with a conserved sequence , the RNA trafficking sequence ( RTS ) , located in the untranslated region of transported mRNAs . This interaction facilitates transport of myelin basic protein mRNA and dendritic mRNAs in oligodendrocytes and neurons , respectively . Here we investigated whether RTS-recognition by CBF-A coordinates transport and localized translation of the Protamine 2 mRNA in spermatogenic cells . During spermatogenesis the Protamine 2 mRNAs is synthesized and kept in a silent form to be translated at later stages . We show that by interacting with the RTS of the Protamine 2 mRNA both CBF-A isoforms contribute to regulate the transcript at the translational level . In a CBF-A knockout mouse model , we demonstrate that the interplay between the CBF-A isoforms in translation regulation of the Protamine 2 mRNA and other testicular transcripts has an impact on spermatogenesis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
The Transacting Factor CBF-A/Hnrnpab Binds to the A2RE/RTS Element of Protamine 2 mRNA and Contributes to Its Translational Regulation during Mouse Spermatogenesis
|
Strongyloidiasis is a truly neglected tropical disease , but its public health significance is far from being negligible . At present , only a few drugs are available for the treatment and control of strongyloidiasis . We investigated the activity of tribendimidine against third-stage larvae ( L3 ) of Strongyloides ratti in vitro and against juvenile and adult stages of the parasite in vivo . S . ratti larvae incubated in PBS buffer containing 10–100 µg/ml tribendimidine died within 24 hours . A single 50 mg/kg oral dose of tribendimidine administered to rats infected with 1-day-old S . ratti showed no effect . The same dose administered to rats harboring a 2-day-old infection showed a moderate reduction of the intestinal parasite load . Three days post-exposure a significant reduction of the immature worm burden was found . Administration of tribendimidine at doses of 50 mg/kg and above to rats harboring mature S . ratti resulted in a complete elimination of the larval and adult worm burden . For comparison , we also administered ivermectin at a single 0 . 5 mg/kg oral dose to rats infected with adult S . ratti and found a 90% reduction of larvae and a 100% reduction of adult worms . Tribendimidine exhibits activity against S . ratti in vitro and in vivo . The effect of tribendimidine in humans infected with S . stercoralis should be assessed .
An estimated 30–100 million people are infected with Strongyloides stercoralis , the causative agent of strongyloidiasis , and yet this is a truly neglected tropical disease [1] . One explanation is that current diagnostic tools have limitations [2] . Whilst S . stercoralis mainly occurs in tropical and subtropical areas , endemic foci also occur in temperate regions such as Spain or the United States [3] . Serious clinical problems have been observed in S . stercoralis-infected patients who are immunocompromised due to a co-infection with human T-cell leukaemia virus type 1 ( HTLV-1 ) or HIV , or corticosteroid-treated patients [4] . However , the global burden of strongyloidiasis is currently not known . The growing evidence that an infection with S . stercoralis is a risk factor for biliary tract cancer needs to be considered when estimating the burden of strongyloidiasis [5] . As with other nematodes ( Ascaris lumbricoides , hookworms and Trichuris trichiura ) and trematodes ( e . g . Schistosoma spp . ) , there are only a few drugs available for the treatment and control of strongyloidiasis [6] . Albendazole and mebendazole were found to be safe , but multiple treatment courses repeated over several weeks were required to achieve acceptable cure rates [7] . Another benzimidazole , thiabendazole , is highly efficacious ( two treatment courses of 25–50 mg/kg are commonly given for 3–4 days 2 weeks apart ) , but severe adverse events , including liver dysfunction and neuropsychiatric symptoms , have been observed [8] . Ivermectin , a semi-synthetic macrocyclic lactone , which was developed as a veterinary anthelmintic , is safe and efficacious , and is now the drug of choice for strongyloidiasis [9] . Ivermectin resistance in humans infected with S . stercoralis has not been reported thus far , but host and parasite-specific resistance to the drug has been reported in veterinary medicine [9] . Tribendimidine is an aminophenyldimidine derivative of amidantel . Tribendimidine is safe and has a broad spectrum of activity against numerous nematode species , including A . lumbricoides , Enterobius vermicularis and the hookworms ( Ancylostoma duodenale and Necator americanus ) [10] . Tribendimidine has been approved by Chinese regulatory authorities in 2004 [10] and phase IV trials have been completed recently [11] . Efforts are ongoing to secure a western registration for tribendimidine , in order for the treatment to be considered for global soil-transmitted helmithiasis control usage . In our recent work , we have documented the in vivo activity of tribendimidine against a number of trematodes , namely the intestinal fluke Echinostoma caproni [12] and the two liver flukes Clonorchis sinensis and Opisthorchis viverrini [13] . Here , we investigated the in vitro activity of tribendimidine against third-stage larvae ( L3 ) of Strongyloides ratti . Moreover , we evaluated the dose-response relationships of single oral doses of tribendimidine against adult S . ratti harbored in rats , and assessed the in vivo activity of tribendimidine against different immature stages of the parasite in the rat model .
All in vivo studies presented here were carried out at the Swiss Tropical Institute ( Basel , Switzerland ) in accordance with Swiss national animal welfare regulations ( permission no . 2081 ) . Male Wistar rats ( n = 48 , age: 3 weeks , weight: ∼80 g ) were purchased from RCC ( Itingen , Switzerland ) . Rats were kept in groups of 4 in macrolon cages under environmentally-controlled conditions ( temperature: ∼25°C; humidity: ∼70%; 12 h light and 12 h dark cycle ) and acclimatized for 1 week . The S . ratti life cycle has been maintained at our institute for 15 years by serial passage through rats . S . ratti L3 were obtained from the faeces of infected rats following standardized procedures based on the Baermann technique [14] . Tribendimidine was synthesized and kindly provided by the Department of Pharmaceutics , National Institute of Parasitic Diseases , Chinese Center for Disease Control and Prevention ( Shanghai , China ) . Ivermectin was purchased from Sigma Aldrich ( Buchs , Switzerland ) . A stock solution of tribendimidine at 10 mg/ml was prepared with 60% DMSO for the in vitro studies . For the in vivo studies , both drugs were prepared in homogenous suspensions in 7% Tween-80 and 3% ethanol before oral administration . Freshly harvested S . ratti L3 were washed 3 times with PBS buffer and incubated in 6-well microtiter plates ( Costar ) containing 4 ml PBS buffer ( pH 7 . 3 ) . 800 S . ratti L3 were used for each control and experimental group . The worms were incubated in three serial drug dilutions of tribendimidine , i . e . 100 , 10 and 1 µg/ml for up to 96 hours ( h ) . Each experiment was carried out in triplicate and then repeated once . The control wells contained 0 . 06% DMSO . Cultures were kept at 25°C in an atmosphere of 5% CO2 . Larval counts were performed immediately after exposure and at 1 , 2 , 24 , 48 , 72 and 96 h post-exposure under a dissecting microscope . Rats were infected subcutaneously with 1300 freshly harvested S . ratti L3 . To analyse the effect of tribendimidine on adult S . ratti , 5 days post-exposure , 5 groups with 4 rats each were administered tribendimidine at doses of 200 , 100 , 50 , 25 and 12 . 5 mg/kg , respectively ( first set of experiments: doses 50–200 mg/kg; second set of experiments: doses 12 . 5 and 25 mg/kg ) . Ivermectin was given to 4 rats at a single 0 . 5 mg/kg oral dose . Eight rats were left untreated and served as controls . Stool samples from all rats investigated were collected shortly before treatment and daily for 4 days after treatment . From each treatment group 250 mg stool was homogenized with 2 . 5 ml PBS buffer and 10 aliquots of 10 µl stool suspension were analyzed for the presence of rhabditiform larvae . The average rhabditiform larvae count per gram of stool was calculated . Seven days post-treatment rats were euthanised by CO2 . At necropsy the intestine was removed from the pylorus to the ileocecal valve , placed in a Petri dish containing 10 ml of PBS buffer ( pH 7 . 3 ) opened longitudinally and incubated for 3 h . Ten aliquots of 10 µl PBS were analyzed for the presence of rhabditiform larvae and adults and the average worm counts were recorded . To determine whether tribendimidine has an effect on the tissue stages of S . ratti , 12 rats were treated intragastrically , either on day 1 , 2 or 3 post-exposure ( 4 rats each ) , with a single 50 mg/kg oral dose of tribendimidine . One group with 4 infected but untreated rats served as control . Stool samples were collected between days 5 and 9 post-exposure and processed as described above . Rats were sacrificed on day 9 post-exposure and the intestine was analysed for the presence of S . ratti adults and rhabditiform larvae as explained before . Statistical analyses were done with version 2 . 4 . 5 of Statsdirect statistical software ( Statsdirect Ltd; Cheshire , UK ) . The effect of tribendimidine was assessed by comparing the mean number of S . ratti rhabditiform larvae in the stool and S . ratti adults and rhabditiform larvae in the intestine in the treatment group with the mean number of larvae and adults in the respective control group . The responses between the medians of the treatment and control groups regarding larvae and stool and larvae and adults in the intestines were analysed with the Kruskal-Wallis ( KW ) test . Differences in medians were considered to be significant at a significance level of 0 . 05 .
Table 1 summarizes the effect of S . ratti L3 after exposure to tribendimidine at different concentrations in vitro . S . ratti exposed to tribendimidine at 10 or 100 µg/ml contracted immediately and had a coiled shape appearance . The worms died within 24 h . No effect was observed with the lowest concentration ( 1 µg/ml ) of tribendimidine: 66 . 6% of S . ratti were still active after an incubation period of 72 h . Ninety-six h post-incubation 58 . 4% of S . ratti showed no movement when exposed to this concentration , similar to the control group , where 60 . 0% of the worms were found to be inactive . Figure 1 shows the effect of tribendimidine on S . ratti rhabditiform larvae harvested from rat fecal samples as assessed by quantitative stool examination . In the first set of experiment ( evaluating single 50 , 100 and 200 mg/kg oral doses of tribendimidine ) on 5 daily stool examinations , between 3300 ( 6 days post-exposure ) and 10 , 000 ( 9 days post-exposure ) rhabditiform larvae per gram of stool were estimated in the group of untreated control animals . No larvae were found in fecal samples obtained from rats treated with single 100 or 200 mg/kg oral doses of tribendimidine and 0 . 5 mg/kg ivermectin commencing 48 h post-treatment . While no larvae were found at 48 h and 96 h post-treatment with 50 mg/kg tribendimidine , a low mean of 100 larvae per gram of stool was estimated 72 h post- treatment ( Figure 1 ) . Untreated rats in the second set of experiment ( assessing the activity of 12 . 5 and 25 mg/kg tribendimidine ) passed between 1000 and 18 , 600 S . ratti rhabditiform larvae per gram of stool on days 5 to 9 post-exposure . Larvae were also present in the stools of rats treated with 25 and 12 . 5 mg/kg tribendimidine; the highest numbers of rhabditiform larvae were detected 72 h post-treatment , namely 1300 and 2900 larvae per gram of stool in rats treated with 25 and 12 . 5 mg/kg , respectively ( Figure 2 ) . However , treatment had a significant effect on larvae presence in stools ( KW = 67 . 1 , degree of freedom ( df ) = 2; P < 0 . 001 ) . The effect of tribendimidine against S . ratti rhabditiform larvae and adults in the intestine , as assessed by worm burden reduction , is summarized in Table 2 . The untreated control rats harbored a mean of 1012 S . ratti rhabditiform larvae and 413 adult worms in their intestines . Tribendimidine given at doses of 50 , 100 and 200 mg/kg resulted in complete elimination of larvae and adult worms . For comparison , ivermectin administered at 0 . 5 mg/kg achieved a significant larval reduction ( 90 . 0%; KW = 4 . 58 , P = 0 . 032 ) and a complete elimination of adult worms . The second control group harbored 750 S . ratti adults and 2025 larvae in their intestines . Tribendimidine at 25 mg/kg produced a 91 . 4% reduction of S . ratti larvae and a complete reduction of adult worms . When tribendimidine was given at 12 . 5 mg/kg , the adult worm burden was reduced by 83 . 3% and the larval burden by 54 . 4% . There was a significant difference in the larval ( KW = 6 . 83 , df = 2 , P = 0 . 041 ) and the adult worm ( KW = 9 . 46 , df = 2 , P = 0 . 009 ) burden between these 2 treatments and the control groups . Figure 3 shows the effect of tribendimidine given 1–3 days post-exposure on S . ratti rhabditiform larvae present in stool . The number of rhabditiform larvae in the control group increased from 700 on day 5 post-exposure to 6700 rhabditiform larvae per gram of stool 4 days later . A larval reduction ranging from 30 . 6% ( 6 days post-exposure ) to 73 . 1% ( 9 days post-exposure ) was observed in fecal samples of rats treated with 50 mg/kg tribendimidine on day 1 post-exposure . In rats treated with tribendimidine at 50 mg/kg on day 2 post-exposure , a reduction of rhabditiform larvae ranging between 50 . 0% ( 7 days post-exposure ) and 83 . 6% ( 9 days post-exposure ) was observed . Finally , no rhabditiform larvae were found in 5 consecutive stool samples from rats treated with 50 mg/kg of tribendimidine on day 3 post-exposure . Administration of tribendimidine to rats harboring tissue stages of S . ratti had a significant effect on the presence of larvae in stool ( KW = 14 . 1; df = 3 , P = 0 . 002 ) . Table 3 summarizes observed larvae and adult worm burden reductions in the intestines of rats following treatment with tribendimidine . Administration of a single 50 mg/kg oral dose of tribendimidine on day 1 post-exposure to S . ratti-infected rats showed no effect on the intestinal larval and adult parasite load . Treatment of infected rats 48 h post-exposure with a single 50 mg/kg oral dose of tribendimidine resulted in larvae and adult worm burden reductions of 41 . 0–61 . 5% . Finally , when tribendimidine ( 50 mg/kg ) was given 72 h post-exposure , a 98 . 9% reduction of larvae in the intestines was observed . There was a significant difference between the number of larvae ( KW = 9 . 65 , P = 0 . 021 ) and adult worms ( KW = 6 . 29 , P = 0 . 098 ) recovered from the intestines of treated and non-treated control rats .
Discovered in the mid-1980s [15] , detailed laboratory investigations and subsequent clinical testing for safety and efficacy have led to tribendimidine being registered in China , in early 2004 , as an anthelmintic drug with a broad spectrum of activity . In experimentally-infected animals , tribendimidine showed excellent activity against major nematode infections , i . e . A . lumbricoides and the hookworms , particularly N . americanus [10] . Tribendimidine has also shown excellent activity against a number of trematodes , e . g . C . sinensis , E . caproni and O . viverrini in different rodent models [12] , [13] . Here , we extended in vitro and in vivo activity testing of tribendimidine to yet another helminth , namely S . ratti , which is a commonly used experimental model of a nematode infection [16] . A single 50 mg/kg oral dose of tribendimidine administered to rats harboring adult S . ratti resulted in complete worm burden reductions . The same dose given to rats infected with juvenile S . ratti revealed a significant reduction of the larval burden already 3 days post-exposure . However , no effect was found when tribendimidine was administered 1 day post-exposure and only a moderate effect was observed at day 2 post-exposure . Whether immature S . ratti are less sensitive to tribendimidine or whether drug levels are lower in the tissues where larvae reside at this age remains to be investigated . Further studies are necessary to investigate the effect of higher and also multiple doses of tribendimidine against immature S . ratti . It is interesting to compare our results with a 2-day treatment regimen of ivermectin , the current drug of choice for strongyloidiasis . Two doses of 0 . 3 mg/kg ivermectin resulted in complete worm burden reduction of adult S . ratti in mice [17] . A 2-day dose of 0 . 5 mg/kg ivermectin was highly efficacious against the migrating and lung stages of this parasite , resulting in worm burden reductions of 91–96% [18] . In our own experiment , a single 0 . 5 mg/kg oral dose of ivermectin resulted in a 90% larval reduction and a complete elimination of adult worms . On the other hand , albendazole or thiabendazole were found to be less efficacious against immature S . ratti even when multiple doses were administered . For example , a 3-day treatment schedule of 50 mg/kg albendazole and thiabendazole administered on days 4–6 post-exposure cured rats infected with adult S . ratti . However , no or only low cure rates ( up to 33% ) were observed when these drugs were administered during the lung and tissue stages of the parasite on days 1 , 2 and 3 post-exposure [19] . Interestingly , 2 doses of 50 mg/kg mebendazole were found to be highly efficacious against the tissue stages of S . ratti [20] . Our in vitro studies revealed that worms incubated in the presence of 100 µg/ml tribendimidine died within 2 h . The worms had a coiled appearance . It has been suggested that tribendimidine , which , similar to amidantel is biotransformed into p- ( 1-dimethlyamino ethylimino ) aniline ( S . H . Xiao , pers . comm . ) , acts as agonist at the level of the acetylcholine receptor [21] . The rapid onset of action of tribendimidine is supported by our in vivo studies since no larvae were found in stools of rats treated with at least 50 mg/kg of tribendimidine 48 h post-treatment . A previous investigation showed that tribendimidine acted similarly rapidly when it was administered to mice infected with the intestinal trematode E . caproni . Scanning electron microscopic investigations revealed that severe damage of the tegument already occurred 2 h after drug administration and 8 h post-treatment the majority of worms had been expelled [12] . Concluding , we have documented in vitro and in vivo activities of tribendimidine against S . ratti . Our findings warrant further investigations , which is justified as follows . First , discovery and development of novel anthelmintic drugs in general [22] , [23] and strongylocidal drugs in particular , is limited . Hence , over the past decade only a few compounds have been examined in the S . ratti-rat model [24] , [25] . Second , tribendimidine has recently been registered in China as an anthelmintic drug , and it might thus be deployed as an additional control tool against major helminth infections [26] . Efforts are ongoing to pursue registration of tribendimidine in a 1st tiered regulatory agency so that the drug could eventually be integrated in global helminth control programs . Since many helminth infections show large geographical overlaps , it will be important to monitor the effect of tribendimidine on concomitant infections of different nematodes and trematodes . We are currently in the process of examining the effect of tribendimidine against S . stercoralis in people co-infected with this parasitic roundworm and other nematodes and trematodes .
|
Although an estimated 30–100 million individuals are infected with the parasitic roundworm Strongyloides stercoralis , which can cause strongyloidiasis , it is a so-called neglected tropical disease . There are only very few drugs available for treating strongyloidiasis . We evaluated the strongyloicidal properties of the anthelmintic compound tribendimidine in vitro and in an animal model . Larvae from S . ratti incubated in PBS buffer containing 10–100 µg/ml tribendimidine died within 24 hours . Tribendimidine showed a significant activity against adult S . ratti harbored in rats: oral administration of tribendimidine at single doses of 50 mg/kg and above resulted in a complete elimination of larvae and adult worms . A single 50 mg/kg oral dose of tribendimidine was less effective against the migrating tissue stages , in particular the 1- and 2-day-old S . ratti . In view of our findings , the effect of tribendimidine against S . stercoralis infections in humans should be assessed .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/helminth",
"infections"
] |
2008
|
Strongyloides ratti: In Vitro and In Vivo Activity of Tribendimidine
|
The human immunodeficiency virus type-1 ( HIV-1 ) Rev protein regulates the nuclear export of intron-containing viral RNAs by recruiting the CRM1 nuclear export receptor . Here , we employed a combination of functional and phylogenetic analyses to identify and characterize a species-specific determinant within human CRM1 ( hCRM1 ) that largely overcomes established defects in murine cells to the post-transcriptional stages of the HIV-1 life cycle . hCRM1 expression in murine cells promotes the cytoplasmic accumulation of intron-containing viral RNAs , resulting in a substantial stimulation of the net production of infectious HIV-1 particles . These stimulatory effects require a novel surface-exposed element within HEAT repeats 9A and 10A , discrete from the binding cleft previously shown to engage Rev's leucine-rich nuclear export signal . Moreover , we show that this element is a unique feature of higher primate CRM1 proteins , and discuss how this sequence has evolved from a non-functional , ancestral sequence .
HIV-1 is unable to replicate in most non-human species due to species-specific differences in cellular factors that either inhibit or promote viral replication . In particular , non-human versions of the cellular restriction factors APOBEC3G , TRIM5α and tetherin/BST-2/CD317 can each potently inhibit HIV-1 replication because the HIV-1 encoded evasion strategies ( e . g . , the viral Vif and Vpu proteins ) are ineffective [1] . In other instances , HIV-1 does not replicate in certain species due to the lack of functional versions of cellular proteins necessary for completion of key aspects of the viral life cycle . Mice and other rodents represent notable examples and exhibit multiple cellular deficiencies in pathways required for efficient HIV-1 replication [2] . While these deficiencies have impeded the development of a small animal model with which to study HIV-1 , murine cell lines have served as powerful tools for delineating important molecular attributes of species-specific HIV-1 co-factors , including the CD4 entry receptor [3] , [4] and CCR5 co-receptor [5] , as well as the cyclin T1 ( CycT1/CCNT1 ) transcription co-factor [6] , [7] . Significantly , the combined provision of human versions of CD4 , co-receptor ( CCR5 or CXCR4 ) and CycT1 to murine cell lines does not restore HIV-1 replication , largely reflecting additional deficiencies that affect post-transcriptional steps of the virus life cycle [8]–[10] . The HIV-1 genomic RNA ( gRNA ) serves as the viral mRNA encoding the Gag and Gag-Polymerase ( Gag-Pol ) structural proteins , the genetic substrate that is packaged by Gag into virions , and as an RNA scaffold that facilitates Gag-Gag interactions [11] . Moreover , the full-length gRNA also represents the viral pre-RNA , with the potential to undergo splicing in the nucleus to generate the entire repertoire of viral mRNAs . Therefore , full-length gRNA and a subset of partially spliced viral mRNAs retain functional introns; this represents a specific challenge for retroviruses because mRNAs containing introns are typically prevented from exiting the nucleus [12] . HIV-1 overcomes this barrier through the activity of its regulatory protein Rev . Rev is expressed from fully spliced viral mRNAs and targeted to the nucleus where it binds and multimerizes on a cis-acting HIV-1 RNA target called the Rev response element ( RRE ) found only within HIV-1 intron-containing mRNAs . Subsequently , Rev binds the cellular chromosomal region maintenance-1 ( CRM1 , also known as exportin-1/XPO-1 ) nuclear export receptor through its leucine-rich nuclear export signal ( NES ) thereby forming the viral ribonucleoprotein transport complex [13] . CRM1 is a member of the karyopherin-β family of nuclear transport receptors regulated by the small GTPase Ran , and engages NES-containing cargoes in the nucleus prior to transporting them through the nuclear pore complex for release into the cytoplasm [14] . CRM1-mediated nuclear export of gRNA therefore acts a switch to initiate the late stages of the viral life cycle , because the cytosolic accumulation of gRNA is necessary for the expression of the Gag and Gag-Pol proteins that ultimately assemble the virus capsid . In mouse cells expressing hCycT1 , the cytoplasmic abundance of HIV-1 gRNA and Gag protein synthesis are significantly reduced in comparison to human cells , and Gag is not efficiently targeted to plasma membrane assembly sites [6] , [8] , [9] , [15]–[19] . HIV-1 particle production can be restored in mouse cells by either modulating Gag's amino-terminal matrix ( MA ) membrane targeting domain in ways that enhance membrane binding [15] , [16] , [18]–[20] or by reprogramming the nuclear export pathway used by Gag-encoding mRNAs without modifying the Gag coding region [18] , [19] , [21] . More specifically , we have demonstrated that replacing the RRE in intron-containing Gag mRNAs with four copies of the constitutive transport element ( CTE ) from Mason-Pfizer monkey virus ( M-PMV ) effectively restores efficient virus particle assembly in mouse cells [19] . The CTE mediates M-PMV gRNA nuclear export independently of CRM1 [22] , leading us to propose that the nuclear export of RRE-encoding transcripts and Gag assembly competence are linked mechanistically [11] , [18] , [19] . Fusing HIV-1 infected mouse cells with human cells results in vastly improved levels of virus production , indicating that one or more human cellular factors function to complement these murine-specific defects [8] , [9] . Mouse-human somatic cell hybrids were used to map the relevant gene ( s ) to human chromosome 2 ( Ch2 ) [23] , and recent work from Shida and colleagues studying rat cells identified species-specific activity in CRM1 , a gene product of Chr2 [24] , [25] . Here , we demonstrate that human CRM1 ( hCRM1 ) rescues a defect in the nucleocytoplasmic transport of viral intron-containing RNAs , including the gRNA . The molecular determinant of CRM1 underlying this stimulatory activity is a defined cluster of amino acids on the outer face of hCRM1's ringed structure , discrete from the hydrophobic cleft that binds the leucine-rich Rev NES . Moreover , combined phylogenetic and functional analyses indicate that the stimulatory activity conferred by this element may have evolved exclusively in higher primates .
To address the hypothesis that defects in RRE-dependent virus production in murine cells reflect the lack of functional human versions of one or more factors , we screened a panel of human cDNAs encoding proteins with known functions in post-transcriptional regulatory pathways . These cDNAs were co-expressed with GP-RRE transcripts and Rev in 3T3 cells and we assayed for improvements to RRE/Rev-dependent Gag expression and VLP production [26] . In this screen , we identified hCRM1 as a factor whose expression led to an increase to VLP production relative to a luciferase control ( Figure 1B , compare lane 4 to lane 2 ) . hCRM1 effects on VLP production from GP-RRE transcripts were dependent on Rev expression ( Figure 1B , compare lane 4 and lane 5 ) and were not exerted on GP-4xCTE transcripts that do not rely on Rev-dependent nuclear export ( Figure 1B , compare lane 4 to lane 9 ) . Subsequent experiments suggested that hCRM1 displayed substantially more activity in 3T3 cells than the murine version of CRM1 ( mCRM1 ) , indicating that the effect might reflect species-specific activity ( Figure 1B , compare lane 4 to lane 3 ) . By contrast , neither mCRM1 nor hCRM1 expression affected VLP production in human HeLa cells from either RRE/Rev-dependent or 4xCTE-dependent transcripts ( Figure 1B , right panel ) . Taken together , these results highlighted mCRM1 as a candidate for the source of the defect to RRE/Rev-dependent HIV-1 virion production in 3T3 cells . We further assessed hCRM1 effects on RRE/Rev-dependent VLP production in a variety of cell lines , using an ELISA to quantify p24Gag ( capsid ) levels in the cell supernatant at ∼48 h post-transfection ( Figure 2 ) . hCRM1 expression enhanced p24Gag levels relative to mCRM1 in both 3T3 and murine Ltk- cells ( Figure 2A , samples 1-8 ) but did not differentially affect VLP production in cells of human origin including human osteosarcoma ( HOS ) cells and HeLa cells ( Figure 2A , samples 9-16 ) , or in African green monkey Cos7 cells ( Figure 2A , samples 17-20 ) . Importantly , HOS cells exhibit low levels of Gag expression similar to 3T3 cells [20] and were not affected by hCRM1 expression ( Figure 2A , compare samples 11 and 12 , and Figure 2B , compare lanes 7 and 8 ) , suggesting that hCRM1 responsiveness is not merely a corollary of low levels of Gag expression . mCRM1 expression consistently resulted in slight increases to VLP production in murine cell lines relative to a luciferase control ( e . g . , Figure 1B , Figure 2A ) , so that we directly compared the relative activities of mCRM1 and hCRM1 . Varying amounts of myc epitope-tagged versions of these proteins were expressed with GP-RRE transcripts and Rev prior to detection by immunoblot using an anti-myc antiserum ( Figure 3A ) . myc-hCRM1 was substantially more active than myc-mCRM1 in stimulating VLP production , even at lower levels of abundance ( Figure 3A , compare lanes 3-5 to lane 2 ) . The myc tag also allowed us to demonstrate by indirect immunofluorescence that both proteins exhibited similar intracellular distributions in 3T3 cells , localizing predominantly to the nucleus but with pronounced accumulation at the nuclear membrane ( Figure 3B ) . To further test hCRM1 species-specificity , we established 3T3 cell lines that stably expressed GFP-tagged versions of mCRM1 ( 3T3 . GFP-mCRM1 ) or hCRM1 ( 3T3 . GFP-hCRM1 ) . Compared to the parental cell line , VLP production was improved ∼4-fold for the cells expressing GFP-hCRM1 relative to GFP-mCRM1 , despite similar levels of transgene expression relative to endogenous CRM1 ( Figure 3C , compare lane 3 to lanes 1 and 2 ) . In sum , these experiments demonstrated that hCRM1 exhibits species-specific activity compared to mCRM1 in enhancing HIV-1 particle production . To evaluate the functional consequences of hCRM1 expression on the individual post-transcriptional stages of the HIV-1 life cycle , we assessed hCRM1 effects in the context of the full-length HIV-1NL4-3 provirus ( Figure 4 ) . To ensure efficient Tat-dependent transcription from the HIV-1 promoter , we co-expressed a previously described version of murine CycT1 ( tyrosine-261 changed to cysteine; Y261C ) that is fully Tat-responsive in mouse cells [6] , [27] . Consistent with the GP-RRE system , myc-hCRM1 expression increased HIV-1 particle release ∼6-fold relative to myc-mCRM1 as measured by ELISA ( Figure 4A , compare lanes 3 and 4 ) . myc-hCRM1 did not affect a Rev-deficient ( NL4-3/Rev-minus ) provirus , confirming that these effects were Rev-dependent ( Figure 4A , lane 5 ) . We also tested if these viruses were infectious by harvesting cell supernatants at 48 h post-transfection and adding them to TZM reporter cells ( Figure S1 ) . The combined expression of mCycT1-Y261C and myc-hCRM1 resulted in a ∼100-fold increase in infectious virus production from 3T3 cells relative to the expression of mCycT1-Y261C alone ( Figure S1A , compare lanes 2 and 5 ) and , when normalized for levels of input p24Gag , this virus exhibited comparable infectivity to viruses harvested from HeLa cells ( Figure S1B ) . To test the effects of hCRM1 expression on HIV-1 RNA abundance in the cytoplasm , we performed northern blotting on samples from an experiment identical to that in Figure 4A , using a probe that detects the full repertoire of HIV-1 mRNAs that includes ∼9 kb unspliced , ∼4 kb partially-spliced and ∼2 kb fully-spliced transcripts . The intron-containing ∼9 kb and ∼4 kb transcripts harbor the RRE and require Rev for their nuclear export while the accumulation of ∼2 kb transcripts in the cytoplasm is independent of Rev activity . The intron-containing RNAs accumulated to low levels in the cytoplasm of 3T3 cells expressing wild-type provirus and , as anticipated , were absent from the cytoplasm in cells expressing a Rev-minus mutant ( Figure 4B , compare lanes 2 and 5 ) . The relative abundance of cytoplasmic ∼9 kb unspliced RNA ( gRNA ) was increased ∼4-fold by hCRM1 relative to mCRM1 ( Figure 4B , compare lane 3 to lane 4 ) . We next directly compared hCRM1 effects on Gag synthesis rates and virus particle production . myc-hCRM1 expression led to an enhanced rate of Gag translation relative to myc-mCRM1 at all levels of input plasmid ( Figure 4C ) as measured by metabolic labeling , correlating well with the observed increases to gRNA levels in the cytoplasm . Interestingly , relative effects on net virus particle release as measured by p24Gag ELISA for these conditions were ∼3-fold higher than the increase in translation rate ( Figure 4C , compare lanes 3 and 6 , black bars ) . Taken together , the results presented in Figure 4 demonstrated that the ectopic expression of hCRM1 in murine cells increases the cytosolic abundance gRNA , resulting in improved Gag expression and a more pronounced boost to the efficiency of virus particle production . In mouse cells , it is well-established that virus particle assembly is enhanced by modifications of Gag amino-terminal matrix domain ( MA ) that enhance Gag-membrane association [15] , [16] , [18]–[20] . MA encodes a bipartite plasma membrane targeting signal consisting of a hydrophobic myristoylation that modifies the amino-terminal glycine residue and a patch of basic amino acids distributed between amino acids 15 and 33 ( Figure 5A ) [30] . Gag membrane targeting is thought to be regulated by a myristoyl switch mechanism wherein the myristoyl group is sequestered within the MA globular head domain unless exposed in response to stimuli including Gag-Gag interactions and binding to the plasma membrane resident phosphoinositide PI ( 4 , 5 ) P2 [31] , [32] . We recently described a Gag mutant carrying a single change to a non-charged amino acid , leucine-21 to serine ( L21S ) ( highlighted in Figure 5A ) , that dramatically improves Gag assembly efficiency in murine cells , likely by circumventing the myristoyl switch mechanism in order to constitutively target Gag to the plasma membrane [18] , [33] . To test if hCRM1 effects on Gag assembly in 3T3 cells are MA-dependent , we expressed Gag from GP-RRE subgenomic transcripts encoding wild-type Gag and Gag-L21S with myc-mCRM1 or myc-hCRM1 and measured virus assembly efficiency by calculating a “release factor” representing the ratio of released Gag to cell-associated Gag at 48 h post-transfection ( Figure 5B ) . These experiments were performed under conditions where the viral protease was inactivated so that Gag levels could be measured by quantitative immunoblot as a discrete , uncleaved 55 kDa species . hCRM1 expression significantly enhanced the assembly efficiency of wild-type , Rev-dependent Gag ( GP-RRE ) relative to mCRM1 almost 5-fold , corresponding to an increased Gag translation rate of ∼ 2-fold ( Figure 5B , compare lane 3 to lane 4 ) . Moreover , these effects were CRM1-dose dependent ( Figure S2 ) . By contrast , hCRM1 had relatively little impact on the assembly efficiency of the Gag-L21S mutant that constitutively targets the plasma membrane [18] ( Figure 5B , compare lanes 5 and 6 ) . Overall VLP output of Gag-L21S increased ∼2-fold in the presence of hCRM1 , corresponding not to better assembly efficiency but to improvements in Gag synthesis rates ( Figure 5B , lower panel , compare lanes 5 and 6 ) . Consistent with a rescue of Gag trafficking to virus assembly sites , single-cell visual analysis of Gag distribution under these conditions revealed a striking accumulation of Gag at the plasma membrane in 43% of cells expressing hCRM1 compared with 17% for mCRM1 ( Figures 5C ) . In sum , hCRM1 expression promotes Gag's ability to traffic to the plasma membrane in mouse cells and efficiently assemble into virus particles . The myristoyl switch in Gag is regulated by Gag multimerization , which is a cooperative process [32] , [34] . Since hCRM1 exerted moderate effects on Gag expression in mouse cells but amplified effects on the production of virus particles ( Figures 4C and 5B ) , we asked if these effects were cooperative and due to achieving a threshold level of intracellular Gag or , instead , reflected a second function for hCRM1 in modulating MA-dependent Gag membrane targeting . We titrated Gag expression plasmids in the presence of hCRM1 to achieve intracellular levels of Gag equivalent to that observed in the absence of hCRM1 expression . At comparable levels of Gag for either condition , we observed nearly identical levels of VLP production ( Figure 5D top panel , compare lanes 1 and 3 ) . Moreover , the magnitude of the release factor for virion production correlated with the intracellular abundance of Gag ( Figure 5D , bottom panel ) . Therefore , hCRM1's effects on HIV-1 assembly in mouse cells can be attributed to its ability to enhance Gag expression ( Figures 4C and 5B , lower panel ) , which is achieved through increasing the cytoplasmic level of gRNA . To assess the relevance of individual domains of CRM1 to HIV-1 Rev function , we constructed eight myc-tagged CRM1 chimeras , alternating the mouse or human species identity of the amino- , central- , and carboxyl- portions of the protein as depicted in Figure 6A . When co-transfected with GP-RRE and Rev plasmids into 3T3 cells , we observed significant increases in VLP production whenever the central region ( residues 381 to 800 ) of the chimera was derived from hCRM1 ( Figure 6B , lanes 3 , 4 , 7 and 8 ) . For instance , the mouse-human-mouse ( MHM ) chimera exhibited activity while the human-mouse-human ( HMH ) chimera did not , demonstrating that the activity within the central domain was both transferable and sufficient ( Figure 6B , compare lanes 6 and 7 ) . CRM1 is a toroid-shaped molecule that is remarkably well-conserved throughout the animal kingdom , with murine and human versions of CRM1 differing at only 21 of 1071 amino acids ( 98% identity ) . CRM1 consists of 21 “HEAT” repeats; antiparallel alpha helices wherein the “A” helix faces outward on the convex face of the molecule and the “B” helix faces inward as depicted in Figure 6C . Recent structural work provides strong evidence for a model wherein Ran-GTP ( in purple ) binds to the inner surface of CRM1 and triggers allosteric changes in the hydrophobic NES binding pocket located within HEAT repeats 11 and 12 , thereby promoting the binding of an NES-bearing cargo ( Rev NES in blue ) to form the trimeric CRM1/Ran/cargo export complex [35]-[38] . The mCRM1 and hCRM1 proteins differ at only seven positions within the central domain ( Figure 6C , specific residues are highlighted in red ) , all of which are located on the outward-facing “A” helices of HEAT repeats 9A and 10A , with the exception of amino acid 402 that is situated on the loop just upstream of HEAT repeat 9A . HEAT repeats 9A and 10A form a contiguous surface “patch” that is over 20 Å away from the Rev NES binding site ( Figure 6C , bottom panels and Figure S3 , note; no density was observed for the loop including residue 402 in the hCRM1 crystal structure ) and clearly does not interact with the Rev NES . Indeed , the amino acids that interact with Ran or the Rev NES [36] , [37] are invarient between human and mouse . Single mouse-to-human amino acid substitutions in mCRM1 , at each of the differing seven residues , were not sufficient to stimulate VLP production ( Figure 6B , lanes 12 through 18 ) . By contrast , a triple substitution ( T411P/V412M/S414F ) corresponding to the human configuration of amino acids in HEAT repeat helix 9A , consistently exerted a stimulatory effect ( ∼2-fold compared to mCRM1 ) on VLP production ( Figure 6B , lane 10 ) .
Here , we provide evidence that the human CRM1 protein contains a species-specific element required for efficient nucleocytoplasmic transport of Rev-dependent HIV-1 intron-containing RNAs and infectious HIV-1 production in murine cells ( Figures 1-5 ) . CRM1 is the major nuclear export receptor for cellular proteins , and maintains the nucleocytoplasmic partitioning of a broad array of cellular factors that regulate cell signaling and gene expression . These critical functions are emphasized by CRM1's high level of sequence conservation; for example , 98% amino acid identity is shared between human and mouse and 96% between human and fish ( Danio rerio ) . Our results therefore present a striking example of how the evolution of subtle changes within an essential host protein , with no evidence of disturbing general cellular function , can have profound implications for the replication of an important human pathogen . In keeping with our observations , the Shida lab previously demonstrated a synergistic effect for hCRM1 expression combined with human CycT1 expression in increasing HIV-1 production from rat macrophages [25] , and recently reported a defect in rat CRM1 that specifically impacted HIV-1 assembly with effects that were largely independent of changes to cytoplasmic gRNA abundance , Gag levels or Gag trafficking to the plasma membrane [24] . By contrast , our work identifies ineffective Rev-mediated RNA nuclear export as the principal manifestation of murine CRM1 activity ( Figure 4B ) and we demonstrate that hCRM1 expression triggers a significant increase to cytoplasmic gRNA levels and intracellular Gag concentration in murine cells ( e . g . , Figures 4 and 5 ) . These increases likely underlie the observed stimulation of MA-dependent transport of Gag molecules to the plasma membrane ( Figure 5 ) , and are consistent with a model wherein cooperative , concentration-dependent Gag-Gag interactions regulate the efficiency of virus particle assembly [32] , [34] . At a fundamental level , our data support the earlier Trono and Baltimore assertion that Rev-dependent nuclear export is deficient in mouse cells [17] , and we conclude that the species-specific factor responsible for this defect is CRM1 . The ability of hCRM1 to stimulate HIV-1 production requires a species-specific configuration of amino acids on the convex surface of CRM1 within HEAT repeat helices 9A and 10A ( Figure 6 ) . Activity can be transferred from hCRM1 to mCRM1 by swapping the central domain , indicating that , in the context of either species' CRM1 , this region is both necessary and sufficient for stimulating virus particle production in murine cells ( Figure 6B , compare lanes 6 and 7 ) . Remarkably , amino acids within HEAT repeats 9A and 10A are almost entirely conserved in all sequenced placental animals , with two notable sets of changes in the primate and rodent lineages ( Figures 6C and 7B ) . Regarding hCRM1 , we demonstrate that the insertion of the primate configuration of proline-411 , methionine-412 , phenylalanine-414 to the mouse protein ( Figure 6B ) , as well as the removal of methionine-412 and phenylalanine-414 from the human central domain in the M-Ancestral-M chimera ( Figure 7C ) both impact on CRM1 activity , highlighting the biological significance of this surface-exposed element . How might a CRM1 element impact Rev-dependent nucleocytoplasmic RNA transport , considering that the hydrophobic cleft that engages the Rev NES , located within HEAT repeats 11 and 12 ( amino acids 514 to 575 ) , is wholly conserved between hCRM1 and mCRM1 ( Figure 6A ) , and throughout the animal kingdom ? The implicated species-specific domain comprising HEAT repeat helices 9A and 10A is positioned more than 20 Å from the NES binding site ( Figure S3 ) . While significant , this distance might not exclude the formation of a secondary interface between CRM1 and one or more additional elements associated with Rev or the viral gRNA ribonucleoprotein complex . Indeed , CRM1 was reported to interact more strongly with Rev compared to a Rev NES peptide [45] . Moreover , protein footprinting analysis implied a secondary Rev/CRM1 interface , although these Rev-protected residues localize to CRM1 HEAT repeats 15 and 16 and not to HEAT repeat 9A/10A [46] . Importantly , several of the residues in HEAT repeats 9A and 10A that contrast between human and mouse CRM1 , including proline-411 , phenylalanine-414 , arginine-474 and histidine-481 , were implicated in CRM1's ability to recruit RanBP3 [47] , a factor affecting CRM1's interaction with RanGTP and ability to bind to specific substrates [48]–[50] . Despite this finding , wild-type versions of human and rat CRM1 exhibit a similar capacity to engage RanBP3 [47] so the relevance of this particular interaction remains to be determined . Taken together , it will be important to further characterise how differences between the human , mouse and ancestral sequences of CRM1 influence its interaction with the Rev hexamer on the RRE as well as with other nuclear export co-factors . Positive selection and neutral selection are two possible scenarios for how HEAT repeats 9A and 10A may have evolved more drastically in the primate and murid lineages . While extensive phylogenetic and computational analyses of selective pressure in CRM1 failed to prove positive selection , we find it remarkable that these “bursts” of diversification within CRM1 HEAT repeats 9A and 10A were maintained over the subsequent 80 million years ( Figures 6 and 7 ) . Considering that this region of CRM1 clearly exhibits important biological relevance , at least to HIV-1 , we suggest that positive , pathogen-driven selection may well underlie the emergence of these key CRM1 residues . We can only speculate as to the source of such selective pressure , but emphasize that modulation of nuclear membrane transport is critical for retroviral replication . For example , all lentiviruses such as HIV-1 , as well as deltaretroviruses like HTLV , encode Rev-like proteins that use CRM1 to regulate the export of intron-containing RNA from the nucleus . Notably , neither lentiviruses nor deltaretroviruses are associated with natural infection of rodents . Despite this apparent exclusion , a Rev equivalent , Rem , was recently identified for the betaretrovirus mouse mammary tumor virus [51] , [52] , indicating that CRM1 may indeed be co-opted by rodent retroviruses . In sum , we hypothesize that CRM1 was subject to a strong selection event in the primate lineage ∼80 million years ago that altered the sequence of HEAT repeat 9A . While we do not know the pathogen ( or other selective pressure ) that caused this , we have shown that the resulting CRM1 sequence is better able to support HIV-1 Rev's function as a mediator of viral RNA nuclear export . This may therefore serve as an example of the complexity of the pathogen-host “arms race” , wherein protein evolution in response to one pathogen has , over time , provided a useful foothold for the efficient replication of another .
Cells were cultured in Dulbecco's modified Eagle medium supplemented with 10% fetal bovine serum plus L-glutamine and penicillin/streptomycin . The pGP-RRE- , pGP-4×CTE-based vectors , pBC12/IL-2 , pcRev , pBC12/mCycT1-Y261C-3HA and pluciferase have been described [18] , [19] , [27] , [53] . The 4xCTE was a kind gift of Hans Georg Kräusslich [54] . Murine and human CRM1 cDNAs were obtained from Open Biosystems ( Thermo Scientific ) and cloned into pcDNA3 . 1 ( Invitrogen ) . An amino-terminal triple-myc encoding epitope tag was added to CRM1 , luciferase and GFP expressing vectors using a modified triple-myc pcDNA3 . 1 ( Invitrogen ) -based plasmid . myc epitope tagged mouse-human chimeric cDNAs and mutants thereof were generated by overlapping PCR and also cloned into the triple-myc vector . The Rev-minus HIV-1NL4-3 provirus was generated by replacing the EcoRI-NheI fragment of pNL4-3 with that of pNL4-3Rev-/4xMS2 [55] , generating pNL4-3/Rev- . To generate the 3T3 . GFP-mCRM1 and 3T3 . GFP-hCRM1 cell lines , the GFP reading frame was fused to the mCRM1 and hCRM1 reading frames using overlapping PCR and these DNAs were subsequently subcloned into a retroviral vector [56] for expression from transcripts also carrying an internal ribosomal entry site ( IRES ) and encoding neomycin-resistance . Cell lines were plated at ∼30% confluency in 6 well dishes prior to transfection using FuGene 6 reagent ( Roche ) following the manufacturer's instructions and medium was replaced at 24 h post-transfection . Levels of p24Gag in viral supernatants were measured by enzyme-linked immunosorbent assay ( ELISA ) ( Perkin Elmer ) . Viral infectivity was gauged by adding filtered supernatants in the presence of 5 µg/ml polybrene to TZM-bl indicator cells [57] at ∼50% confluency and measuring the induced expression of ß-galactosidase at 24 h using the Galacto-Star system ( Applied Biosystems ) . Immunoblot analyses were carried out as previously described [18] . Gag was detected using mouse anti-p24Gag antiserum 24-2 ( diluted 1∶1 , 000 ) [58] , myc-tagged species using mouse anti-myc antiserum ( 9E10 ) [59] , CRM1 using rabbit anti-CRM1 ab24189 antiserum ( Abcam ) and HSP90 using rabbit anti-HSP90 antiserum ( Santa Cruz Biotechnologies ) followed by anti-mouse or anti-rabbit secondary antibodies conjugated to infrared fluorophore IRDye800 ( Li-Cor Biosciences ) for quantitative immunoblotting . Anti-mouse secondary antibodies conjugated to horse radish peroxidase ( Pierce ) were used for detection of the myc-tagged CRM1 species . For Figures 5B and S2 , the protease inhibitor saquinavir ( NIH AIDS Research and Reference Reagent Program ) was added at 1 µM at 24 h post-transfection . Rates of translation were analyzed using [35S]methionine-cysteine metabolic labeling as previously described [18] . RNA isolation and northern blot analyses were as described [26] with minor modifications . For nuclear/cytoplasmic separation , 3T3 cells were lysed in 400 µl of cold , low salt NB buffer ( 50 mM Tris-HCL pH 8 . 0 , 20 mM NaCl , 1 . 5 mM MgCl2 , 0 . 5% NP-40 ) at ∼40 h post-transfection , held on ice for 5 min and then centrifuged at 500 × g to pellet nuclei . 200 µl of the cytoplasmic fraction was added to 600 µl RLT buffer ( Qiagen ) and vortexed vigorously . The nuclear pellet was washed twice in cold , low salt NB buffer , lysed in RLT buffer and spun through a Qiashredder column ( Qiagen ) . The 32P-labelled random primed probes for northern analyses were generated using HIV-1NL4-3 nucleotides 8465-8892 or a β-actin PCR fragment [60] . 3T3 cells were plated on glass coverslips , transfected and processed as described [18] . myc-tagged proteins were detected using anti-myc antiserum ( 9E10 ) [59] and Gag using mouse monoclonal anti-p24Gag antiserum ( 24-2; diluted 1∶1 , 000 in NGB ) [58] and rabbit polyclonal anti-p17Gag serum ( UP595; diluted 1∶500 in NGB ) [19] , respectively , followed by goat anti-mouse-AlexFluo546 and goat anti-rabbit-AlexaFluo488 fluorescent secondary antibodies ( Invitrogen ) . Cell nuclei were visualized by staining with 4′ , 6-diamidino-2-phenylindole ( DAPI ) . Cells were visualized using laser scanning confocal imaging on a DM IRE2 microscope ( Leica ) . Images were processed using LCS ( Leica ) and Openlab ( Improvision ) software packages . CRM1 sequences were retrieved from NCBI and Ensembl . The phylogeny of 17 full-length ( 3213 nt ) mammalian CRM1 sequences was reconstructed by maximum likelihood ( ML ) inference , under the general time reversible model of nucleotide substitutions , using the program FastTree 1 . 0 [61] . The species included in the analysis were human ( Homo sapiens ) , chimpanzee ( Pan troglodytes ) , gorilla ( Gorilla gorilla ) , gibbon ( Nomascus leucogenys ) , macaque ( Macaca mulatta ) , marmoset ( Callithrix jacchus ) , mouse ( Mus musculus ) , rat ( Rattus norvegicus ) , rabbit ( Oryctolagus cuniculus ) , dog ( Canis lupus familiaris ) , panda ( Ailuropoda melanoleuca ) , horse ( Equus caballus ) , microbat ( Myotis lucifugus ) , cow ( Bos Taurus ) , pig ( Sus scrofa ) , elephant ( Loxodonta Africana ) , opossum ( Monodelphis domestica ) . Local support values of the phylogenetic branches were calculated on the basis of 1000 replicates . Trees were edited using the program FigTree v1 . 3 . 1 ( http://tree . bio . ed . ac . uk/software/figtree ) . The ancestral reconstruction of amino acid alteration along the CRM1 phylogeny was performed by maximum likelihood inference under the Whelan and Goldman empirical model , as implemented in the program codeML from the package PaML v3 . 14a [62] . The reconstructed ML phylogeny was fixed for all subsequent selection analyses . For sliding window analysis we used KaKs Calculator v2 [63] , using the modified LPB option , a window of 30 bp and step size of 3 bp . The chicken ( Gallus gallus ) and zebra fish ( Dani rerio ) sequences were obtained from Ensembl . In addition , we analysed several CRM1 sequences from Enembl or NCBI that are from low-coverage or preliminary assemblies/annotation and therefore did not have full length CRM1 sequences . These included the bushbaby ( Otolemur garnetti ) , tarsier ( Tarsius syrichta ) , baboon ( Papio hamadryas ) , orangutan ( Pongo abelii ) , pika ( Ochotona princeps ) , squirrel ( Spermophilus tridecemlineatus ) , kangaroo rat ( Dipodomys ordii ) , armadillo ( Dasypus novemcinctus ) and platypus ( Ornithorhynchus anatinus ) . To reconstruct the tarsier CRM1 sequence , we used the NCBI trace archive database to nearly complete the partial sequence obtained from Ensembl . This allowed a sequence that had only two amino acid gaps ( position 112 and 117 ) to be assembled . As this sequence is identical to several Laurasiatheria CRM1 sequences ( horse , cow , dog , panda ) and has only 1 difference compared to the rabbit ( at position 396 ) , which is not conserved in other Rodentia or primate sequences , this is considered the ancestral Euarchontoglires sequence . The mouse lemur ( Microcebus murinus ) CRM1 sequence for HEAT repeat 9A was obtained from the NCBI trace archive database . Several approaches were used to investigate the role of selection on the CRM1 gene during mammal evolution . First , evidence for codon-specific positive selection was sought using three different maximum likelihood tests implemented in the HyPhy package [64]: SLAC ( Single Likelihood Ancestor Counting ) , FEL ( Fixed Effect Likelihood ) and REL ( Random Effects Likelihood ) . Analyses were conducted under the Hasegawa-Kishino-Yano ( HKY85 ) model of nucleotide substitution , and the MG94xHKY85 model of codon evolution . The M7 model ( neutral model ) and the M8 model ( positive selection model ) implemented in the program codeML were also fitted to the sequence alignment . Model M7 assumes a beta distribution for dN/dS over sites limited to the interval ( 0 , 1 ) , providing a null hypothesis for testing positive selection . Model M8 adds an extra class of dN/dS over sites to M7 , allowing dN/dS >1 . A likelihood ratio test was used to test whether allowing individual sites to evolve under positive selection ( i . e , M8 ) provided a significantly better fit to the data than the neutral model ( i . e . , M7 ) . In the latter analysis , codon frequencies were calculated under the F3x4 model . Second , the branch-site test implemented in codeML was used to identify codons subjected to positive selection along specific branches of the phylogeny ( ‘foreground’ branches ) [65] . The two foreground branches tested were the one supporting the higher primate lineage ( red branch in Figure 7A ) and the one supporting the rodent lineage ( blue branch in Figure 7A ) . These two branches were selected on the basis of the excess of amino acid changes they exhibit compared to other internal branches . Two models were compared: ( i ) model A , in which the foreground branches may have different proportions of sites under neutral selection than the rest of the phylogeny ( i . e . relaxed purifying selection ) , and ( ii ) model B , in which the foreground branches may also have a proportion of sites under positive selection . A likelihood ratio test was performed to estimate whether model B gave a significantly better fit to the data . Each test was performed on the full-length CRM1 alignment ( 3213 nt ) , the HEAT repeat helix 9A region only ( codon positions 402 to 423; 66 nt ) and the HEAT repeat 10A region only ( codon positions 469 to 481; 51 nt ) .
|
HIV-1 requires multiple cellular co-factors to replicate , and non-human cells often carry species-specific variations in the genes encoding these co-factors that can prevent efficient replication . Here , the basis for murine cell-specific deficiencies in the late steps of HIV-1 replication is addressed . We show that differences between the mouse and human forms of the essential host protein CRM1 , a protein required for the transport of macromolecules from the nucleus to the cytoplasm , underlie this problem . More precisely , murine CRM1 , unlike its human counterpart , fails to fully support the function of the HIV-1 Rev protein , a factor necessary to transport viral RNAs to the cytoplasm . Key amino acid differences between the mouse/human CRM1 proteins are identified and computational analyses of divergent animal CRM1 proteins reveal a unique motif in higher primates likely acquired in response to ancient evolutionary pressures . This CRM1 element may represent a novel pathogen interaction site that evolved to evade prior infections , but is now contributing to the susceptibility of humans to HIV-1 .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunodeficiency",
"viruses",
"virology",
"biology",
"microbiology",
"host-pathogen",
"interaction"
] |
2011
|
Evolution of a Species-Specific Determinant within Human CRM1 that Regulates the Post-transcriptional Phases of HIV-1 Replication
|
Whether epithelial-mesenchymal transition ( EMT ) is always linked to increased tumorigenicity is controversial . Through microRNA ( miRNA ) expression profiling of mammary epithelial cells overexpressing Twist , Snail or ZEB1 , we identified miR-100 as a novel EMT inducer . Surprisingly , miR-100 inhibits the tumorigenicity , motility and invasiveness of mammary tumor cells , and is commonly downregulated in human breast cancer due to hypermethylation of its host gene MIR100HG . The EMT-inducing and tumor-suppressing effects of miR-100 are mediated by distinct targets . While miR-100 downregulates E-cadherin by targeting SMARCA5 , a regulator of CDH1 promoter methylation , this miRNA suppresses tumorigenesis , cell movement and invasion in vitro and in vivo through direct targeting of HOXA1 , a gene that is both oncogenic and pro-invasive , leading to repression of multiple HOXA1 downstream targets involved in oncogenesis and invasiveness . These findings provide a proof-of-principle that EMT and tumorigenicity are not always associated and that certain EMT inducers can inhibit tumorigenesis , migration and invasion .
Epithelial-mesenchymal transition ( EMT ) is regulated by transcription factors [1] , [2] , extracellular ligands [3] and microRNAs ( miRNAs ) [4]–[9] . It has been proposed that inducing EMT in epithelial tumor cells enhances migration , invasion and dissemination , whereas the mesenchymal-epithelial transition ( MET ) process facilitates metastatic colonization [1] , [2] , [10]–[12] . In addition , induction of EMT in differentiated tumor cells has been shown to generate cells with properties of tumor-initiating cells , or cancer stem cells [13] , [14] . However , whether EMT and tumorigenicity are always linked is debated . Recently , analysis of clonal populations derived from the PC-3 prostate cancer cell line demonstrated that a metastatic clone was highly proliferative and expressed genes associated with an epithelial phenotype , whereas a non-metastatic clone was poorly proliferative and expressed genes associated with EMT [15] . Whether this finding is attributed to clonal bias or holds true in general is unknown . Moreover , whether there exist specific gene products that concurrently induce EMT and inhibit tumorigenesis remains elusive .
To systematically identify miRNAs differentially expressed in EMT , we overexpressed EMT-inducing transcription factors , Twist , Snail or ZEB1 , in the experimentally immortalized , non-transformed human mammary epithelial cells [16] , termed HMLE cells . Each of these transcription factors was capable of inducing EMT , as evidenced by changes in morphology ( Figure S1A ) , downregulation of E-cadherin ( CDH1 ) , and upregulation of N-cadherin ( CDH2 ) , vimentin ( VIM ) and multiple EMT-inducing transcription factors ( Figure S1B ) . Next , we performed miRNA microarray profiling analysis ( Table S1 ) of these HMLE cells that had been induced to undergo EMT and identified a set of 13 EMT-associated miRNAs ( Figure 1A and S2A; Table S2 ) . Using TaqMan qPCR assays , we confirmed that four miRNAs , miR-100 , miR-125b , miR-22 and miR-720 , were commonly upregulated miRNAs in EMT; five miRNAs , miR-200c , miR-141 , miR-205 , miR-663 and miR-638 , were commonly downregulated miRNAs in EMT ( Figure 1B and Table S2 ) . The most dramatically deregulated miRNAs were miR-205 and two clustered miR-200 family members – miR-200c and miR-141 ( Table S2 ) , which are the first EMT-regulating miRNAs discovered through other approaches [4] , [5] . This result validated the efficacy of our experimental system . Differentially expressed miRNAs could be either causes or consequences of EMT . We cloned the four upregulated miRNAs into puromycin resistance cassette-containing retroviral vectors ( MSCV-PIG and pBabe-puro ) and expressed them individually in HMLE cells . While miR-125b and miR-720 did not cause any changes in cell morphology or EMT markers ( Figure 1C and data not shown ) , expression of either miR-100 or miR-22 ( Figure S2B ) was sufficient to induce EMT: upon expression of either miRNA , epithelial cells became scattered and assumed fibroblastic morphology ( Figure 1C ) ; E-cadherin expression was undetectable and the mesenchymal marker vimentin was dramatically induced ( Figure 1D ) . Similarly , expression of miR-100 in the MCF7 human epithelial breast cancer cell line ( Figure S2C ) also markedly downregulated E-cadherin and upregulated vimentin ( Figure 1D ) , although we did not observe a clear morphological change . We examined miR-100 expression levels in a series of human breast cancer cell lines . Relative to HMLE cells , epithelial-like tumor cell lines exhibited either comparable or much lower miR-100 expression , whereas mesenchymal-like tumor cell lines showed higher levels of miR-100 ( Figure 1E ) . The association between miR-100 and EMT markers was further validated in human tumors: from the Cancer Genome Atlas ( TCGA ) breast cancer data [17] , we observed a moderate but significant inverse correlation between miR-100 and E-cadherin expression levels ( Rs = −0 . 1 , P = 0 . 006 , Figure 1F ) and a highly significant positive correlation between miR-100 and vimentin expression levels ( Rs = 0 . 43 , P<2×10−16 , Figure 1G ) . We performed TCGA data analysis to determine the expression levels of miR-100 and miR-22 in human breast cancer . Surprisingly , miR-100 was found to be downregulated in all subtypes of human breast tumors , including luminal A ( P = 1×10−11 ) , luminal B ( P = 0 . 008 ) , basal-like ( P = 0 . 006 ) and HER2 ( P = 0 . 001 ) subtypes , compared with paired normal breast tissues ( Figure 2A ) . Consistent with the correlation of miR-100 with EMT markers ( Figure 1F and 1G ) , the luminal A subtype of primary breast tumors ( which are known to be E-cadherin-positive and vimentin-negative ) exhibited the most significant downregulation of miR-100 ( Figure 2A ) . In contrast , miR-22 expression showed no significant difference between cancer and paired normal tissues ( Figure S3 ) . To determine the cellular origin of miR-100 expression , we performed in situ hybridization on human normal and cancer tissues , and found that miR-100 was indeed highly expressed in normal human mammary epithelium as opposed to barely detectable expression in the stroma , whereas human breast tumors exhibited reduced miR-100 expression ( Figure 2B and 2C ) . Therefore , downregulation of miR-100 reflects the difference between normal mammary epithelium and breast tumor cells , but is not due to the difference in the stroma . This observed downregulation of miR-100 in human breast tumors prompted us to determine whether it could be a tumor suppressor . Indeed , expression of miR-100 significantly inhibited the proliferation of HMLE cells in vitro , either in the presence or absence of ectopic expression of the Erbb2 mammary oncogene ( Figure 2D and S4A ) . To validate this effect in vivo , we subcutaneously implanted Erbb2-expressing HMLE cells ( HMLE-Erbb2 ) with or without miR-100 overexpression into nude mice . Strikingly , miR-100 expression dramatically suppressed tumor formation and growth ( Figure 2E–2G ) , as it not only delayed initial tumor onset by one week ( Figure 2E ) , but also caused a 83% reduction in tumor volume ( 683 . 3 mm3 vs . 117 . 2 mm3 , Figure 2E ) and a 84% reduction in tumor weight ( 0 . 62 g vs . 0 . 098 g , Figure 2F and 2G ) at the late stage . Western blot analysis of E-cadherin and vimentin in tumor lysates ( Figure 2H ) and E-cadherin immunohistochemical staining of the tumors ( Figure S4B ) confirmed that the EMT status was retained in tumors formed by miR-100-expressing HMLE-Erbb2 cells . Furthermore , a 91% decrease in tumor weight was observed in mice implanted with miR-100-overexpressing MCF7 human breast cancer cells , compared with hosts of mock-infected MCF7 cells ( Figure 2I and 2J ) . We hypothesized that different target genes of miR-100 mediate the two distinct functions of this miRNA . Four miR-100 targets , SMARCA5 , SMARCD1 , MTOR ( mammalian target of rapamycin ) and BMPR2 , have been identified by reporter assays previously [18] , [19] . In addition , among all predicted targets of miR-100 , HOXA1 is a mammary oncogene [20] and is upregulated in human breast cancer [21]; overexpression of HOXA1 in immortalized human mammary epithelial cells was sufficient to induce aggressive tumor formation in vivo [20] . While miR-100 did not substantially alter expression levels of SMARCD1 , mTOR and BMPR2 in HMLE cells ( Figure S5A ) , overexpression of this miRNA in both HMLE and MCF7 cells resulted in a pronounced decrease in SMARCA5 and HOXA1 protein levels ( Figure 3A ) . Moreover , the activity of a luciferase reporter fused to a wild-type HOXA1 3′ UTR , but not that of a reporter fused to a mutant HOXA1 3′ UTR with mutations in the miR-100 binding site ( Figure S5B ) , was reduced by 80% upon expression of miR-100 ( Figure 3B ) , which validated HOXA1 as a direct target of this miRNA . We silenced SMARCA5 in HMLE cells . This markedly reduced E-cadherin protein expression ( Figure 3C ) but did not alter cell proliferation ( Figure S5C ) , suggesting that downregulation of SMARCA5 partially mediates the EMT-inducing effect of miR-100 but not its growth-inhibitory function . Conversely , re-expression of SMARCA5 in miR-100-overexpressing HMLE cells restored the expression of E-cadherin at both mRNA and protein levels ( Figure 3D and 3E ) , although the mesenchymal morphology was not reversed . SMARCA5 ( also named hSNF2H ) is a chromatin-remodeling protein that physically interacts with the DNA methyltransferase DNMT3B [22] . Although it is not clear how this interaction modulates DNMT3B activity , we speculated that miR-100 might promote CDH1 ( encoding E-cadherin ) gene methylation by targeting SMARCA5 . Indeed , bisulfite sequencing assays of the 27 CpG sites in the CDH1 promoter region revealed 29 . 6% methylation in the control HMLE cells and 55 . 1% methylation in miR-100-overexpressing HMLE cells , while re-expression of SMARCA5 reversed the effect of miR-100 on CDH1 promoter methylation ( Figure 3F ) . In contrast to the effect of SMARCA5 , restoring HOXA1 expression in miR-100-overexpressing HMLE-Erbb2 cells to the same level as the control HMLE-Erbb2 cells ( Figure 4A ) did not affect expression levels of EMT-associated markers ( Figure S5D ) , but instead fully rescued tumor onset and partially rescued tumor volume ( 51% rescue , Figure 4B ) and tumor weight ( 40% rescue , Figure 4C and 4D ) . Consistent with the in vitro effect of miR-100 on EMT induction ( Figure 1C and 1D ) and cell proliferation ( Figure S4A ) , the control HMLE-Erbb2 tumors were epithelial and had 80% Ki-67-positive cells , miR-100-expressing HMLE-Erbb2 tumors exhibited mesenchymal morphology and 8% Ki-67-positive cells , whereas HMLE-Erbb2 tumors with co-expression of miR-100 and HOXA1 were mesenchymal but showed 63% Ki-67-positive cells ( Figure 4E ) . Taken together , downregulation of HOXA1 mediates , at least in part , the tumor-suppressing effect of miR-100 but not its EMT-inducing function . Unexpectedly , despite strong EMT induction in both HMLE-Erbb2 and MCF7 cells , expression of miR-100 suppressed their migration and invasion in vitro , as gauged by Transwell assays ( Figure 5A and 5B; Figure S6A and S6B ) . To further confirm the inhibitory effect of miR-100 on cell motility , we tracked the movement of individual cells cultured on top of collagen over a 24-hour period . Using time-lapse video microscopy , we observed a 53% decrease in the speed of movement of miR-100-expressing HMLE-Erbb2 cells compared with HMLE-Erbb2 cells ( Figure 5C; Video S1 and S2 ) . It should be noted that in order to permit the space for cell movement , the condition used for this experiment was low density and did not allow the majority of HMLE-Erbb2 cells to form epithelial clusters; however , we did observe HMLE-Erbb2 cell clusters with epithelial island structure that exhibited a surprisingly rapid collective movement and long trajectories without cell dissociation ( Video S1 – note that an epithelial cell cluster initially appeared in the upper left corner and then moved to the lower part of the field ) , whereas all miR-100-expressing HMLE-Erbb2 cells had highly limited area of movement and reduced speed ( Video S2 ) . To our knowledge , this is the first time that conversion from an epithelial state to a mesenchymal state has been found to be accompanied by reduced motility and invasiveness , which indicates that miR-100 may concurrently target EMT-repressing genes ( SMARCA5 ) and pro-invasive genes . Indeed , HOXA1 has been identified as a driver of both oncogenesis and the invasion-metastasis cascade in human melanoma [23] . Consistent with this finding , restoration of HOXA1 in miR-100-overexpressing HMLE-Erbb2 cells ( Figure 4A ) rescued cell migration and invasion ( Figure 5A and 5C; Figure S6A; Video S3 ) . In contrast , neither re-expression of SMARCA5 in miR-100-overexpressing HMLE cells nor knockdown of SMARCA5 in HMLE cells affected cell motility ( Figure S6C and S6D ) . To determine the loss-of-function effect , we used a miR-Zip method to achieve lentiviral inhibition of miR-100 in MDA-MB-231 breast cancer cells . Compared with cells infected with a scrambled hairpin control ( Zip-scr ) , cells with approximately 60% knockdown of miR-100 ( Zip-100 , Figure 5D ) displayed a significant increase in their migratory and invasive capacity ( Figure 5E ) , while their mesenchymal status was not altered ( data not shown ) . We further validated the effect on tumor invasion in vivo: tumors formed by miR-100-overexpressing HMLE-Erbb2 cells were well demarcated and did not show overt invasion to their surrounding tissues ( Figure 5F ) ; in contrast , tumors formed by either the control HMLE-Erbb2 cells ( mock ) or HMLE-Erbb2 cells with simultaneous expression of miR-100 and HOXA1 were invasive and infiltrated muscular , adipose and stromal tissues ( Figure 5F ) . We conclude from these experiments that miR-100 suppresses migration and invasion , at least in part , through direct targeting of HOXA1 but not SMARCA5 . HOXA1 is required for the development of the hindbrain , inner ear and neural crest in mammals [24]–[26] . Genome-wide expression profiling analysis of Hoxa1-null mouse embryos identified a number of Hoxa1 downstream targets involved in developmental processes [26]; three of the genes downregulated in Hoxa1 null embryos , Met , Smo ( smoothened ) and Sema3c ( semaphorin 3c ) , are positive regulators of tumor cell migration , invasion and/or growth . MET , the receptor for hepatocyte growth factor , has been identified as a driver of tumorigenesis , motility and metastasis [27] . SMO is a central mediator of Hedgehog signaling , whereby Hedgehog binds to the twelve-pass transmembrane protein patched , alleviating patched-mediated inhibition of SMO [28] . It has been shown that the SMO inhibitor cyclopamine can lead to regression of medulloblastoma deficient in patched [29] . SEMA3C is a secreted protein that can induce migratory and invasive properties of breast cancer and prostate cancer cells [30] , [31] . In addition , ectopic expression of HOXA1 in MCF7 breast cancer cells upregulated cyclin D1 [20] , a cyclin that is required for steroid-induced proliferation of mammary epithelium during pregnancy [32] and promotes the development of mammary adenocarcinomas when overexpressed [33] . In the present study , ectopic expression of miR-100 markedly reduced the mRNA levels of MET , SMO , SEMA3C and CCND1 , either in the presence or absence of Erbb2 expression ( Figure 6A and 6B ) , while restoration of HOXA1 rescued the expression of each of these four genes ( Figure 6B ) . A similar effect was observed on cyclin D1 protein expression levels ( Figure 6C ) . Therefore , miR-100 downregulates multiple HOXA1 downstream targets involved in oncogenesis and invasiveness . We sought to understand how miR-100 expression is regulated . Examination of the 2 . 5 kb genomic sequence upstream of the human mir-100 stem-loop identified two putative ZEB1-binding sites at −400 bp ( Z-box , CAGGTA ) and −2 . 2 kb ( E-box , CAGCTG ) , respectively ( Figure S7A ) . We designed PCR amplicons to assay for the presence of these putative binding sites in chromatin immunoprecipitates . This experiment revealed that ZEB1 bound to the E-box but not to the Z-box ( Figure 7A and 7B ) . Moreover , luciferase assays demonstrated that ZEB1 significantly increased the activity of the putative mir-100 promoter ( Figure 7C ) , suggesting that mir-100 is likely to be a transcriptional target of ZEB1 . Interestingly , overexpression of either Twist or Snail increased ZEB1 expression to the level as high as that of ZEB1-overexpressing cells ( Figure S1B ) , which could explain why miR-100 was identified as a commonly upregulated miRNA in HMLE cells overexpressing Twist , Snail or ZEB1 . Consistently , miR-100 exhibited a strong positive correlation with Twist ( Rs = 0 . 3 , P = 5×10−19 ) , Snail ( Rs = 0 . 2 , P = 4×10−7 ) and ZEB1 ( Rs = 0 . 5 , P<2×10−16 ) expression levels in human breast tumors ( Figure S7B–S7D ) Upregulation of ZEB1 has been observed in triple-negative and basal-like breast tumors [34] , [35] . Paradoxically , miR-100 is commonly downregulated in all subtypes of human breast cancers ( Figure 2A ) , which indicates that other mechanisms lead to downregulation of miR-100 . The mir-100 gene is embedded in a non-coding host gene , MIR100HG . Analysis of TCGA data revealed that 1 . 2% of the breast tumors ( 11 out of a total of 913 samples with copy number data available ) had homozygous deletion of both mir-100 and MIR100HG , which could explain loss of miR-100 in these samples . Besides genetic alterations , a second common cause of loss of a tumor suppressor is DNA hypermethylation . From TCGA data , the majority of breast tumors ( consisting of luminal A , luminal B , basal-like and HER2 subtypes ) had much higher levels of MIR100HG gene methylation compared with paired normal mammary tissues ( P = 2×10−12 , n = 90 , Figure 7D ) . Moreover , we observed a significant inverse correlation between MIR100HG gene methylation and miR-100 expression levels in breast cancer patients ( Rs = −0 . 3 , P = 7×10−17 , n = 522 , Figure 7E ) . Consistently , treatment of MCF7 and SUM149 human breast cancer cell lines with the DNA demethylating agent 5-azacytidine led to significant upregulation of miR-100 expression ( Figure 7F and 7G ) . Taken together , these data suggest that miR-100 expression is regulated by both transcriptional activation and epigenetic silencing .
In summary , we identified miR-100 as a novel EMT inducer and a tumor suppressor , and validated in human tumors that miR-100 is downregulated in clinical breast cancer and correlates with EMT-associated markers . Notably , our results indicate the following: on one hand , both DNA hypermethylation and genetic deletion could contribute to miR-100 downregulation or loss in all subtypes of human breast tumors independently of EMT . On the other hand , induction of miR-100 may serve as a negative feedback mechanism to counteract the tumor-promoting and pro-invasive effect of EMT-inducing transcription factors . However , these transcription factors also regulate many other genes involved in cancer stemness , invasion and metastasis; for example , ZEB1 represses miR-200 [7] and Twist transactivates miR-10b [36] . This appears to be similar to other pleiotropically acting transcription factors: for instance , MYC is a cancer-driving oncoprotein and it is known to transcriptionally activate both pro-survival and pro-apoptotic genes [37] . Because induction of the EMT program can generate stem-like cells [13] , [14] , we examined the ability of miR-100 to regulate stem cell properties , as gauged by the stem cell marker ALDH1 ( aldehyde dehydrogenase 1 ) [38] and mammosphere-forming ability [39] . Indeed , we observed induction of both ALDH1 expression ( Figure S8A ) and mammosphere formation ( Figure S8B and S8C ) by miR-100 in HMLE and HMLE-Erbb2 cells . Thus , the anti-tumor function of miR-100 is not due to depletion of the stem-like cell population , but instead results from inhibition of cell proliferation . In support of this notion , miR-100-expressing HMLE-Erbb2 tumors displayed a 90% reduction in the percentage of Ki-67-positive cells compared with the control HMLE-Erbb2 tumors ( Figure 4E ) . Our work is consistent with the anti-proliferative function of miR-100 described in several recent studies [18] , [40] , and is the first report of an EMT inducer that suppresses cell movement and invasion . Mechanistically , miR-100 induces EMT by targeting SMARCA5 , an epigenetic regulator of E-cadherin , and inhibits tumorigenesis , migration and invasion by targeting HOXA1 , leading to downregulation of multiple HOXA1 downstream targets involved in oncogenesis and invasiveness , including CCND1 , MET , SMO and SEMA3C ( Figure 7H ) . It should be noted that miR-100 has been reported to target IGF2 in 4T1 mouse mammary tumor cells [40]; however , IGF2 expression is undetectable in the human mammary epithelial cells ( HMLE ) used in this study ( data not shown ) , although it is possible that IGF2 mediates the function of miR-100 in cells that express IGF2 . Another EMT-inducing miRNA identified in our study is miR-22 . Consistent with our results , a recent report also demonstrated that miR-22 is an EMT inducer [41] . However , in stark contrast to miR-100 , miR-22 functions to promote tumorigenesis , invasion and metastasis , ostensibly through direct targeting of the TET family of methylcytosine dioxygenases [41] . Although miR-22 expression showed no significant difference between breast tumors and paired normal mammary tissues based on TCGA data analysis ( Figure S3 ) , patients with high levels of miR-22 had worse survival rates than patients with low levels of miR-22 [41] . Taken together , these results do not argue that EMT itself suppresses cancer , but instead demonstrate that EMT is not always associated with increased tumorigenesis , migration and invasion , and that all EMT inducers are not equal: while some of them ( such as miR-22 ) can promote tumorigenicity , motility and invasiveness , others ( such as miR-100 ) inhibit these properties owing to their ability to target both EMT-repressing genes and oncogenic/pro-invasive genes ( Figure 7H ) . Our findings raise the caution that the validity of using EMT-associated gene products as cancer biomarkers should be carefully assessed .
The HMLE cell line was from R . A . Weinberg's lab stock and cultured in complete Mammary Epithelial Cell Growth Medium ( MEGM from Lonza ) . The MCF7 , T47D , MDA-MB-231 and 293T cell lines were purchased from American Type Culture Collection and were cultured under conditions specified by the manufacturer . The SUM149 , SUM159 and SUM229 cell lines were from S . Ethier and cultured as described ( http://www . asterand . com/Asterand/human_tissues/149PT . htm ) . For demethylating studies , the MCF7 and SUM149 cells were treated with 2 µM 5-azacytidine ( Sigma ) for 12 hours . The human mir-100 , mir-22 , mir-125b and mir-720 genomic sequences were PCR amplified from normal genomic DNA and cloned into the MSCV-PIG or pBabe-puro retroviral vector . A 1 . 5 kb putative human mir-100 promoter sequence containing the ZEB1-binding site ( E-box ) was PCR amplified from normal genomic DNA and cloned into the pGL3-Basic vector . A HOXA1 3′ UTR fragment was cloned into the pMIR-REPORT luciferase construct , using the following cloning primers: forward , 5′-ATCTTAGCTGGATATAATGTA-3′; reverse , 5′-TGCTTCATAAATTTCTTCATC-3′ . A rat oncogenic ( activated ) form of Erbb2/NeuNT was from W . Guo . The Twist , Snail and ZEB1 expression constructs were from R . A . Weinberg . The human HOXA1 ORF was from Open Biosystems through MD Anderson's shRNA and ORFeome Core ( PLOHS_100003514 ) . The human SMARCA5 shRNA was from Sigma ( TRCN0000013215 ) . The human SMARCA5 expression vector was from GeneCopoeia ( EX-E2767-Lv105 ) . The miR-Zip construct expressing a short hairpin inhibiting miR-100 was from System Biosciences . The HOXA1 3′ UTR mutant was generated using a QuikChange Site-Directed Mutagenesis Kit ( Stratagene ) . The vectors used in this study are listed in Table S3 . Total RNA , inclusive of small RNAs , was isolated using the mirVana miRNA Isolation Kit ( Ambion ) and was then reverse transcribed with an iScript cDNA Synthesis Kit ( Bio-Rad ) . The resulting cDNA was used for qPCR using the TaqMan Gene Expression Assays ( Applied Biosystems ) , and data were normalized to an endogenous control β-actin . Quantification of the mature form of miRNAs was performed using the TaqMan MicroRNA Assay Kit ( Applied Biosystems ) according to the manufacturer's instructions , and the U6 small nuclear RNA was used as an internal control . Real-time PCR and data collection were performed on a CFX96 instrument ( Bio-Rad ) . The production of lentivirus and amphotropic retrovirus and infection of target cells were performed as described previously [42] . Genes that contain the miR-100-binding site ( s ) in the 3′ UTR were obtained using the TargetScan program [43] ( www . targetscan . org; version 6 . 2 ) . The RNAhybrid program [44] was used to predict duplex formation between miR-100 and human HOXA1 3′ UTR . To determine growth curves , we plated equal numbers of cells in 6-cm dishes . Starting from the next day , cells were trypsinized and counted every day . Cell counts were obtained from a TC10 Automated Cell Counter ( Bio-Rad ) . Transwell migration and Matrigel invasion assays were performed as described previously [36] . Mammosphere assay was performed according to the vendor ( Stemcell Technologies ) 's protocol . Briefly , single cell suspensions were seeded in the 6-well ultra-low attachment plate ( Corning , 3471 ) at a density of 3 . 5–4 . 0×104 cells in 2 ml of freshly prepared Complete MammoCult Medium ( Stemcell Technologies , 05620 ) per well . After incubation for 7 days , the number of mammospheres that were larger than 40 µm in diameter was counted . Dual luciferase reporter assays were performed as described previously [36] . Western blot analysis was performed with precast gradient gels ( Bio-Rad ) using standard methods . Briefly , cells were lysed in the RIPA buffer containing protease inhibitors ( Roche ) and phosphatase inhibitors ( Sigma ) . Proteins were separated by SDS-PAGE and blotted onto a nitrocellulose membrane ( Bio-Rad ) . Membranes were probed with the specific primary antibodies , followed by peroxidase-conjugated secondary antibodies . The bands were visualized by chemiluminescence ( Denville Scientific ) . The following antibodies were used: antibodies to E-cadherin ( 1∶1000 , BD Transduction Laboratories , 610182 ) , vimentin ( 1∶2000 , NeoMarkers , MS-129-P ) , Erbb2 ( 1∶500 , Cell signaling Technology , 2242 ) , HOXA1 ( 1∶1000 , Santa Cruz Biotechnology , sc-17146 ) , SMARCA5 ( 1∶500 , sc-8760 from Santa Cruz Biotechnology and ab3749 from Abcam ) , SMARCD1 ( 1∶500 , Abcam , ab86029 ) , mTOR ( 1∶1000 , Cell signaling Technology , 2972 ) , BMPR2 ( 1∶1000 , Cell signaling Technology , 69679 ) , cyclin D1 ( 1∶1000 , Cell signaling Technology , 2922 ) , ALDH1A1 ( 1∶1000 , Santa Cruz Biotechnology , sc-22589 ) , HSP90 ( 1∶3000 , BD Transduction Laboratories , 610419 ) and cyclophilin B ( 1∶2000 , Thermo , PA1-027A ) . ChIP was performed with 293T cells transfected with SFB-tagged GFP or ZEB1 , by using a Chromatin Immunoprecipitation ( ChIP ) Assay Kit ( Millipore , 17–295 ) according to the manufacturer's instructions . After immunoprecipitation with FLAG antibody-conjugated beads ( Sigma , M8823 ) , protein-DNA crosslinks were reversed and DNA was purified to remove the chromatin proteins and used for PCR and qPCR . The PCR primers are: E-box , 5′-TACTAGGTCAGTATTTGATTT-3′ ( forward ) and 5′-GTTAGCGATAGACTAAGATCTAT-3′ ( reverse ) ; Z-box , 5′-ACCTATAAATCCGTTGGTAG-3′ ( forward ) and 5′-AATCTGGGCAAAGTGATACC-3′ ( reverse ) . The qPCR primers are 5′-ACTTTGGATTGTTTGGAGGTTAAC-3′ ( forward ) and 5′-AATTTGCATGGCGCTCTTG-3′ ( reverse ) . Genomic DNA was extracted using the DNeasy Kit ( Qiagen , 69504 ) . The MethylDetector kit ( Active Motif , 55001 ) was used to generate bisulfite-modified DNA . The modified DNA was purified and used as the template for nested PCR reactions with the following primers: outer primers , 5′-ATTCGAATTTAGTGGAATTAGAATC-3′ ( forward ) and 5′-AACCTACAACAACAACAACAACG-3′ ( reverse ) ; nested primers , 5′-TTAGTAATTTTAGGTTAGAGGGTTATCG-3′ ( forward ) and 5′-ACTCCAAAAACCCATAACTAACCG-3′ ( reverse ) . The second-round PCR products were subcloned into the TOPO cloning vector ( Invitrogen , K4600-01 ) and clones were randomly picked for DNA sequencing . The double ( 5′ and 3′ ) digoxigenin ( DIG ) -labeled miR-100 probe and U6 probe were purchased from Exiqon . The normal mammary tissue and breast tumor sections were purchased from Origene ( normal: CS807851; tumor: CS704488 and CS 711714 ) . The tissue microarray ( TMA ) slide was purchased from Biomax ( BR1006 ) . In situ hybridization was performed according to the protocol of the miRCURY LNA microRNA ISH Optimization Kit ( FFPE ) ( Exiqon ) . The stained slide was scanned on the Automated Cellular Image System III ( ACIS III , Dako , Denmark ) for quantification by digital image analysis . The color threshold was set up and standardized for all samples , and the color intensity was automatically scored for all individual cores on the TMA slide . The expression level was calculated from the score of color intensity and normalized to the internal control U6 . The 3 . 5-cm glass bottom multi-well plates ( MatTek Corporation ) were covered with 1 ml of 1 . 7 mg/ml collagen solution . After collagen solidified , we seeded 1×105 cells in serum-free and growth factor-free medium on top of the collagen . The cells were incubated overnight , and then were observed for 24 hours in a humidified , CO2-equilibrated chamber mounted on a Zeiss Axio Observer Z1 microscope . To quantitate the speed , we tracked the distance of individual cell movement by using Axio Vision software ( Zeiss ) in randomly selected fields . The speed of movement was calculated and presented as micrometers per minute . Agilent human miRNA 8×15K microarray was used to profile global miRNA expression with standard procedures . Arrays were scanned using an Agilent scanner and data were extracted using Agilent's Feature Extraction software set to the default miRNA analysis protocol . The raw data were normalized and quantified by the LIMMA ( linear models for microarray data ) library , part of the Bioconductor project , using the R statistical environment . The raw data from all arrays were first background-corrected and then normalized using quantile normalization . The difference in miRNA expression between different groups was analyzed using empirical Bayes method implemented in the LIMMA package . P values obtained from the multiple comparison tests were corrected by false discovery rates . Six- to eight-week-old athymic female nude mice were used for tumor cell implantation . Cells were injected subcutaneously into the left back of recipient animals . For recipients of MCF7 cells , Depo-Estradiol ( Pfizer ) was given to the mice two days before tumor cell implantation ( 1 . 5 mg/kg body weight ) , and the same dose was given once a week after implantation . Tumor size was measured weekly using a caliper , and tumor volume was calculated using the standard formula: 0 . 5×L×W2 , where L is the longest diameter and W is the shortest diameter . Mice were euthanized when they met the institutional euthanasia criteria for tumor size and overall health condition . The tumors were removed and weighed . The harvested tumor samples were fixed in 10% buffered formalin for 12 h , washed with PBS , transferred to 70% ethanol , embedded in paraffin , sectioned and stained with hematoxylin and eosin ( H & E ) . Samples were deparaffinized and rehydrated . Antigen retrieval was done using 0 . 01 M sodium-citrate buffer ( pH 6 . 0 ) at a sub-boiling temperature for 10 min after boiling in a microwave oven . To block endogenous peroxidase activity , the sections were incubated with 3% hydrogen peroxide for 10 min . After 1 h of preincubation in 5% normal goat serum to prevent nonspecific staining , the samples were incubated with the antibody to Ki-67 ( 1∶50 , BD Biosciences , 550609 ) or E-cadherin ( 1∶500 , Cell signaling Technology , 3195 ) at 4°C overnight . The sections were incubated with a biotinylated secondary antibody ( 1∶500 , Vector Laboratories , BA-9200 ) and then incubated with avidin-biotin peroxidase complex solution ( Vector Laboratories , PK-6100 ) for 30 min at room temperature . Color was developed using the Diaminobenzidine ( DAB ) substrate kit ( BD Biosciences , 550880 ) . Counterstaining was carried out using Harris modified hematoxylin . We obtained level 3 data of mRNA expression , miRNA expression and gene methylation of human breast tumors from Synapse ( http://synapse . org ) ( syn1461151 ) . The mRNA expression levels ( RNA-Seq by Expectation Maximization , RSEM ) were measured by Illumina HiSeq ( V2 ) . The miRNA expression levels ( normalized read counts ) were measured by Illumina HiSeq and Illumina Genome Analyzer . The DNA methylation level ( β value ) was measured by the Illumina Infinium Human DNA Methylation 450 platform . The breast cancer subtype information ( luminal A , luminal B , basal-like and HER2 subtypes ) was described previously [17] . Paired t test was used to compare miRNA expression levels in all cases with miRNA expression data available from paired normal and cancer tissues ( n = 56 ) . Wilcoxon signed-rank test was used to compare MIR100HG methylation levels in all cases with gene methylation data available from paired normal and cancer tissues ( n = 90 ) . Spearman rank correlation test was used to assess the correlation between miR-100 expression level and MIR100HG gene methylation level ( n = 522 ) , and the correlation between miRNA expression levels and mRNA expression levels in all breast cancer samples with both miRNA and mRNA expression data available ( n = 777 ) . Each experiment was repeated three times or more . Unless otherwise noted , data are presented as mean ± s . e . m . , and two-tailed Student's t test was used to compare two groups for independent samples . Statistical methods used for TCGA data analysis are described above . P<0 . 05 was considered statistically significant . All animal experiments were performed in accordance with a protocol approved by the Institutional Animal Care and Use Committee of MD Anderson Cancer Center .
|
Induction of epithelial-mesenchymal transition ( EMT ) in epithelial tumor cells has been shown to enhance migration , invasion and cancer ‘stemness’ . Here we demonstrate that a miRNA downregulated in human breast tumors , miR-100 , can simultaneously induce EMT and inhibit tumorigenesis , migration and invasion through direct targeting of distinct genes . This is the first report of an EMT inducer that suppresses cell movement and tumor invasion , which indicates that EMT is not always associated with increased tumorigenesis , migration and invasion , and that all EMT inducers are not equal: while some of them can promote tumorigenicity , motility and invasiveness , others inhibit these properties owing to their ability to concurrently target both EMT-repressing genes and oncogenic/pro-invasive genes . These findings provide new insights into the complex roles of EMT inducers .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"biology"
] |
2014
|
miR-100 Induces Epithelial-Mesenchymal Transition but Suppresses Tumorigenesis, Migration and Invasion
|
Trachoma is a blinding disease , initiated in early childhood by repeated conjunctival infection with the obligate intracellular bacterium Chlamydia trachomatis . The population prevalence of the clinical signs of active trachoma; ‘‘follicular conjunctivitis” ( TF ) and/or ‘‘intense papillary inflammation” ( TI ) , guide programmatic decisions regarding the initiation and cessation of mass drug administration ( MDA ) . However , the persistence of TF following resolution of infection at both the individual and population level raises concerns over the suitability of this clinical sign as a marker for C . trachomatis infection . We systematically reviewed the literature for population-based studies and those including randomly selected individuals , which reported the prevalence of the clinical signs of active trachoma and ocular C . trachomatis infection by nucleic acid amplification test . We performed a meta-analysis to assess the relationship between active trachoma and C . trachomatis infection before and after MDA . TF and C . trachomatis infection were strongly correlated prior to MDA ( r = 0 . 92 , 95%CI 0 . 83 to 0 . 96 , p<0 . 0001 ) ; the relationship was similar when the analysis was limited to children . A moderate correlation was found between TI and prevalence of infection . Following MDA , the relationship between TF and infection prevalence was weaker ( r = 0 . 60 , 95%CI 0 . 25 to 0 . 81 , p = 0 . 003 ) and there was no correlation between TI and C . trachomatis infection . Prior to MDA , TF is a good indicator of the community prevalence of C . trachomatis infection . Following MDA , the prevalence of TF tends to overestimate the underlying infection prevalence . In order to prevent unnecessary additional rounds of MDA and to accurately ascertain when elimination goals have been reached , a cost-effective test for C . trachomatis that can be administered in low-resource settings remains desirable .
Sight loss from trachoma is the end result of a scarring disease process that is initiated in early childhood by the obligate intracellular bacterium Chlamydia trachomatis [1] . Repeated infection of the conjunctiva by C . trachomatis causes a recurrent chronic follicular conjunctivitis ( TF ) of the upper eyelid mucosal surface ( Fig 1 ) [2] . This can sometimes be particularly severe with intense papillary inflammation ( TI ) . Together , TF and / or TI are collectively referred to as “Active Trachoma” . Conjunctival scarring gradually develops , usually becoming visible from early adulthood . Eventually the scarring causes the eyelashes to turn in and scratch the surface of the eye . If left uncorrected , trichiasis traumatises the cornea surface , resulting in opacification and sight loss . There is a variable relationship between the clinical signs of Active Trachoma and the presence of C . trachomatis infection . The natural history of an infection episode in children is probably characterised by a brief “pre-clinical” phase , in which there is detectable infection but the clinical signs of the inflammatory response are yet to develop ( Fig 1 ) . Human volunteer experiments in which the conjunctiva was inoculated with C . trachomatis indicated that the signs of disease typically take about 10 days to develop in previously uninfected individuals [3] . This is followed by a variable period of time in which both infection and disease can be detected at the same time; this may last for several days to many weeks . The immune response brings the infection under control , completely clearing it or reducing it to undetectable levels . However , inflammatory clinical signs persist , typically lasting several weeks after the resolution of the infection . In children aged 4–15 years , data from longitudinal cohort studies estimate the median duration of infection range between 23 days and 8 weeks , and the median duration of disease between 54 days and 18 weeks [4–6] . The duration of disease and infection declines with increasing age . Therefore , at an individual level there is frequently a large mismatch between when infection can be detected and the clinical signs of disease are found [7] . The most recent estimates from the World Health Organization ( WHO ) Alliance for the Global Elimination of Trachoma by 2020 ( GET2020 ) estimates indicate that about 200 million people live in trachoma endemic areas in 42 countries , 2 . 2 million have visual impairment or blindness , and about 3 . 2 million have trichiasis [8] . To meet this large public health challenge , the GET2020 Alliance recommends the implementation of the SAFE Strategy which tackles the disease at different stages: Surgery to correct trichiasis , Antibiotics to treat chlamydial infection and Facial cleanliness and Environmental improvements to suppress transmission of the infection [1] . The antibiotic azithromycin is being used in community-wide mass drug administration ( MDA ) ; it is given as a single oral dose on an annual basis in endemic districts . Decisions around when to initiate and stop MDA are guided by the prevalence of TF in children in endemic communities . According to the current guidelines , azithromycin MDA is indicated for entire districts where the prevalence of TF in 1–9 year olds is ≥10% . Moreover , the determination of whether a program has controlled the active stage of trachoma as a public health problem also rests on the district level prevalence of TF [9] . Therefore , in view of the significance attached to TF for making programmatic decisions , it is important to understand the relationship between active disease and chlamydial infection in endemic communities . It is probable that this relationship changes after the introduction of MDA and will vary with different levels of endemicity . Several studies have specifically investigated what clinical signs can tell us about infection [7 , 10–19] . However , in addition there are many other population-based studies , which report on both disease and infection , that can contribute information . Here we systematically review the published literature for reports that can inform our understanding of the relationship between clinical signs of active disease and C . trachomatis infection , both before and after the introduction of MDA .
In this review we included population-based studies and studies involving a random selection of participants that report the relationship between signs of Active Trachoma ( TF , TI , TF/TI ) and the detection of ocular C . trachomatis by nucleic acid amplification tests ( NAAT ) , including polymerase chain reaction ( PCR ) , polymerase chain reaction/enzyme immunoassay ( PCR-EIA ) , ligase chain reaction ( LCR ) and transcription-mediated amplification ( TMA ) . We excluded studies which did not test individuals without Active Trachoma for infection . References were identified through searching PubMed for articles using the terms ( i ) “trachoma” AND “Chlamydia trachomatis” , ( ii ) “Trachoma” AND “PCR” , ( iii ) “Trachoma” AND “LCR” , ( iv ) “Trachoma” AND “azithromycin” . The search was limited to 1991 onwards , the year of the first report of the use of PCR to test for ocular C . trachomatis infection in a trachoma endemic population [20] . The search was last updated on 5th May 2016 . The titles and abstracts of all articles resulting from these searches were screened by two authors ( AMR , MJB ) for potentially eligible publications . These two authors then independently assessed the potentially eligible articles for inclusion and extracted the data . The bibliographies of publications meeting the inclusion criteria were also reviewed for any additional publications not already identified . Studies were excluded where the participant sample was not population-based . Several studies were identified with multiple related published reports arising from the same data . These are considered as a single study , but for completeness we include all the relevant references . Core information was extracted using a standardised form . The core information included country , year of publication , study design , study population size , age group , TF prevalence , TI prevalence , TF/TI prevalence ( if TF alone was not reported ) , C . trachomatis infection prevalence , diagnostic test used , grading system , use of antibiotic for infection control . The clinical grading system was recorded: the 1981 Detailed WHO FPC System or the 1987 Simplified WHO Trachoma Grading System [2 , 21] . For the purpose of this review the prevalence levels of TF in the populations studied were categorised as follows: Hypoendemic <10% , Mesoendemic 10–20% and Hyperendemic >20% [12] . Due to the heterogeneity in the study designs and the reporting of data , only a limited meta-analysis was performed . Studies report the results for different age groups . Where available we separately present both the “All Age” results and those for children ( ≤15 years ) . In the meta-analysis we use the results from children only where these are available; where these are not available , the all age results are used . Summary graphs are presented of the relationship between: ( i ) the community prevalence of disease signs and C . trachomatis infection , ( ii ) the sensitivity , specificity , positive predictive value ( PPV ) , negative predictive value ( NPV ) of TF for infection , by the community prevalence of disease signs . The degree of correlation between these was tested using Pearson’s correlation coefficient , weighted by study size . The relationship between signs and infection was analysed separately for pre-treatment and post-treatment data , then a test for interaction was performed on the two datasets combined to test whether the association between signs and infection was similar in the pre and post-treatment data . Forest plots were generated to illustrate the strength and heterogeneity of the relationship between disease signs and detection of infection; study heterogeneity was estimated using the I2 statistic . Hierarchical summary receiver operating characteristics ( HSROC ) curves were plotted to illustrate the relationship between the sensitivity and specificity of signs of TF for the presence of C . trachomatis infection and a pooled estimate of sensitivity and specificity was made in both the pre and the post MDA studies . All analyses were performed using Stata 13 . A Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA ) checklist and flow diagram are included in the supporting information ( S1 Checklist and S1 Fig ) .
Of the 36 pre-treatment study populations , 24 were hyperendemic , 8 mesoendemic and 4 hypoendemic ( Table 1 ) . The studies were conducted in populations from Tanzania ( 12 ) , The Gambia ( 7 ) , Ethiopia ( 6 ) , Nepal ( 4 ) , Niger ( 3 ) , Cameroon ( 1 ) , Guinea Bissau ( 1 ) , Egypt ( 1 ) and Australia ( 1 ) . The Simplified WHO Trachoma Grading System was used in 28 studies and 8 used the Detailed WHO FPC System . The majority of studies ( 25 ) used the commercially produced Amplicor CT/NG ( Roche ) PCR assay , five used ligase chain reaction ( LCR , Abbott Laboratories ) , and six used in-house PCR assays . Four of the 36 studies listed in Table 1 did not report the community prevalence of C . trachomatis infection , however they report infection on a selected subset of individuals [11 , 22–24] . These four studies were therefore retained in Table 1 as they contribute useful information , however they were excluded from the meta-analysis . Of the remaining 32 studies included in the meta-analysis , 29 studies reported data for childhood age groups ( variable age ranges ) , seven of which also reported all age data , and three studies reported only all age data . Prior to the introduction of MDA there was evidence of a strong positive correlation between the community-level prevalence of Active Trachoma and the community-level prevalence of detected C . trachomatis , using data from all 32 studies ( r = 0 . 92 , 95%CI 0 . 83 to 0 . 96 , p<0 . 0001 ) , Fig 2A . The correlation was similar when the analysis was limited to children only data ( 29 studies; r = 0 . 91 , 95%CI 0 . 82 to 0 . 96 , p<0 . 0001 ) . The correlation was much weaker for all age data ( 10 studies; r = 0 . 51 , 95%CI -0 . 18 to 0 . 86 p = 0 . 13 ) . Overall , the community prevalence of TF is typically greater than the underlying community prevalence of detected C . trachomatis , with TF having a higher prevalence than C . trachomatis in 25 out of the 32 studies . The mean difference in prevalence was 10 . 4% ( 95% CI 5 . 9%-15 . 0% , p = 0 . 001 ) . There were 19 studies that reported the prevalence of TI separately from TF . There was evidence of a moderate positive correlation between the community-level prevalence of TI and the community-level prevalence of detected C . trachomatis ( r = 0 . 75 , 95%CI 0 . 45 to 0 . 90 p = 0 . 0002 ) , Fig 2B . Although there are only a limited number of studies , in all communities where the TI prevalence was >20% the prevalence of C . trachomatis was at least 30% . In contrast , where the TI prevalence was below 20% there was a considerable degree of variation in the underlying community prevalence of infection . There was sufficient data at the individual level presented to estimate the sensitivity , specificity , PPV and NPV for 21 studies . The sensitivity of TF for identifying the presence of C . trachomatis infection varied substantially with the prevalence of TF in the community , Fig 3A . There was evidence of a strong positive correlation ( r = 0 . 82 , 95%CI 0 . 59 to 0 . 92 , p<0 . 0001 ) . In contrast , there was a strong negative correlation between the specificity of TF for infection and the community prevalence of TF ( r = -0 . 92 , 95%CI -0 . 97 to -0 . 80 , p<0 . 0001 ) , Fig 3B . The Forest plot of the pre-treatment relationship between disease and detection of infection at the individual level showed an overall strong association ( OR 6 . 05 , 95% CI 5 . 49 to 6 . 67 , p<0 . 0001 ) , although there was marked heterogeneity ( I2 = 87 . 1% , p<0 . 001 ) , Fig 4A . A plot of sensitivity against specificity of the pre-treatment studies using TF as a test for C . trachomatis infection at the individual level showed that the overall estimated sensitivity was moderate ( 57 . 1% ( 45 . 4–68 . 1% ) and the overall specificity was good ( 81 . 1% ( 73 . 4–87 . 0% ) , Fig 5A . The relationship between the community-level prevalence of Active Trachoma and the proportion of people with TF who were infected with C . trachomatis ( Positive Predictive Value , PPV ) showed a positive correlation ( r = 0 . 80 , 95%CI 0 . 56 to 0 . 91 , p<0 . 0001 ) , Fig 3C . The pattern of this distribution suggests that when the community-level prevalence of TF is below 20% the PPV of TF for the presence of C . trachomatis infection drops substantially . Above a community-level prevalence of TF of 20% the PPV is typically 40–70% , across a wide range of TF prevalence . The relationship between the community-level prevalence of Active Trachoma and the proportion of people without TF who were not infected with C . trachomatis ( Negative Predictive Value , NPV ) showed a strong negative correlation ( r = -0 . 81 , 95%CI -0 . 92 to -0 . 57 , p<0 . 0001 ) , Fig 3D . When the community-level TF prevalence was greater than 20% , the NPV was more variable . Of the 21 studies from populations following the introduction of MDA , prior to the first treatment , 15 were hyperendemic , 3 mesoendemic and 3 hypoendemic ( Table 2 ) . The studies were conducted in populations from Tanzania ( 10 ) , Ethiopia ( 5 ) , The Gambia ( 3 ) , Nepal ( 2 ) , and Egypt ( 1 ) . The Simplified WHO Trachoma Grading System was used in 17 studies and four used the Detailed WHO FPC System . The majority of studies ( 15 ) used the Amplicor CT/NG ( Roche ) PCR assay , four used LCR ( Abbott ) , one used an in-house PCR and one used the Aptima Combo2 ( Hologic ) TMA assay . There was one study included in Table 2 that did not report the community prevalence of C . trachomatis infection , and was therefore not included in the meta-analysis [23] . One of the Gambian studies involved surveys in four separate districts; we have included these as four separate sets of data in the analysis [25] . This resulted in 23 discrete studies included in the meta-analysis: for 20 studies data was available on children only ( variable age ranges ) , two of which also reported all age data , and three other studies reported only all age data . Following the introduction of MDA there was evidence of a moderate positive correlation between the community-level prevalence of Active Trachoma and the community-level prevalence of detected C . trachomatis , using data from all 23 studies ( r = 0 . 60 , 95%CI 0 . 25 to 0 . 81 , p = 0 . 003 ) , Fig 2C . The relationship was similar when the analysis was limited to the 20 studies of children only ( r = 0 . 60 , 95%CI 0 . 21 to 0 . 82 , p = 0 . 005 ) . However , for the five studies reporting results for all ages there was no significant correlation ( r = 0 . 72 , 95%CI -0 . 18 to 0 . 86 p = 0 . 18 ) . The overall impression is that the relationship between disease and infection is more uncertain post-MDA , such that the community-level prevalence of TF can substantially overestimate the underlying community-level prevalence of C . trachomatis; the community prevalence of TF can remain high ( >20% ) even when the prevalence of infection has declined ( <10% ) , Fig 2C and 2E . We found evidence that the relationship between the prevalence of TF and the prevalence of C . trachomatis infection differs between pre and post-MDA . Assuming a linear relationship between C . trachomatis and TF , the prevalence of C . trachomatis was associated with an expected 6 . 5% increase for every 10% increase in TF in pre-MDA studies , compared with an increase of 2 . 8% in post-MDA ( test for interaction p = 0 . 004 ) . This demonstrates that as the prevalence of TF increases , the expected increase in C . trachomatis is higher in pre-MDA communities than in post-MDA communities . There were only eight studies that reported the prevalence of TI separately from TF after the introduction of MDA . There was no evidence of a correlation between the community-level prevalence of TI and the community-level prevalence of detected C . trachomatis ( r = 0 . 50 , 95%CI -0 . 31 to 0 . 89 p = 0 . 20 ) , Fig 2D . However , it is noteworthy that the prevalence of TI was low ( <10% ) in all but two communities . There was sufficient data presented to estimate the sensitivity , specificity , PPV and NPV for only 10 studies at the individual level after introduction of MDA . Therefore , only limited inference can be drawn . The sensitivity of TF for identifying the presence of C . trachomatis infection after the introduction of MDA varied widely , across a range of community-level TF prevalence levels , Fig 6A . In contrast , there was evidence of a strong negative correlation between the specificity of TF for infection and the community prevalence of TF ( r = -0 . 99 , 95%CI -0 . 99 to -0 . 96 , p<0 . 0001 ) , Fig 6B . The Forest plot of the post-treatment relationship between disease and detection of infection at the individual level showed an overall strong association ( OR 8 . 38 , 95% CI 7 . 09 to 9 . 90 , p<0 . 0001 ) , although there was marked heterogeneity ( I2 = 75% , p<0 . 001 ) , Fig 4B . Although this overall OR is slightly higher post-MDA compared to pre-MDA , it should be noted that this result is calculated from individual-level data , whereas the correlations between TF and C . trachomatis infection prevalence ( Fig 2 ) are calculated from population-level data . Compared to the pre-MDA situation ( Fig 5A ) , after the introduction of MDA the individual level sensitivity was weaker ( 39 . 2% ( 30 . 9% , 48 . 2% ) while specificity was stronger ( 96 . 1% ( 89 . 6% , 98 . 6% ) , Fig 5B . The community-level prevalence of Active Trachoma and the proportion of people with TF who were infected with C . trachomatis ( PPV ) did not appear to be correlated ( r = 0 . 16 , 95%CI -0 . 52 to 0 . 72 , p = 0 . 65 ) , Fig 6C . Finally , the proportion of people without TF who were not infected with C . trachomatis ( NPV ) was high ( >90% ) , across the limited range of community-level prevalence of Active Trachoma in these studies , Fig 6D .
It has long been observed that the relationship between the signs of Active Trachoma and the detection of C . trachomatis infection at the individual level is not highly concordant [12] . Surveys , including those in this review , consistently find that within endemic populations there are many individuals with signs of disease who do not have detectable infection and conversely there are people who do not meet the diagnostic criteria for Active Trachoma ( TF or TI ) who do have detectable infection . Therefore , at the individual level , signs of infection cannot be depended upon to determine which members of an endemic community have ocular C . trachomatis infection . In a trachoma endemic population , the main reason for this mismatch between active disease and infection is probably the different time courses of the typical infection and disease episodes , outlined in the introduction and illustrated in Fig 1 . In addition , it is also possible that other factors contribute to this mismatch . Some individuals who have detectable infection but do not meet the diagnostic criteria for TF may have a mild trachomatous follicular conjunctivitis . Others may have previously acquired immunity and are able clear the infection without developing any detectable inflammatory signs . Alternatively , a positive NAAT test for C . trachomatis in a clinically normal individual could arise through cross-contamination during sample collection or processing . Clinical signs similar to those of Active Trachoma can also arise for other reasons , such as viral or bacterial infections , vernal conjunctivitis and hypersensitivity reactions [26] . It is clear that clinical signs are not a reliable indicator for C . trachomatis at the individual level . There is no point-of-care diagnostic test available for programmes to use to determine which individual members of a community are infected , and who would therefore benefit from targeted antibiotic treatment . Moreover , a strategy of testing everybody is not considered a practical option . Therefore , the WHO guidance and the standard practice is to conduct community-wide antibiotic distribution of the entire population of endemic districts . Decisions around the initiation and cessation of trachoma control measures are based on the prevalence of TF in children aged 1–9 years , determined through district level surveys , such as those conducted by the Global Trachoma Mapping Project . District-wide antibiotic treatment programmes and F&E measures to suppress transmission are initiated where the initial prevalence of TF is ≥10% . Below 10% TF the advice is to conduct sub-district level surveys . If a sub-district has ≥10% TF MDA and F&E are implemented . For sub-districts between 5% and 9 . 9% TF the guidance is to consider targeted MDA and F&E measures . For sub-districts <5% no MDA is needed and implementation of F&E can be considered . The dependence on the clinical signs of disease to guide programmatic decisions raises the important question of how accurate these clinical signs are as a proxy measure for C . trachomatis infection at the population level . This question is particularly relevant after the introduction of MDA , when the association between clinical signs and infection prevalence at the population level is weaker , and as we try to determine when elimination targets have been reached . In this systematic review , we found that prior to the introduction of antibiotic treatment the relationship between the community-level prevalence of TF in children correlated well with that of infection . The prevalence of TF was usually slightly greater than that of infection . Therefore , over a wide prevalence range , the community-level prevalence of TF in children is a reliable indicator that broadly reflects the underlying population burden of infection . The association between disease and infection for all ages was much weaker than that for children only; this supports the rationale for measuring TF in 1–9 year olds as the key indicator group for determining the need of antibiotic . The prevalence of TI was less well correlated than TF with infection before MDA , with the disease prevalence generally underestimating the level of infection . At the individual level the utility of TF as a marker for infection is highly sensitive to the underlying prevalence of TF . Both the sensitivity and PPV rise substantially with increasing TF prevalence , and the specificity and NPV both drop . This sensitivity increase is therefore offset by a corresponding decrease in the specificity of this test in high TF prevalence communities . The usefulness of TF as an indicator of an individual’s infection status is dependent on both the sensitivity and the specificity of the test; too low a sensitivity leads to C . trachomatis infected individuals not receiving treatment , whereas too low a specificity leads to wasted resources treating uninfected individuals . The PPV possibly gives the clearest indication of where TF is useful as an indicator of an individual’s infection status; when the prevalence of TF is low ( ~5–10% ) , TF will only indicate an estimated 10% probability that the individual has C . trachomatis infection . This probability increases steadily with prevalence and once the community prevalence reaches 30% , the estimated PPV is in the order of 50–70% . Thus , where the population TF prevalence is above 30% , a positive TF diagnosis gives a 50–70% probability that an individual will be C . trachomatis infected . After the introduction of MDA the relationship between the community prevalence of TF and chlamydial infection is less certain . Although the relationship between Active Trachoma and C . trachomatis infection appears to remain strong at the individual level , the population-level data suggests that post-MDA , Active Trachoma has a greater tendency to overestimate the underlying population prevalence of C . trachomatis infection . Below the 10% TF level the prevalence of infection was consistently low , and therefore below this level , TF prevalence appears to be a good marker for infection having being brought under control . However , in the studies where the prevalence of TF was above 10% after the introduction of MDA , the underlying prevalence of C . trachomatis infection was much less predictable . In some settings TF prevalence persists at high levels despite relatively little infection being detected . There might be several reasons for this observation . For example , if the loads of infection are substantially lower following the introduction of MDA these may not be so readily detected by diagnostic tests . Other bacterial species might also provoke a follicular conjunctivitis more readily in individuals previously infected with C . trachomatis [26] . However , whatever the explanation , it is likely that in some settings , the prevalence of TF will underestimate the impact MDA has had on the prevalence of C . trachomatis . This might lead to the on-going use of MDA after the infection has been adequately controlled . There is much less published data on the relationship between TI and infection following the introduction of MDA . In general , the prevalence of TI appears to reduce more readily than TF . A number of studies have investigated the relationship between the load of infection and disease signs . These suggest that TI is particularly associated with high loads of infection [27] . Therefore , the decline in TI may reflect a shift to less intense infections . However , there were a few studies in which the prevalence of TI was low , but the prevalence of infection remained substantial . We identified a reasonable number of studies reporting the community-level relationship between disease and infection prior to treatment , over a wide TF prevalence range . There were , however , fewer studies documenting the situation following the introduction of MDA , potentially limiting the conclusions that can be drawn after MDA . However , there was generally less detail in these reports about the individual-level relationship between clinical signs and infection . The studies came mostly from several East and West African countries , providing reasonable geographical coverage of the regions with the greatest trachoma burden . Standard WHO definitions of disease were generally used and the large majority of studies used the same commercially produced PCR assay , providing consistency across studies . However , there was some methodological heterogeneity . The age groups reported varied; where possible we use the data for children only in the meta-analysis to try to provide greater consistency between studies . The sample sizes varied considerably ( from 56 to 7817 ) ; we weighted our analyses to adjust for size . The sampling methodology also varied considerably . The studies included were generally population-based samples or surveys of the entire resident population of a defined area . Overall , the use of TF prevalence to guide the decision to initiate MDA in previously untreated districts appears to be reliable . In contrast , the situation following treatment is more uncertain , calling into question the reliability of clinical signs for monitoring progress towards the achievement of the elimination targets [28] . There are reports from hyperendemic regions that have received many rounds of high-coverage MDA that suggest that the prevalence of TF can be recalcitrant , even when C . trachomatis infection appears to have been brought under control . Therefore , contextually appropriate , cost-effective tests for C . trachomatis infection that can be administered in low-resource settings , and used to estimate the infection prevalence in a population-based sample , would be very helpful in guiding decisions around the cessation of MDA . Such tests are anticipated to reduce the number of annual rounds of MDA required and lead to the confirmation of trachoma control at an earlier stage .
|
Trachoma is the leading infectious cause of blindness worldwide , caused by the bacterium Chlamydia trachomatis . Repeated infection of the conjunctiva during childhood can initiate chronic conjunctival inflammation . This can lead to conjunctival scarring , in turning of the eyelashes , abrasion of the eyelashes on the cornea and eventually blindness later in adulthood . The World Health Organization recommends mass drug administration ( MDA ) for infection control when the prevalence of the clinical sign of Active Trachoma ( TF ) is ≥10% in 1–9 year olds . This systematic review of the literature examined the relationship between TF and C . trachomatis infection before and after MDA in order to evaluate the usefulness of TF for guiding trachoma control programmes . The population prevalence of TF and C . trachomatis infection were strongly correlated prior to MDA , however the relationship was weaker after MDA with a greater tendency for TF to overestimate the underlying infection prevalence . A cost effective test for C . trachomatis suitable for use in low resource settings could prevent unnecessary additional rounds of MDA in the population and could identify when trachoma elimination goals have been reached at an earlier time point .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"antimicrobials",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"chlamydia",
"trachomatis",
"pathogens",
"drugs",
"tropical",
"diseases",
"microbiology",
"geographical",
"locations",
"bacterial",
"diseases",
"tanzania",
"antibiotics",
"eye",
"diseases",
"sexually",
"transmitted",
"diseases",
"chlamydia",
"neglected",
"tropical",
"diseases",
"ethiopia",
"pharmacology",
"bacteria",
"bacterial",
"pathogens",
"africa",
"infectious",
"diseases",
"chlamydia",
"infection",
"medical",
"microbiology",
"microbial",
"pathogens",
"people",
"and",
"places",
"gambia",
"ophthalmology",
"microbial",
"control",
"biology",
"and",
"life",
"sciences",
"trachoma",
"organisms"
] |
2016
|
The Relationship between Active Trachoma and Ocular Chlamydia trachomatis Infection before and after Mass Antibiotic Treatment
|
In the fission yeast Schizosaccharomyces pombe , genetic evidence suggests that two mediators , Rad22 ( the S . pombe Rad52 homolog ) and the Swi5-Sfr1 complex , participate in a common pathway of Rhp51 ( the S . pombe Rad51 homolog ) –mediated homologous recombination ( HR ) and HR repair . Here , we have demonstrated an in vitro reconstitution of the central step of DNA strand exchange during HR . Our system consists entirely of homogeneously purified proteins , including Rhp51 , the two mediators , and replication protein A ( RPA ) , which reflects genetic requirements in vivo . Using this system , we present the first robust biochemical evidence that concerted action of the two mediators directs the loading of Rhp51 onto single-stranded DNA ( ssDNA ) precoated with RPA . Dissection of the reaction reveals that Rad22 overcomes the inhibitory effect of RPA on Rhp51-Swi5-Sfr1–mediated strand exchange . In addition , Rad22 negates the requirement for a strict order of protein addition to the in vitro system . However , despite the presence of Rad22 , Swi5-Sfr1 is still essential for strand exchange . Importantly , Rhp51 , but neither Rad22 nor the Swi5-Sfr1 mediator , is the factor that displaces RPA from ssDNA . Swi5-Sfr1 stabilizes Rhp51-ssDNA filaments in an ATP-dependent manner , and this stabilization is correlated with activation of Rhp51 for the strand exchange reaction . Rad22 alone cannot activate the Rhp51 presynaptic filament . AMP-PNP , a nonhydrolyzable ATP analog , induces a similar stabilization of Rhp51 , but this stabilization is independent of Swi5-Sfr1 . However , hydrolysis of ATP is required for processive strand transfer , which results in the formation of a long heteroduplex . Our in vitro reconstitution system has revealed that the two mediators have indispensable , but distinct , roles for mediating Rhp51 loading onto RPA-precoated ssDNA .
Homologous recombination ( HR ) generates genetic diversity by rearranging DNA sequences using homologous DNA information . It is also an important mechanism for repairing DNA double-stranded breaks ( DSBs ) and restarting stalled DNA replication forks . Accordingly , HR is essential to the preservation of genome integrity; defects in HR result in hypersensitivity to genotoxic agents and chromosomal aberrations . One conspicuous example of the role of HR in genome integrity is cancer prevention via the tumor suppressors BRCA1 and BRCA2 , both of which interact with the Rad51 recombinase [1] . Genetic analyses of DSB repair and mitotic and meiotic HR in the budding yeast Saccharomyces cerevisiae have revealed a major pathway for HR that is under the control of proteins in the Rad52 epistasis group [2–4] . The Rad51 recombinase belongs to the Rad52 group and plays a key role in HR: Rad51 forms nucleoprotein filaments with single-stranded DNA ( ssDNA ) , referred to as presynaptic filaments , and promotes strand exchange with donor DNA in an ATP-dependent manner . A series of analyses suggested that the assembly pathway for Rad51 on ssDNA in vivo is spatiotemporally regulated by replication protein A ( RPA ) and other Rad52 epistasis group proteins , such as Rad52 and Rad55–57 [5 , 6] . RPA immediately binds to ssDNA regions once they are formed ( e . g . , by the resection of DSB ends or by stalled replication forks ) . Rad51 alone cannot bind to RPA-coated ssDNA , as RPA has higher affinity for ssDNA than does the Rad51 recombinase . Rad52 assists in loading Rad51 onto RPA-coated ssDNA and in assembling the Rad51 nucleoprotein filament . The Rad51 paralogs Rad55 and Rad57 , which form a heterodimer , assist Rad51-mediated filament assembly and/or stabilize the filament , leading to efficient strand exchange [7 , 8] . Proteins that facilitate Rad51 loading or filament stabilization are referred to as mediators [3 , 9] . The basic characteristics of the early steps of HR are widely conserved among eukaryotes; however , multicellular eukaryotes , including humans , have five Rad51 paralogs , XRCC2 , XRCC3 , RAD51B , RAD51C , and RAD51D . Several Rad51 paralog complexes have been observed , and these are also assumed to function as mediators [10 , 11] . In addition , BRCA2 , a tumor suppressor , has been suggested to act as a recombination mediator [12–15] . The fission yeast Schizosaccharomyces pombe , which is evolutionarily distant from S . cerevisiae , uses an HR pathway very similar to that of budding yeast . However , a notable exception in S . pombe is the Swi5-Sfr1 complex , which functions as an additional mediator in the HR pathway involving Rad22 and Rhp51 ( fission yeast Rad52 and Rad51 homologs , respectively ) ( reviewed in [16] ) . The Swi5-Sfr1 complex operates in the Rhp51-dependent HR pathway in parallel with another mediator , the Rhp55-Rhp57 complex ( fission yeast Rad55 and Rad57 homologs , respectively ) in vivo [17 , 18] . Swi5 is a small protein that is evolutionarily conserved from S . cerevisiae to man , but it has no known protein motifs [17 , 19 , 20] . Sfr1 ( Swi5-dependent recombination repair protein 1 ) was identified as a Swi5 interactor that is involved in HR repair [17] . It shares homology with the C-terminal half of Swi2 , which overlaps the interaction region for Swi5 and Rhp51 [17] , and this region is modestly conserved from S . cerevisiae to man [20] . Sfr1 also lacks known functional motifs . S . cerevisiae Sae3 and Mei5 are Swi5 and Sfr1 homologs , respectively . However , unlike S . pombe Swi5 and Sfr1 , Sae3 and Mei5 are expressed only during meiosis . sae3 and mei5 mutants both show meiotic phenotypes very similar to those of the S . cerevisiae dmc1 mutant . These mutant phenotypes and the cellular localization of the two proteins suggest that they are specific for meiotic recombination associated with the meiosis-specific recombinase Dmc1 [20 , 21] . On the other hand , swi5 mutants exhibit more severe meiotic defects than do dmc1 mutants . Thus , S . pombe Swi5 clearly has an additional function beyond that involved in Dmc1-dependent activities [19] . We recently purified the Swi5-Sfr1 complex and found that it has ssDNA and double-stranded DNA ( dsDNA ) binding activities but that it lacks nuclease , helicase , and ATPase activities [22] . Consistent with genetic studies , the purified Swi5-Sfr1 complex stimulates both Rhp51- and Dmc1-mediated strand exchange in vitro [22] ( reviewed in [16] ) . The stimulation of Rhp51-mediated strand exchange is closely related to its ssDNA-dependent ATPase activity , which is enhanced by the Swi5-Sfr1 complex . The Swi5-Sfr1 complex does not enhance the binding of Rhp51 to ssDNA per se . On the other hand , the mediator enhances the binding of Dmc1 to ssDNA . The molecular bases of the different effects of Swi5-Sfr1 on the two recombinases are still unknown . An important issue emerged from our previous study . The Swi5-Sfr1 complex cannot efficiently overcome the inhibitory effect of RPA when RPA is bound to ssDNA prior to Rhp51 binding . This observation is consistent with the observation that the Swi5-Sfr1 complex does not appreciably affect the ssDNA binding capacity of Rhp51 . However , the canonical definition of a recombination mediator is that it is an ancillary factor that overcomes the inhibitory effect of RPA on a recombinase . S . cerevisiae Rad52 protein has been shown to interact directly with both RPA and Rad51 and to promote Rad51 filament formation by mediating the displacement of prebound RPA from ssDNA , leading to effective strand exchange mediated by Rad51 [23–25] . Therefore , it is possible that Rad22 acts exclusively to overcome the inhibitory effect of RPA and that the Swi5-Sfr1 complex acts exclusively to activate Rhp51 filaments . Thus , the concerted actions of these two mediators , Rad22 and the Swi5-Sfr1 complex , would direct the loading of Rhp51 onto ssDNA , leading to efficient strand exchange . The work described here addresses this hypothesis with an in vitro system that we have established , which reconstitutes the early central step of homologous recombination . We found that Rad22 overcomes the inhibitory effect of RPA on strand exchange mediated by Rhp51-Swi5-Sfr1 , as predicted . However , Swi5-Sfr1 is still essential for strand exchange , and both Rad22 and Swi5-Sfr1 are required for full reaction efficiency . In-depth analysis indicates that the two mediators work concertedly , but not exclusively , by different effects on Rhp51 to form the active filament required for effective DNA strand exchange . In addition , we have shown that the Swi5-Sfr1 mediator stabilizes and activates Rhp51-ssDNA filaments in an ATP-dependent manner , whereas Rad22 is not involved in Rhp51 activation .
We first determined whether purified Rad22 can overcome the inhibitory effect of RPA on strand exchange mediated by Rhp51-Swi5-Sfr1 . Figure 1A shows a schematic diagram of the Rhp51-mediated three-strand exchange assay used in this study , in which pairing of a ( + ) strand DNA ( circular ssDNA [css] ) with a homologous linear duplex DNA ( linear dsDNA [lds] ) derived from øX174 phage generates a joint molecule ( JM ) that is converted to nicked circular DNA ( NC ) and linear ssDNA products by strand exchange . S . pombe Rad22 was bacterially expressed and purified as described in Materials and Methods . As previously reported [22] , when css was first incubated with Rhp51 ( and the Swi5-Sfr1 mediator ) and then with RPA , large amounts of JMs and NCs were produced ( Figure 1B ) . RPA was essential for strand exchange ( compare lanes 1 and 2 in Figure 1B ) . In contrast , when css was first incubated with RPA and then with Rhp51 and Swi5-Sfr1 , JM and NC formation was dramatically reduced ( Figure 1B , lane 4 ) , as previously reported [22] . This inhibitory effect of RPA was blocked by the addition of purified Rad22 ( Figure 1B , lane5 ) . A roughly equivalent amount of bacterial ssDNA binding protein ( SSB; 2 μM ) could be substituted for RPA ( 1 μM ) when the strand exchange reaction was initiated by Rhp51/css complex formation ( Figure 1B , lane 3 ) . This result is consistent with RPA acting to prevent reversal of the already formed DNA joints by sequestering the free ssDNA , a reaction in which RPA can be replaced by SSB . However , when css was precoated with SSB ( 2 μM ) before adding Rhp51 and Swi5-Sfr1 , JM and NC product formation was severely reduced ( Figure 1B , lane 6 ) . More importantly , Rad22 could not overcome the inhibitory effect of SSB ( Figure 1B , lane 7 ) , indicating that functional interactions between RPA with Rad22 are important for this step . RPA-coated ssDNA is assumed to be a natural substrate for the in vivo strand exchange reaction . Therefore , the requirements for reactions initiated with RPA-coated ssDNA were examined ( Figure 1C ) . These reactions were strictly dependent on Rhp51 , Swi5-Sfr1 , RPA , and ATP . Rad22 was not essential , but in its absence , the levels of JM and NC products were severely reduced . Time-course experiments clearly demonstrated that Rad22 alone stimulated very little Rhp51-dependent strand exchange when RPA-coated ssDNA was used for a substrate ( Figure 1D ) . Swi5-Sfr1 alone stimulated the reaction , and reactions in which Rad22 and Swi5-Sfr1 were coincubated with Rhp51 proceeded with robust efficiency . These results clearly indicate that full reaction efficiency requires the functions of the two mediators , Rad22 and Swi5-Sfr1 . Next , we examined whether the timing of Rad22 addition affects the strand exchange reaction ( Figure 2A ) , since the addition order is critical for the Rhp51-Swi5-Sfr1–mediated reaction [22] . Note that protocol 2 in Figure 2A employs the same addition order as that of the standard reaction ( e . g . , the reaction shown in Figure 1B ) . Surprisingly , the time at which Rad22 was added was not crucial ( Figure 2A ) : all protocols were highly efficient , with the exception of reactions lacking Rad22 . These results indicate that Rad22 can overcome the inhibitory action of RPA irrespective of when it is added . These data suggest that Rad22 may coordinate strand exchange in a single mixture that includes all protein components . To address this possibility , we set up the following reactions . We prepared two mixtures , one containing RPA-coated css and the other containing all other reaction constituents . The reactions were started by combining the two mixtures and incubating at 37 °C for 120 min . As shown in Figure 2B , the results clearly indicate that this protocol allows fully efficient reactions initiated from RPA-coated ssDNA and that Rad22 is essential , since reaction efficiency was substantially decreased if it was omitted ( compare lanes 2 and 3 in Figure 2B ) . Furthermore , we found that a mixture containing all protein components ( RPA , Rad22 , Rhp51 , and Swi5-Sfr1 ) efficiently promoted strand exchange when combined with css ( Figure 2B , lane 4 ) . Rad22 is essential for this reaction as well ( compare lanes 4 and 5 ) . These results indicate that Rad22 coordinates the functions of all proteins and orchestrates DNA strand exchange in vitro . The Rad22-dependent reactions required ATP hydrolysis . ADP or ATPγS did not promote strand exchange ( Figure 2C ) , as previously observed for the Swi5-Sfr1–dependent reaction in the absence of Rad22 [22] . Interestingly , AMP-PNP supported a small amount of JM formation , but not NC production . The most effective concentration of Rad22 was approximately one tenth the concentration of Rhp51 . Higher concentrations of Rad22 inhibited the reaction ( Figure 2D ) . The inhibitory effect of RPA may result from its higher affinity for ssDNA compared to that of Rhp51 . If this is correct , either or both of the two mediators may function to displace RPA from ssDNA to facilitate the loading of Rhp51 , thereby leading to efficient presynaptic filament formation for the strand exchange reaction . In addition , once the correct filament is formed , it should be stabilized to protect against further RPA binding to ssDNA , since the thermodynamic equilibrium favors RPA binding to ssDNA . Either or both of the two mediators may be involved in this stabilization , as well . To test these hypotheses , we set up pull-down assays in which the conditions were the same as for the strand exchange reaction . We first performed a titration experiment of RPA to css-bound beads ( css beads ) ( 10 μM nucleotides ) . The results of this experiment indicated that approximately 30% of input RPA ( 1 μM ) was excess to RPA bound ( ∼0 . 7μM ) to css beads ( Figure 3A ) . Next , we analyzed the nucleotide dependency of Rhp51 binding to ssDNA ( Figure 3B and 3C ) . In the absence of adenine nucleotides , Rhp51 did not bind to ssDNA , indicating that the ssDNA binding activity of Rhp51 requires an adenine nucleotide . Titration experiments indicated that both ATP and AMP-PNP were highly efficient cofactors , whereas ADP and ATPγS were slightly less effective ( Figure 3B ) . Note that the amount of Rhp51 ( 5 μM ) used in the standard strand exchange reaction is excessive ( about 2 . 5- to 3-fold ) to its binding to css beads in the presence of any adenine nucleotide . We then performed competition experiments to compare the ssDNA binding activity of Rhp51 to that of RPA . Mixtures containing RPA ( 1 μM ) and Rhp51 ( 5 μM ) were incubated with css beads . Protein–DNA complexes were pulled down , and the amount of Rhp51 bound to ssDNA was analyzed by SDS-PAGE ( Figure 3D–3G ) . In the presence of adenine nucleotides , but in the absence of mediators , Rhp51 was not pulled down with css , indicating that RPA indeed has a higher affinity for ssDNA than does Rhp51 ( Figure 3D ) . A similar result has been reported for Rad51 and RPA from budding yeast [26] . We examined which of the two mediators assists Rhp51 loading onto ssDNA . The Swi5-Sfr1 complex alone promoted Rhp51 loading onto ssDNA in the presence of ATP and AMP-PNP ( Figure 3E ) . About 22% and 16% of the input Rhp51 were pulled down with ssDNA in the presence of ATP and AMP-PNP , respectively . Reactions containing ADP or ATPγS or lacking an adenine nucleotide only weakly supported Rhp51 loading ( about 5% of the input Rhp51 was pulled down ) . Incubation with Rad22 alone allowed a small amount of Rhp51 to be pulled down ( about 10% ) in the absence of a nucleotide cofactor or in the presence of ATP , ADP , ATPγS , or AMP-PNP ( Figure 3F ) . The amounts of Rhp51 that were pulled down were not significantly affected by the absence or presence of the nucleotide cofactor , or by the type of nucleotide in this case . Budding yeast Rad52 associates with RPA-ssDNA to accelerate the Rad51-mediated displacement of RPA [27] . However , since the binding of Rhp51 to ssDNA required an adenine nucleotide ( Figure 3B and 3C ) , the detection of Rhp51 in the pulled-down ssDNA protein complexes may reflect Rhp51 bound to Rad22 that is associated with preformed RPA-ssDNA complexes , rather than the direct binding of Rhp51 to ssDNA . The very low level of Rhp51 pulled down in the presence of ADP and ATPγS in the absence of Rad22 ( Figure 3D and 3E , lanes 4 and 5 ) suggests that this basal level of Rhp51 is dependent on Rad22 . We also observed Rhp51–Rad22 and Rad22–RPA interactions in the absence of an adenine nucleotide with an immunoprecipitation assay ( unpublished data ) ; similarly , tight Rad51–Rad52 and Rad52–RPA interactions have been reported for budding yeast and human cells [23–30] . These observations also support the notion that the small amount of Rhp51 pulled down when Rad22 alone is included is due to the basal level of Rhp51 that binds to Rad22 , which in turn is associated with RPA bound to css beads . When Rad22 and Swi5-Sfr1 were present , Rhp51 was loaded onto ssDNA ( 39% of input ) in an ATP-dependent manner ( Figure 3G ) . The amount of Rhp51 loaded under these conditions was much higher than with Swi5-Sfr1 alone or with Rad22 alone , indicating that the two mediators function synergistically to promote Rhp51 loading onto ssDNA . Interestingly , the nonhydrolyzable ATP analog , AMP-PNP , but not ATPγS , supported Rhp51 loading under these conditions . Small amounts of pulled-down Rhp51 were detected in the absence of nucleotide ( Figure 3G , lane 2 ) and in the presence of ADP or ATPγS ( Figure 3G , lanes 4 and 5 ) . These low levels are likely due to Rhp51 associated with ssDNA through the Rad22–RPA interaction , as mentioned above . We next examined which protein factor is directly involved in displacing RPA from ssDNA ( Figure 4 ) . RPA was first incubated with css beads . ATP , Rhp51 , and/or the two mediators were then added . The mixtures were pulled down , and proteins in the bound and unbound fractions were analyzed by SDS-PAGE . Neither Rad22 nor Swi5-Sfr1 could displace RPA from ssDNA ( Figure 4A–4C ) . Only high amounts of Rhp51 could displace RPA at a detectable level , indicating that Rhp51 per se , but not the mediators , is an intrinsic factor that displaces RPA from ssDNA ( Figure 4D ) . Swi5-Sfr1 stimulated Rhp51-dependent RPA displacement ( Figure 4F ) , whereas Rad22 alone stimulated displacement only modestly ( Figure 4E ) . See also the graphs of the amounts of displaced RPA ( Figure 4H ) and bound Rhp51 ( Figure 4I ) . At low protein concentrations , Rad22 appeared to assist the loading of Rhp51 onto ssDNA at a considerably high level , as judged by the amount of Rhp51 in the bound fractions ( Figure 4I ) . However , since the RPA levels in the unbound fractions increased only slightly when Rad22 was also included ( Figure 4H ) , the observed increase of Rhp51 in Figure 4I may be due to Rhp51 bound to RPA-coated css beads via Rad22 . It has been reported that Rad22 interacts with both Rhp51 and RPA [31 , 32] , and the ternary complex can also be detected in vitro by coimmunoprecipitation ( unpublished data ) . However , RPA displacement mediated by Rhp51 with Swi5-Sfr1 was further stimulated by coincubation with Rad22 ( Figure 4G ) . This synergistic effect was robust; three independent experiments yielded the same results . Therefore , these results again indicate that the two mediators function coordinately to assist in the displacement of RPA . In the absence of ATP , RPA displacement did not increase from basal levels , even in the presence of all the protein components in a 4-fold excess relative to their concentrations in the standard reaction ( unpublished data ) , indicating that RPA displacement requires ATP . This result is consistent with the hypothesis that Rhp51 per se is a displacing factor , since neither Rad22 ( unpublished data ) nor Swi5-Sfr1 is an ATP-binding protein [22] . Time-course experiments for RPA displacement from and Rhp51 loading onto ssDNA were also performed ( Figure 5 ) . Rhp51 alone was not loaded onto ssDNA and did not promote RPA displacement ( Figure 5A ) . A small amount of Rhp51 in the presence of Rad22 was loaded onto ssDNA , but this increase and the displacement of RPA ceased within 30 min ( Figure 5B ) . Swi5-Sfr1 promoted Rhp51 loading and RPA displacement ( Figure 5C ) , and coincubation of Rad22 and Swi5-Sfr1 strongly enhanced both processes ( Figure 5D ) . A quantitative presentation of these assays is shown in Figure 5E and 5F . Taking these results together , we conclude that the two mediators work concertedly , but not exclusively , to promote Rhp51 loading onto and RPA displacement from ssDNA to form the active presynaptic filament required for effective DNA strand exchange . The results described above suggest that the ATPase activity of Rhp51 plays an important role in both RPA displacement and Rhp51 filament formation . Therefore , we examined the ATPase activity of Rhp51 under various conditions ( Figure 6 ) . It has been reported that Rhp51 has low activity , but considerably higher than that of other Rad51 proteins in the absence of ssDNA and that this basal-level ATPase activity is very slightly enhanced in the presence of ssDNA [22 , 33] . We show here that neither of the two mediators has an effect on the intrinsic ( DNA-free ) or dsDNA-dependent ATPase activities of Rhp51 ( Figure 6A and 6C ) . However , the Swi5-Sfr1 complex stimulated the ssDNA-dependent ATPase activity of Rhp51 about 3-fold ( Figure 6B ) , consistent with a previous report [22] , but Rad22 had no effect in this respect , regardless of the presence of the Swi5-Sfr1 complex ( Figure 6B ) . We hypothesized that stimulation of the ssDNA-dependent ATPase activity of Rhp51 by Swi5-Sfr1 alters the Rhp51 filament . An RPA attack experiment supported this hypothesis ( Figure 7A–7E ) . Rhp51 filaments were allowed to form on css beads under various conditions , and RPA was then added . Proteins that remained bound to ssDNA were analyzed by SDS-PAGE . In the presence of ATP , but absence of RPA , Rhp51 was pulled down efficiently ( Figure 7A , lanes 1–3 , and Figure 3C ) . Swi5-Sfr1 had no effect on the ssDNA binding capacity of Rhp51 , as previously reported [22] . However , when RPA was added to the reaction mixture in the absence of Swi5-Sfr1 , it became detectable in the ssDNA fraction , and almost all of the Rhp51 bound to ssDNA disappeared ( Figure 7A , lane 4 ) . In contrast , when RPA was added to the reaction mixture in the presence of Swi5-Sfr1 , the Rhp51 filament became resistant to RPA attack in a Swi5-Sfr1 concentration-dependent manner ( Figure 7A , lanes 5 and 6 ) . The formation of the resistant Rhp51 filament was ATP dependent , and neither ADP nor ATPγS could replace ATP in this reaction ( Figure 7B–7D ) . Interestingly , AMP-PNP made the Rhp51 filament resistant to RPA attack , even in the absence of Swi5-Sfr1 ( Figure 7E ) , consistent with a report that human Rad51 forms more stable filaments with AMP-PNP [34 , 35] . Rad22 had little detectable effect on the formation of resistant Rhp51 filaments ( Figure 7F ) . However , large amounts of Rad22 modestly facilitated Rhp51 pull down ( Figure 7F , lane 6 ) . Unlike what was seen for coincubation with Swi5-Sfr1 ( Figure 7A ) , however , the amount of RPA bound to ssDNA was constant ( Figure 7F , lanes 4–6 , and the histogram below ) . In addition , the recovery of Rhp51 was adenine nucleotide-independent ( unpublished data ) . Therefore , Rhp51 recovered with assistance of Rad22 is not the same as the resistant Rhp51 induced by Swi5-Sfr1 . Since Rad22 binds strongly to both Rhp51 and RPA ( unpublished data , and [31 , 32] ) , and the ternary complex is formed in solution in the absence of ATP ( unpublished data ) , these results indicate that an RPA-Rad22-Rhp51 complex bound to ssDNA via RPA is pulled down . Taken together , these results suggest that Swi5-Sfr1 induces activation of the Rhp51 filament to promote strand exchange in an ATP-dependent manner . Rad22 may not be directly involved in filament activation , but rather , it may recruit Rhp51 to RPA-coated ssDNA . We measured the ATPase activity of Rhp51 using RPA-coated ssDNA as a cofactor , which more closely approximates physiological conditions . As shown in Figure 6D , Swi5-Sfr1 stimulated the ATPase activity of Rhp51 , but this stimulation was less than that observed when RPA-free ssDNA was used as a cofactor ( compare Figure 6B and 6D ) . Interestingly , the level of stimulation of the Rhp51 ATPase activity by Swi5-Sfr1 reached the level of that observed with RPA-free ssDNA when Rad22 was added to the reaction ( Figure 6D ) . This synergistic stimulation of Rhp51 ATPase activity is consistent with the coordinated action of these two mediators in DNA strand exchange . In a control experiment , we constructed and purified an Rhp51 Walker B box mutant protein ( D to N alteration; Rhp51D244N ) . The behavior of Rhp51D244N during purification by chromatography was the same as that of the wild-type protein ( unpublished data ) . The mutant did not produce any detectable levels of phosphate generated by ATP hydrolysis . Coincubation of Swi5-Sfr1 with Rhp51D244N did not increase the level of hydrolyzed phosphate ( Figure 6E ) . This result indicates that the ATPase activity stimulated by Swi5-Sfr1 is indeed that of the wild-type Rhp51 protein .
This study demonstrates an in vitro reconstitution of the central step in eukaryotic HR . Our system consists entirely of purified components , including recombinase , RPA , and the Rad22 and Swi5-Sfr1 mediators , and it reflects the genetic requirements for these components in vivo . Using this system , we present for the first time robust biochemical evidence that the two mediators function in a concerted manner to form the active Rhp51 filament . Dissection of the reaction uncovered several of the molecular details of strand exchange . First , Rad22 overcomes the inhibitory effect of RPA , but not of SSB , in strand exchange mediated by Rhp51-Swi5-Sfr1 ( Figure 1B ) . In addition , Rad22 negates the need for a strict order of addition of protein components , indicating that it coordinates strand exchange ( Figure 2 ) . However , even in the presence of Rad22 , Swi5-Sfr1 is essential for Rhp51-mediated strand exchange , highlighting the different fundamental properties of the two mediators ( Figure 1C ) . Although the molecular functions of the two mediators are distinct ( see below ) , they function synergistically to promote Rhp51 loading ( Figure 3 ) . Importantly , Rhp51 , but not Rad22 or the Swi5-Sfr1 mediator , displaces RPA from ssDNA ( Figures 4 and 5 ) . We previously showed that Swi5-Sfr1 stimulates the ssDNA-dependent ATPase activity of Rhp51 [22] . Here , we demonstrated that Rad22 does not affect the ATPase activity of Rhp51 ( Figure 6 ) . Most important , Swi5-Sfr1 renders Rhp51 resistant to RPA attack in an ATP-dependent manner ( Figure 7A ) , suggesting that Swi5-Sfr1 stabilizes the presynaptic filaments of the recombinase . Since this stabilization is ATP-dependent , Swi5-Sfr1 stimulates the ssDNA-dependent ATPase activity of Rhp51 , and the mediators stimulate strand exchange mediated by Rhp51 . Thus , these mutual relationships strongly suggest that the induced stabilization of the Rhp51 filament reflects a structural/functional alteration upon activation . In contrast , Rad22 is not involved in Rhp51 activation ( Figure 7F ) . Based on these results , we propose a model for the early step of the strand exchange reaction involving Rhp51 and the two mediators . Rad22 recruits Rhp51 to RPA-coated ssDNA . Rad22 and Swi5-Sfr1 collaborate in displacing RPA and loading Rhp51 onto ssDNA , but the displacing factor itself is Rhp51 , and displacement requires the ATP binding activity of Rhp51 . Swi5-Sfr1 activates Rhp51 recruited to RPA by Rad22 to form and stabilize/activate the presynaptic filament in an ATP-dependent manner , promoting processive strand exchange . Localization of budding yeast Rad51 to DSB sites requires Rad52 [5 , 6 , 26 , 36 , 37] . Although experimental data on whether Rhp51 focus formation is dependent on Rad22 functions are not yet available , at least to our knowledge , this is widely believed to be true as well for fission yeast , based on similarities between the two systems . However , the contribution of Rad22 to our in vitro strand exchange reaction was relatively small . In contrast , the influence of the other mediator , Swi5-Sfr1 , is much stronger than that of Rad22 . Our results indicate that substoichiometric amounts of Rad22 are optimal to overcome the RPA inhibitory effect on strand exchange ( 1 to 5∼10 ratio of Rad22 to Rhp51; see Figure 2D ) . This is similar to what has been seen for budding yeast Rad52 in strand exchange [38] . These results suggest that Rad22 may only transiently interact with the Rhp51 nucleoprotein filament . Such transient interactions likely mediate assembly of the Rhp51 filament . However , Rad22 ( and presumably Rad52 ) may have ( an ) other uncovered function ( s ) involved in the overall in vivo strand exchange reaction . Based on chromatin immunoprecipitation analyses , Wolner and coworkers suggested that Rad51 binds first , followed by Rad52 and Rad55 , to a single DSB site in budding yeast [26] . This result apparently contradicts the genetic dependency of Rad51 focus formation on Rad52 . Wolner et al . proposed that only the stable binding of Rad51 is detectable by chromatin immunoprecipitation , reflecting the assembly of a nucleoprotein filament catalytically competent for strand invasion . Indeed , Arai et al . have demonstrated that a stoichiometric complex of Rad52 with Rad51 is required for the efficient formation of D-loops via strand invasion [39] . The strand exchange system we used in this study does not include a strand invasion step , accounting for the small amounts of optimal concentration and a lower dependency on Rad22 . An apparent strong dependency on Swi5-Sfr1 may be valid only for a three-strand exchange reaction using a long DNA substrate , an assay that mimics the strand transfer reaction but which does not include strand invasion . Further study will be needed to reveal functional differences between the two mediators in D-loop formation . We propose here a two-phase activation mechanism of Rhp51 filament formation . Rhp51 is the only component with ATPase activity among the proteins used in our assay , and this activity enables the protein to bind to ssDNA in an adenine nucleotide-dependent manner . However , Rhp51 alone cannot efficiently promote strand exchange; instead , it requires Swi5-Sfr1 [22] . Thus , the first phase of activation is one in which Rhp51 is activated to bind ssDNA . Swi5-Sfr1 then further activates ATP-bound Rhp51 to make it catalytically competent for strand exchange . In the second phase of activation , the Swi5-Sfr1 mediator also renders the Rhp51 filament resistant to attack by RPA ( Figure 7A ) . The ATPase activities of Rad51 proteins from budding yeast and human cells are dependent on the presence of ssDNA [40–42] . In contrast , the Rhp51 ATPase is considerably efficient in the absence of ssDNA , and the presence of ssDNA does not enhance its activity . Binding of human Rad51 to DNA can occur in the absence of ATP , but budding yeast Rad51 requires ATP for DNA binding [43] . Rhp51 also requires adenine nucleotides for ssDNA binding ( Figure 3 ) . These observations indicate that Rad51 properties relevant to ATPase and DNA binding are not universal , and variations may be related to the apparent differences in activation mechanisms for strand exchange . Budding yeast and human Rad51 proteins can carry out a robust strand exchange reaction in the absence of a Swi5-Sfr1-type mediator in vitro . Human Rad51 is already activated for the first step because it can bind ssDNA without ATP . Once ATP is included in the reaction , human Rad51 may be further activated to the catalytically competent state . Budding yeast Rad51 requires ATP to bind DNA , implying that the first stage is similar to that of Rhp51 . Once Rad51 binds to ssDNA , its ATPase activity is stimulated , indicating that the second phase of activation readily occurs without a Swi5-Sfr1–type mediator . In other words , the basal level of activation of the first phase is higher in human Rad51 , and the second phase is higher in budding yeast Rad51 . This idea can reconcile the apparent differences among the three recombinases . Interestingly , under biochemical conditions that slow ATPase activity , such as the presence of Ca2+ , strand exchange mediated by human Rad51 is enhanced [34 , 44 , 45] . However , activation by Ca2+ holds only for human Rad51 , not for budding yeast Rad51 [44] or fission yeast Rhp51 ( unpublished data ) . The presence of Ca2+ attenuates the disassembly of Rad51 from ssDNA , resulting in stable filaments on ssDNA [45] . In contrast , Swi5-Sfr1 stimulates the Rhp51 ATPase [22] and stabilizes the filament in an ATP-dependent manner ( Figure 7 ) . We cannot explain why these two opposing effects lead to a stimulation of strand exchange , although the differences in the basal-level status of yeast and human Rad51 proteins may be a key to this issue . AMP-PNP , but not ATPγS , induces a similar stabilization of Rhp51 that is independent of Swi5-Sfr1 . Although Rhp51 binding to AMP-PNP is thought to mimic the transient ATP-bound form during ATP hydrolysis , the AMP-PNP–bound and ATP-bound Rhp51 forms are qualitatively different . The AMP-PNP–bound form of Rhp51 must be locked in a unique activated state with Swi5-Sfr1 , which presumably represents the second phase of activation , since the ATP-bound Rhp51 filament itself is not sufficient for strand exchange . Importantly , AMP-PNP cannot support processive strand exchange . Both the Swi5-Sfr1–activated ATP-bound form and the AMP-PNP–bound form are competent states that promote homologous pairing and the transient formation of a three-strand intermediate . However , hydrolysis of ATP is required for the subsequent steps of consecutive strand transfer from duplex DNA to ssDNA that result in the formation of a long heteroduplex . Swi5-Sfr1 promotes both of the states that permit homologous pairing and consecutive strand transfer by stimulating the ssDNA-dependent ATPase activity of Rhp51 . The enhancement of the Rhp51 ATPase activity by Swi5-Sfr1 is not due to an increased ADP–ATP exchange rate , but rather to an enhanced turnover rate [22] . A rapid turnover between the first and the second stages might promote efficient strand exchange . The precise mechanism by which Swi5-Sfr1 induces activation of the Rhp51 filament remains to be clarified in future studies .
S . pombe Rhp51 was purified as previously described [22] . Alternatively , Rhp51 was expressed in the Escherichia coli strain BL21-CodonPlus ( DE3 ) -RIPL carrying an Rhp51 expression plasmid derivative of pET11b ( Novagen ) . Cells were incubated at 37 °C in LB media containing ampicillin . When cell density reached an optical density at 600 nm ( OD600 ) of approximately 0 . 5 , isopropyl-ß-D-thiogalactopyranoside ( IPTG ) was added to a final concentration of 0 . 5 mM , and the culture was further incubated for 18 h at 18 °C . The cells were collected by centrifugation and resuspended in R buffer ( 20 mM Tris-HCl [pH 8 . 0] , 1 mM EDTA , 1 mM dithiothreitol [DTT] , 10% glycerol ) containing 300 mM NaCl . The cells were disrupted by sonication , and the lysate was clarified by ultracentrifugation . Proteins in the lysate were precipitated by ammonium sulfate fractionation at 40% saturation and centrifuged 35 , 000 × g for 30 min . The pellet was resuspended in P buffer ( 20 mM potassium phosphate [pH 7 . 5] , 0 . 5 mM EDTA , 10% glycerol , and 0 . 5 mM DTT ) containing 300 mM KCl and diluted by P buffer to a final concentration of KCl 50 mM before being subjected to SP Sepharose ( GE Healthcare ) chromatography . The pass-through fraction was collected and directly subjected to Q Sepharose ( GE Healthcare ) chromatography . The proteins were eluted with a linear gradient of 50 mM to 800 mM KCl in P buffer . Rhp51 was eluted at approximately 500 mM KCl . The peak fractions were diluted 5-fold with P buffer and loaded onto a HiTrap Heparin column ( GE Healthcare ) . Rhp51 was eluted at approximately 400 mM KCl in a linear gradient of 100 mM to 700 mM KCl in P buffer . The peak fractions were diluted 4-fold with P buffer and loaded onto a Resource Q column ( GE Healthcare ) . Rhp51 was then eluted at approximately 500 mM of KCl in a linear gradient of 100 mM to 600 mM KCl in P buffer . The Rhp51 preparation obtained by this procedure is indistinguishable from that obtained by a previously described method [22] . An rhp51 derivative with a mutated Walker B box ( rhp51 D244N ) was constructed by site-directed mutagenesis using a QuickChange kit ( Stratagene ) . The sequences of the primer set are 5′-CATTGTTAGTTGTCaATAGTTGTACTGCC-3′ and 5′-GGCAGTACAACTATtGACAACTAACAATG-3′ , where the lowercase letters are mutations that convert D to N . The mutant gene was subcloned into pET11b and expressed in the same manner as wild-type rhp51 , and rhp51 D244N was purified as described above . The chromatographic elution patterns of Rhp51D244N were the same as for wild-type Rhp51 . S . pombe Rad22 was also expressed in E . coli BL21-CodonPlus ( DE3 ) -RIPL carrying a Rad22 expression plasmid derivative of pET11b ( Novagen ) . Rad22 expression was induced by 0 . 2 mM IPTG at 30 °C for 3 h . The induced cell lysate was processed as described above , and the clarified lysate in R buffer containing 500 mM NaCl was precipitated by ammonium sulfate ( 30% saturation ) . The pellet was resuspended in R buffer containing 200 mM NaCl and directly loaded onto a Q Sepharose column , and Rad22 was eluted in one step with 600 mM NaCl in R buffer . Rad22 fractions were collected , diluted 3-fold with R buffer and loaded onto a HiTrap Heparin column . Rad22 was eluted at 500 mM NaCl with a linear gradient of 200 mM to 600 mM NaCl in R buffer . Peak fractions were collected and diluted 5-fold with R buffer and loaded onto a HiTrap SP column ( GE Healthcare ) . A linear gradient of 100 mM to 600 mM NaCl in R buffer allowed elution of Rad22 at approximately 300 mM NaCl . Rad22 fractions were applied to a Superdex 16/60 200 pg column ( GE Healthcare ) and developed in R buffer containing 1 M NaCl . Rad22 was eluted in the void fractions and dialyzed against R buffer containing 100 mM NaCl . The dialyzed sample was applied to a Resource Q column . A linear gradient of 100 mM to 700 mM NaCl in R buffer allowed elution of Rad22 at approximately 250 mM NaCl . Protein concentrations were determined by measuring absorbance at 280 nm . The following extinction coefficients ( ε280 ) were used: 1 . 86 × 104 M−1 cm−1 for Rhp51 and Rhp51D244N , 2 . 93 × 104 M−1 cm−1 for Rad22 , 1 . 44 × 104 M−1 cm−1 for the Swi5-Sfr1 complex , and 9 . 89 × 104 M−1 cm−1 for RPA . The purification of S . pombe RPA and the Swi5-Sfr1 complex was previously described [22] . E . coli SSB was purchased from Sigma-Aldrich . Procedures for the standard reaction protocols were essentially the same as previously described [22] , with the exception of Rad22 addition . Briefly , the reactions ( 10 μl ) contained the following components: 10 μM øX174 ssDNA ( css ) , 10 μM ApaLI-linearized øX174 dsDNA ( lds ) ( New England Biolabs ) , 5 μM Rhp51 , 0 . 5 μM Rad22 , 0 . 5 μM Swi5-Sfr1 , 1 μM RPA , 2 mM ATP , and an ATP regeneration system ( 8 mM creatine phosphate and 8 U/ml creatine kinase ) in buffer F ( 25 mM Tris-OAc [pH 7 . 5] , 1 mM DTT , 5% glycerol , 3 mM Mg ( OAc ) 2 , 100 mM KCl ) . When replacing RPA , SSB was used at 2 μM . Reactions were incubated for 120 min at 37 °C and terminated by adding 1 . 2 μl of a stop solution containing 8% SDS and 0 . 6 μl 20 mg/ml Proteinase K , with a final incubation for 30 min at 37 °C . The products were analyzed by 1% agarose gel electrophoresis as previously described [22] . Immobilized øX174 ssDNA beads ( css beads ) were prepared by annealing a 5′-biotinylated 100-mer oligonucleotide to øX174 ssDNA and capturing the fragments with Dynabeads M-280 Streptavidin ( Invitrogen ) , as previously described [22 , 46] . To determine the amount of ssDNA immobilized on the beads , an aliquot of the css-beads suspension was denatured by 0 . 1 M NaOH , and the concentration of the released ssDNA was determined by measuring at A260 . About 80% of css was immobilized on the beads . In a standard assay , a bead suspension ( 2 μl ) containing 33 ng of css was mixed with the indicated amounts of each protein in the presence or absence of nucleotide in 10 μl of buffer F containing 0 . 01% ( v/v ) NP-40 for 30 min at 37 °C , with constant tapping . The beads were captured with a Magnet Stand Dynal MPC ( Invitrogen ) , and the supernatants ( the unbound fraction ) and beads ( bound fraction ) were separated . The bead-bound proteins were eluted with 15 μl of SDS-PAGE loading buffer , and 12 μl of the eluates was analyzed by SDS-PAGE . A 5-fold concentration of SDS loading buffer ( 3 μl ) was added to the supernatants , and 12 μl of each sample was analyzed by SDS-PAGE . The gels were stained with BioSafe CBB G-250 ( Bio-Rad ) , gel images were captured by LAS-4000 ( Fuji Photo Film ) , and protein band densities were quantified with Multi Gauge ( Fuji Photo Film ) to determine the amounts of bound and unbound proteins . The procedures were conducted essentially as previously described [22] . Reaction mixtures ( 13 . 5 μl ) contained 5 μM Rhp51 in buffer F . In some assays , 1 μM RPA , 0 . 5 μM Swi5-Sfr1 , 0 . 5 μM Rad22 , 10 μM øX174 ssDNA , or 10μM ApaL1-linearized øX174 dsDNA were added , as indicated . The reactions were started by adding 1 . 5 μl of a mixture of [γ-32P]ATP and cold ATP ( final concentration , 2 mM ) at 37 °C . Aliquots ( 2 μl ) were taken at various time points and immediately mixed with 4 μl of stop solution ( 0 . 5 M EDTA ) . Samples ( 1 μl ) were subjected to thin layer chromatography , as previously described [22] . The amounts of 32Pi and [γ-32P]ATP in each spot were determined using a phosphoimager ( Fuji BAS2500 ) .
|
Homologous recombination promotes genetic diversity in the next generation and serves as a driving force for evolution . It also provides efficient machinery for repairing DNA damage such as double-strand breaks . Homologous recombination involves DNA exchange between homologous chromosomes , which is mediated by evolutionarily conserved proteins called recombinases . It is thought that a recombinase binds to single-stranded DNA ( ssDNA ) to form a nucleoprotein filament called the presynaptic filament , and that this higher order structure engages in a search for homologous DNA sequences . Once a homologous duplex is found , the presynaptic filament initiates strand exchange . However , when ssDNA regions are created , they are immediately covered by replication protein A ( RPA ) , thereby inhibiting recombinase filament formation even under conditions in which homologous recombination is appropriate . Previous studies suggested that mediator proteins help load recombinases onto ssDNA , and further studies showed that at least two mediators function together in a single recombination pathway . How these mediators coordinate recombinase loading has been unclear . We have addressed this question by reconstituting an in vitro strand exchange reaction with purified proteins including a fission yeast recombinase , Rhp51 , two mediators , Rad22 and the Swi5-Sfr1 complex , and RPA . Our results indicate that Rad22 orchestrates the loading of Rhp51 onto RPA-coated ssDNA by acting as a scaffold for nucleating the recombinase filament , whereas the other mediator , Swi5-Sfr1 , stabilizes and activates the filament .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"molecular",
"biology"
] |
2008
|
Reconstitution of DNA Strand Exchange Mediated by Rhp51 Recombinase and Two Mediators
|
Mother-to-child-transmission ( MTCT ) of human T-cell lymphotropic virus type-1 ( HTLV-1 ) contributes disproportionately to the burden of HTLV-1 associated diseases . All preventive measures to avoid MTCT rely on the identification of infected mothers . However , the impact of pregnancy on HTLV-1 diagnosis has not been clearly assessed . Paired samples from 21 HTLV-1 infected women taken during pregnancy and while not pregnant were analysed by CMIA and PCR . The signal-to-cut-off values ( S/CO ) were higher during pregnancy than in the paired non-pregnant samples . HTLV-1 proviral load did not alter significantly by pregnant state . S/CO positively correlated with HTLV proviral load . Pregnancy does not impair the diagnosis of HTLV-1/2 , by either immunological ( CMIA ) or molecular ( qPCR/nPCR ) tests .
At least 5–10 million individuals are living with human T-cell lymphotropic virus type 1 ( HTLV-1 ) in the world [1] . This virus can be transmitted through unprotected sexual intercourse , by exposure to infected lymphocytes in blood or tissue and by mother-to-child transmission ( MTCT ) , mainly by breastfeeding . The latter , responsible for maintaining infection in successive generations , is associated with a disproportionately high risk of adult T cell leukaemia/lymphoma ( ATL ) [2] . Infection in early life is also linked to infective dermatitis in children as well as juvenile and adult cases of disabling HTLV-1-associated myelopathy/tropical spastic paraparesis ( HAM/TSP ) . Treatment for these high morbidity diseases remains limited and once infection has occurred disease cannot be prevented . Thus , avoidance of transmission is essential . Blood donor screening has been implemented in many countries , however Japan alone has a national antenatal screening programme . Prevention of HTLV-1 MTCT relies on the identification of infected mothers prior to delivery . Diagnosis of HTLV-1/2 infection is based on screening ( anti-HTLV antibody detection by enzyme linked immunoassay [ELISA] or chemiluminescent microparticle immunoassay [CMIA] ) followed by confirmatory tests ( HTLV gag and env antibodies by Western Blot [WB] and/or HTLV DNA amplification and detection by polymerase chain reaction [PCR] ) [3] . The current commercial assays cite high sensitivity and specificity . One reason given in the UK national screening committee’s recent decision not to implement HTLV-1 antenatal screening was a lack of data on the reliability of HTLV-1 diagnostics tests during pregnancy[4] . Conversely , studies of pregnant women are considered a more reliable indicator of HTLV-1 seroprevalence in the general population with 45 such studies cited in the ECDC report on the geographical distribution of areas with a high prevalence of HTLV-1 infection [5] , with data from Europe showing consistently higher seroprevalence in the antenatal population compared with blood donors [3 , 6] . To address whether pregnancy impacts on the serological and molecular diagnosis of HTLV-1 infection the results from testing samples taken from women when pregnant were compared with paired samples from the same women when not pregnant .
This study was conducted under the auspices of the communicable diseases research tissue bank and approved by the NRES Committee South Central–Oxford C ( 15/SC/0089 ) . All participants signed a written informed consent . Twenty-one women attending the National Centre for Human Retrovirology , St . Mary’s Hospital , London , with either HTLV-1 or -2 infection donated blood samples . HTLV infection had in all cases been confirmed by Western Blot ( Genelabs HTLV 2 . 4 ) in accordance with the manufacturer’s instructions , 19 had HTLV-1 and two HTLV-2 infection . Paired sera obtained during a pregnancy and whilst not pregnant were analysed according to the manufacturer’s instructions for anti-HTLV-1/2 antibodies detection using Abbott Architect rHTLV-I/II platform , a fully automated third generation CMIA that includes HTLV-1/2 recombinant proteins . The optical density ( OD ) of each sample compared to the negative/reactive cut-off value for each time point was recorded . HTLV-1 proviral load ( PVL ) in peripheral blood mononuclear cells ( PBMCs ) was determined by quantitative real time PCR ( qPCR ) targeting HTLV tax gene and human betaglobin gene as previously reported [7] . In those samples with an undetectable PVL by qPCR a nested PCR ( nPCR ) was used to detect and type HTLV DNA [8] . D'Agostino & Pearson omnibus normality test was used to verify whether the results had a Gaussian distribution . Paired t test and Wilcoxon matched pairs test were used to compare groups ( pregnant and not pregnant ) of parametric and non-parametric data , respectively . Spearman test was used to verify if there was a correlation between S/CO values and PVL . The clinical status , the trimester of testing and the serological and molecular results are presented in Table 1 . Virtually all samples from non-pregnant time point were collected at least one year before delivery ( varying from 1 year up to 10 years ) . Samples from patients number 1 and 9 were collected after delivery ( 7 days and 2 . 5 years after delivery , respectively ) All samples were strongly positive by CMIA with a higher mean S/CO ratio 121 . 7 ( SD 48 . 39 ) observed during pregnancy compared with the not pregnant time point 110 . 4 ( SD 44 . 08 ) ; ( p = 0 . 0209 ) . The range of S/CO values was 51 . 1–210 . 3 during pregnancy and 41 . 46–198 . 1 in the samples collected when the women were not pregnant ( Fig 1 ) . The S/CO ratio was higher in 15/21 ( 71 . 4% ) women during their pregnancy than in the paired non-pregnant sample . Third trimester serology did not differ significantly from the early trimesters . There was no significant difference in HTLV PVL in samples collected during pregnancy and while not pregnant ( Median PVL ( Interquartile range ) HTLV-1 DNA copies per 100 PBMCs: Not pregnant 0 . 6 ( 3 . 85 ) ; pregnant: 0 . 6 ( 7 . 65 ) ; p = 0 . 8 ) . The PVL was quantifiable in 17 women in whom it was lower during pregnancy in seven , the same in four and higher in six . Four women ( 3 HTLV-1 and 1 HTLV-2 ) did not have quantifiable HTLV PVL DNA in either sample of which: two women had HTLV-1 provirus DNA amplified and detected by nested PCR only in the sample from pregnancy; one did not have detectable HTLV-1 DNA in either sample , whilst in the patient with HTLV-2 proviral DNA was detected in 1/4 replicates , only in the sample obtained whilst pregnant . HTLV PVL positively correlated with the antibody titre ( S/CO ) in both settings ( Fig 1 ) .
Pregnancy is a very specific physiological state in which the woman must tolerate the foetus by undergoing many immune and morphophysiological changes [9] . Immunoglobulin concentration in the serum decreases during pregnancy [10–12] and lower levels of antibody production are observed following influenza immunisation [13] . Haemodilution also results in physiological decrease in lymphocyte counts . Therefore , pregnancy might impact antibody production and could modulate the number of infected lymphocytes impacting HTLV proviral load . This could , in turn , hamper the diagnosis of pathogens by both immunological and molecular tests . Immunological assays to detect anti-HTLV-1 antibodies have been demonstrated by their respective manufacturers to have excellent sensitivity ( usually considered 100% ) . The Abbott Architect rHTLV-I/II platform is a fully automated third generation assay with high sensitivity and specificity for HTLV-1/2 diagnosis in different clinical settings [14–16] . According to manufacturers , the interpretation of reactivity in Abbott Architect rHTLV-I/II is any signal-to-cut-off ( S/CO ) ratio of 1 or above . In the present study , this CMIA detected HTLV-1/2 antibodies in each of the women whilst pregnant . Furthermore , there was no evidence that anti-HTLV-1 antibody reactivity were lower in pregnancy . Indeed , in paired samples the S/CO values were overall higher during pregnancy . In an earlier study of 12 , 250 blood samples evaluated by CMIA a strong correlation with a S/CO above 20 and subsequent confirmation of HTLV-1/2 infection was observed , whereas no HTLV-1/2 infection was confirmed in patients where the S/CO was less than 4 [17] . The sera collected during pregnancy were not only reactive in the CMIA but all had high S/CO values with the lowest being 41 . 5 . This points against a decrease in sensitivity for HTLV-1 diagnosis during pregnancy . Once HTLV-1/2 reactivity has been detected the importance of further tests both to confirm and type HTLV-1/2 infection is well documented . Whilst more specific immunological tests such as WB and Immunofluorescence have been used as confirmatory tests for HTLV-1 infection ( 3 ) molecular tests are sometimes required especially for investigating indeterminate serology . In a recent study from Southern Brazil of 643 pregnant women Medeiros et al ( 18 ) reported that 0 . 6% tested positive in CMIA , of which 50% were confirmed by PCR . They found that negative-PCR samples had low S/CO values ( 1 . 3 and 2 . 5 ) in the Architect assay whereas the PCR-positive samples had high S/CO values ( 78 . 3 and 137 . 5 ) consistent with previously reported UK findings [18] . In our study , all sera had high S/CO values even those from the four patients with undetectable HTLV-1 and HTLV-2 DNA by qPCR . All patients had HTLV infection confirmed by WB and the one with a negative PCR was an asymptomatic carrier . From a diagnostic perspective it is important to realise that a proportion of carriers , HTLV elite controllers , have undetectable HTLV-1/2 DNA . However , from a transmission perspective we can consider these women to have low risk of HTLV-1 MTCT . In a Japanese cohort HTLV-1 PVL was stable during pregnancy , with a slight increase after birth [19] . In our study , there was no statistically significant difference between samples collected during pregnancy and while not pregnant . The results in both CMIA and PCR were concordant in the paired samples . All four undetectable results by qPCR were undetectable in both settings . Thus , the risk of MTCT can be discussed prior to conception . Strong correlation between antibody reactivity and PVL in blood was already described among pregnant women at delivery [20] and in non-pregnant HTLV-1 carriers [21] . The present study observes modest correlation in both settings . High antibody reactivity was assumed to be a risk factor for HTLV-1 MTCT . However , further studies showed that the association was due to the strong correlation among PVL and antibodies [20] . High PVL was reported as an independent risk factor associated with MTCT . The protective role of these antibodies ( if any ) and if they are transferred to the new-born need to be evaluated . In conclusion , this study shows that pregnancy does not impair the diagnosis of HTLV-1/2 , by either immunological ( CMIA ) or molecular ( qPCR/nPCR ) tests . Therefore , currently available tests can be used for antenatal screening and for confirmation of HTLV-1/2 infection allowing women to make informed decisions particularly in regard to infant feeding and onward transmission to their offspring .
|
Human T-cell lymphotropic virus ( HTLV ) can be transmitted from mother to child , mainly by breastfeeding . All preventive measures to avoid HTLV mother to child transmission depend on the identification of infected pregnant women . HTLV diagnosis is based on serological screening tests , confirmed by serology and/or molecular assays . The aim of the study was to verify whether pregnancy adversely impacts the identification of HTLV infection . Using paired samples from 21 women living with HTLV-1/2 obtained during pregnancy and while not pregnant we demonstrate that both serology and molecular assays perform equally well in both settings and can be use for the diagnosis of HTLV infection during pregnancy .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Discussion"
] |
[
"blood",
"cells",
"medicine",
"and",
"health",
"sciences",
"immune",
"physiology",
"immune",
"cells",
"pathology",
"and",
"laboratory",
"medicine",
"maternal",
"health",
"obstetrics",
"and",
"gynecology",
"enzyme-linked",
"immunoassays",
"pathogens",
"immunology",
"microbiology",
"retroviruses",
"viruses",
"women's",
"health",
"rna",
"viruses",
"sexually",
"transmitted",
"diseases",
"pregnancy",
"molecular",
"biology",
"techniques",
"antibodies",
"immunologic",
"techniques",
"research",
"and",
"analysis",
"methods",
"immune",
"system",
"proteins",
"infectious",
"diseases",
"white",
"blood",
"cells",
"serology",
"artificial",
"gene",
"amplification",
"and",
"extension",
"animal",
"cells",
"proteins",
"medical",
"microbiology",
"htlv-1",
"serodiagnosis",
"microbial",
"pathogens",
"t",
"cells",
"immunoassays",
"molecular",
"biology",
"biochemistry",
"diagnostic",
"medicine",
"cell",
"biology",
"polymerase",
"chain",
"reaction",
"viral",
"pathogens",
"physiology",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"organisms"
] |
2019
|
Pregnancy does not adversely impact diagnostic tests for HTLV-1/2 infection
|
Buruli ulcer ( BU ) caused by Mycobacterium ulcerans is a devastating skin disease , occurring mainly in remote West African communities with poor access to health care . Early case detection and subsequent antibiotic treatment are essential to counteract the progression of the characteristic chronic ulcerative lesions . Since the accuracy of clinical BU diagnosis is limited , laboratory reconfirmation is crucial . However , currently available diagnostic techniques with sufficient sensitivity and specificity require infrastructure and resources only accessible at a few reference centres in the African endemic countries . Hence , the development of a simple , rapid , sensitive and specific point-of-care diagnostic tool is one of the major research priorities for BU . In this study , we have identified a previously unknown M . ulcerans protein , MUL_3720 , as a promising target for antigen capture-based detection assays . We show that MUL_3720 is highly expressed by M . ulcerans and has no orthologs in other prevalent pathogenic mycobacteria . We generated a panel of anti-MUL_3720 antibodies and used them to confirm a cell wall location for MUL_3720 . These antibodies could also specifically detect M . ulcerans in infected human tissue samples as well as in lysates of infected mouse footpads . A bacterial 2-hybrid screen suggested a potential role for MUL_3720 in cell wall biosynthesis pathways . Finally , we demonstrate that a combination of MUL_3720 specific antibody reagents in a sandwich-ELISA format has sufficient sensitivity to make them suitable for the development of antigen capture-based diagnostic tests for BU .
Buruli ulcer ( BU ) is a neglected mycobacterial skin disease , reported from tropical and subtropical countries world-wide with highest incidence rates in Western Africa [1] . Populations in rural areas with limited access to health facilities are most affected and often seek medical advice at late disease stages [2] . Advances in the clinical management of BU have shifted options for treatment from surgical resection to combination antibiotic therapy [1] . While PCR analysis targeting the insertion sequence IS2404 has evolved into the gold standard for laboratory diagnosis of BU , this test is only available at a few reference centres . Therefore , the diagnosis of BU is currently often based on clinical findings and antibiotic therapy is started before laboratory diagnostic results can be obtained . BU has a wide range of clinical manifestations including non-ulcerative forms such as subcutaneous nodules or papules , plaques and oedema , which may progress to chronic ulcerative lesions . Due to this diversity of disease presentations the accuracy of clinical diagnosis is limited [1 , 3–5] and thus a significant proportion of patients reporting with skin lesions may not receive adequate treatment . This includes cases of cutaneous tuberculosis which may be misdiagnosed as BU and thus receive the recommended eight week course of Streptomycin/Rifampicin combination chemotherapy for BU [5] , which is much too short for the treatment of tuberculosis . As for IS2404 PCR , two of the other three currently applied methods for laboratory reconfirmation of BU—histopathology and cultivation of the extremely slow-growing mycobacteria—equally require expensive equipment and expertise [4 , 6–8] not accessible at peripheral health facilities . The only available point-of-care diagnostic test , direct-smear examination by microscopy for the detection of acid fast bacilli ( AFB ) , has limited sensitivity and specificity [6] . Hence , one of the major research priorities for BU is the development of a fast , low-tech , sensitive and specific point-of-care diagnostic test , which can be directly implemented at peripheral health centres . The development of a specific point-of-care diagnostic test for the detection of M . ulcerans is complicated by the broad antigenic cross-reactivity among the various mycobacterial species . Serological approaches targeting the few M . ulcerans-specific antigens identified , turned out to be not suitable for differentiation between BU patients and exposed control individuals , as both groups may or may not exhibit serum IgG titers against these antigens [9–11] . In recent years , point-of-care tests in the form of antigen capture assays have successfully been developed for tropical infectious diseases [12] . Extensive studies focussing on rapid diagnostic tests for malaria [13–17] have paved the way for the development of antigen capture based assays for other diseases such as dengue fever [18 , 19] , hepatitis C [20 , 21] or visceral leishmaniasis [22] to name but a few . In the present work we aimed at the identification of targets for the development of an antigen capture test for the diagnosis of M . ulcerans infection by using a proteomics approach .
Ethical clearance for the analysis of clinical specimens was obtained from the Cameroon National Ethics Committee ( N°172/CNE/SE/201 ) and the Ethics Committee of Basel ( EKBB , reference no . 53/11 ) . Immunization of mice for the generation of monoclonal antibodies was performed in strict accordance with the rules and regulations for the protection of animal rights ( “Tierschutzverordnung” ) of the Swiss “Bundesamt für Veterinärwesen” . All animal infection experiments performed were approved by the animal welfare committee of the Canton of Vaud ( authorization number 2261 ) and were conducted in compliance with the Swiss animal protection law under BSL-3 conditions . In this study we analyzed M . ulcerans isolates from Ghana ( NM20/02 ) , Côte d’Ivoire ( ITM 940511 ) , Togo ( ITM 970680 ) , China ( ITM 98912 ) , Japan ( ITM 8756 ) and Australia ( JS5147 ) as well as additional mycobacterial strains including M . abscessus ( ATCC 19977 ) , M . avium ( MAC101 ) , M . chelonae ( DSM 43804 ) , M . fortuitum ( ATCC 49403 ) , M . gordonae ( Pasteur 14021 . 001 ) , M . haemophilum ( ATCC 29548 ) , M . intracellulare ( clinical isolate ) , M . kansasii ( NCTC 10268 ) , M . lentiflavum ( clinical isolate ) , M . malmoense ( NCTC 11298 ) , M . marinum ( ATCC 927 ) , M . scrofulaceum ( Pasteur 14022 . 0031 ) , M . simiae ( clinical isolate ) M . smegmatis ( Pasteur 14133 . 0001 ) , M . terrae ( clinical isolate ) , M . xenopi , M . bovis ( ATCC 35734 ) and M . tuberculosis ( Pasteur 14001 . 0001 ) . M . ulcerans strains were grown in BacT/Alert culture bottles supplemented with enrichment medium according to the manufacturer’s protocol ( bioMérieux ) . For the preparation of M . ulcerans protein lysates , bacteria ( 5 ml of culture , OD600~1 ) were washed in PBS , heat-inactivated at 95°C for 35 min , centrifuged at 10′000 × g for 10 min and resuspended in 400 μl lysis buffer ( PBS containing 5% SDS , 1 mM phenylmethylsulfonyl fluoride ( PMSF ) and a protease inhibitor cocktail ( complete mini , Roche ) ) . The mix was transferred to lysing tubes ( Precellys ) and homogenized using a mechanical bead beater device ( Precellys 24 , Bertin Technologies ) twice at 6′800 rpm for 30 s . Beads and non-lysed cells were removed by centrifugation at 10′000 × g for 10 min . The preparation of lysates of other mycobacterial species was described previously [9] . 90 μg of trichloroacetic acid ( TCA ) precipitated M . ulcerans ( NM20/02 ) protein lysate was resuspended in rehydration buffer ( 8 M urea , 2% 3-[ ( 3-Cholamidopropyl ) -dimethylammonio]-1-propanesulfonate ( CHAPS ) , 0 . 5% ( v/v ) ZOOM Carrier Ampholytes ( Invitrogen ) , 0 . 002% bromophenol blue and 0 . 4% dithioerythritol ( DTE ) ) . The mix was incubated with a 3–10 pH gradient strip ( ZOOM Strip; Invitrogen ) over night ( ON ) at room temperature ( RT ) . First-dimension isoelectric focusing ( IEF ) was performed on a ZOOM IPG runner ( Invitrogen ) using a step voltage protocol ( 175 V for 15 min , 175–2000 V for 45 min , 2000 V for 2 h ) . After IEF , the strips were incubated for 15 min with equilibration buffer ( 6 M urea , 50 mM Tris pH 8 . 8 , 30% glycerol , 2% SDS , 30 mM DTE ) followed by a 15 min incubation period with alkylating solution ( 6 M urea , 50 mM Tris ( pH 8 . 8 ) , 30% glycerol , 2% SDS , 0 . 23 M iodacetamide ) . Second-dimension gel electrophoresis was performed at 200 V for 35 min using a 10% NuPAGE Novex Bis-Tris ZOOM Gel ( Invitrogen ) . The gel was stained with Coomassie blue ( Invitrogen ) . All Coomassie stained protein spots were selected for mass spectrometry analysis . Spots were excised from the 2D gel , placed in a low-binding microcentrifuge tube and destained in 0 . 1 M ammonium bicarbonate / 30% acetonitrile at 30°C . Gel spots were dried in a SpeedVac concentrator and digested with 4 μl of 10 μg/ml trypsin ( trypsin porcine , Roche Applied Science ) ON at 37°C . Peptides were extracted from gel pieces with 4 μl of 0 . 3% trifluoroacetic adic ( TFA ) / 50% acetonitrile . The samples were desalted and concentrated using ZipTipC18 tips ( Millipore ) . Eluted peptides were loaded onto a MALDI target . MS analysis was performed using a MALDI-TOF mass spectrometer ( Bruker ultraflex III TOF/TOF , Bruker Daltonics Inc . ) in the reflector mode . 1 μl of tryptic digest and 1 μl of matrix ( 5 mg/ml α-cyano-4-hydroxycinnamic acid , 50% acetonitrile , 0 . 1% TFA ) were spotted onto a MALDI target ( MTP AnchorChip 600/384 , Bruker Daltonics ) and allowed to co-crystallize at room temperature . Data were processed using FlexAnalysis software ( Bruker Daltonics flexAnalysis 2 . 4 ) . Spectra were smoothed ( Sawitzgy Golay algorithm , 0 . 2 m/z width , 1 cycle ) , baseline subtracted ( median algorithm , 0 . 8 flatness ) and calibrated using trypsin autocleavage or internal standard peptide mass peaks . A monoisotopic peak list was generated from the spectrum using SNAP algorithm and analyzed with BioTools ( Bruker Daltonics BioTools 3 . 0 ) . Peptide mass fingerprinting searches were performed using the Aldente search engine on the Expasy server . The full length MUL_3720 ( aa 1–207 ) and a truncated version of this protein lacking the lectin domain ( aa 115–207 ) were recombinantly expressed in Escherichia coli BL21 Star ( DE3 , Invitrogen ) as N-terminal hexahistidin-tagged fusion proteins . Briefly , PCR was performed on a pUC57 vector containing the DNA sequence of MUL_3720 generated by gene synthesis ( Genscript ) , including NdeI and NotI restriction sites . The amplified sequences were inserted into a TOPO-TA cloning vector using the TOPO Cloning Kit and introduced into E . coli ( Top 10 , Invitrogen ) . The vector was digested with NdeI and NotI ( New England Biolabs ) and the sequence was ligated into a pET28a expression vector using the Rapid DNA Ligation Kit ( Roche ) . E . coli BL21 Star ( DE3 , Invitrogen ) were grown in Luria-Bertani ( LB ) medium until an OD600 of ~0 . 5 . Protein expression was induced by addition of isopropyl thiogalactoside ( IPTG ) to a final concentration of 1 mM and subsequent incubation for 3 h at 37°C . Bacteria were lysed by sonication and His-tagged proteins were purified by nickel-nitrilotriacetic acid ( Ni-NTA ) chromatography . MUL_3720 was amplified from genomic M . ulcerans DNA and cloned into TOPO vector using NdeI and ScaI restriction sites . Electrocompetent E . coli TOP10 cells ( Invitrogen ) were transformed with TOPO::MUL_3720 vectors and spread onto LB-Ampicillin ( 50 μg/ml ) agar . Plasmid DNA of the mutant colonies was prepared and inserts with correct size and sequence were excised from TOPO by NdeI/ScaI and ligated into the mycobacterial vector pSD5 . Chemically competent E . coli TOP10 were transformed with pSD5::MUL_3720 and grown on LB-Kanamycin ( 50 μg/ml ) plates . Plasmid DNA was prepared and the presence of the insert was confirmed . M . ulcerans strain NM20/02 was grown in BacT bottles ( bioMérieux ) containing enrichment medium ( bioMérieux ) . Bacteria were harvested and washed twice with 10% glycerol or distilled water . Competent M . ulcerans bacteria were electroporated ( 2 . 5 kV , 1000 Ohm , 25 μF ) with varying amounts ( 50–1000 ng ) of DNA , transferred to MGIT-OADC medium ( BD ) and grown under non-selective conditions for 36 hours at 30°C . After the recovery phase , bacteria were spread on 7H10-Kanamycin ( 25 μg/ml ) agar and incubated for several months at 30°C . Colonies were picked and regrown on selective agar and in BacT bottles ( bioMérieux ) in order to prepare lysates and stocks . For the preparation of mAbs , mice were immunized two times intraperitoneally with 40 μg of recombinant full length MUL_3720 ( aa 1–207 ) emulsified in Immune Easy adjuvant ( Qiagen ) . Two weeks after the second immunization serum antibody titres against MUL_3720 ( aa 1–207 ) as well as against the truncated MUL_3720 ( aa 115–207 ) were determined by ELISA . Based on these results one BALB/c mouse was selected to receive a final intraperitoneal injection of 40 μg of recombinant MUL_3720 ( aa 1–207 ) without adjuvant . Three days after this last booster dose , hybridoma cell lines were generated as described previously [23] . Briefly , the spleen of the selected mouse was removed and the spleen cells were fused with mouse myeloma cells ( PAI cells ) . After a few days , cell culture supernatants were tested for the presence of anti-MUL_3720 ( aa 1–207 ) as well as anti-MUL_3720 ( aa 115–207 ) antibodies . Positive cell lines were cloned by limiting dilution and expanded . MAbs were purified using HiTrap rProtein A column ( Amersham Biosciences ) . Two individual fusion experiments resulted in 24 and 17 MUL_3720-ELISA positive B-cell hybridoma cell lines , respectively . Of these , a total of 5 B-cell hybridoma cell lines ( JD3 . 1 , JD3 . 2 , JD3 . 3 , JD3 . 4 , JD3 . 6 , JD3 . 7 ) were successfully cloned and expanded for antibody production . Rabbit polyclonal antibodies were generated and affinity purified by Eurogentech . New Zealand white Rabbits were injected intramuscularly with 20 μg recombinant MUL_3720 ( aa 1–27 ) with Sigma Adjuvant System ( SZ3398 ) or Imject Alum ( SZ3403 ) on day 0 , 14 , 28 and 56 . Total IgG was purified from antiserum collected on day 66 by protein A affinity chromatography . 96-well Immulon microtiter plates ( Thermo Scientific ) were coated with 1 μg recombinant MUL_3720 ( aa 1–207 ) or MUL_3720 ( aa 115–207 ) per well in 100 μl PBS and incubated ON at 4°C . Plates were washed three times with washing buffer ( 2 . 5% Tween 20 in dH2O ) and blocked with 5% non-fat dry milk in PBS containing 0 . 1% Tween for 1 h at 37°C . After washing as described above , 100 μl of the primary antibody ( mAbs or hybridoma supernatant ) was added and incubated for 2 h at 37°C . Following an additional washing step , 100 μl of 1:30′000 diluted goat anti-mouse IgG ( γ-chain specific ) antibodies coupled to alkaline phosphatase ( SouthernBiotech ) was added to each well and incubated for 1 h at 37°C . Plates were washed and 100 μl/well of phosphatase substrate solution ( 1 mg/ml p-Nitrophenyl phosphate in substrate buffer ) was added and incubated for 1 h at 37°C . Absorbance at 405 nm was measured with a microplate reader ( Tecan Sunrise ) . 2 μg of mycobacterial protein lysates per lane were separated on NuPAGE Novex 4–12% Bis-Tris ZOOM Gels ( Invitrogen ) using NuPAGE MES SDS Running Buffer ( Invitrogen ) under reducing conditions . After electrophoresis proteins were transferred onto nitrocellulose membranes using an iBlot gel transfer device ( Invitrogen ) . Membranes were blocked with blocking buffer ( 5% non-fat dry milk in PBS ) ON at 4°C . Membranes were then incubated in blocking buffer containing anti-MUL_3720 IgG ( mouse mAbs JD3 . 2 , JD3 . 4 or rabbit polyclonal IgG SZ3398 ) or mouse mAb DD3 . 7 ( specific for a conserved mycobacterial protein ) serving as loading control for 1 h at RT . After washing , membranes were incubated with secondary goat anti-mouse IgG ( γ-chain specific ) ( HRP , SouthernBiotech ) or goat anti-rabbit IgG ( Fc fragment specific ) ( HRP , Milan ) for 45 min at RT . After washing , bands were visualized by chemiluminescence using the ECL Western Blotting substrate ( Pierce ) . Immunohistochemical analysis was performed on tissue or punch biopsies from different IS2404 qPCR reconfirmed patients . Tissue or punch biopsies of BU patients were removed aseptically and immediately fixed in 10% neutral buffered formalin for 24 hours . Afterwards the tissue was embedded into paraffin , cut into 5 μm thin sections and transferred onto microscopy glass slides . Immunohistochemical staining of the sections was performed after deparaffinisation , rehydration and antigen retrieval with citrate-pretreatment according to standard protocols [24] . Inactivation of endogenous peroxidase as well as prevention of unspecific binding was achieved by incubation in PBS containing 0 . 3% hydrogen peroxide and 1 . 5% horse serum for 20 min . Primary anti-MUL_3720 IgG was diluted in PBS containing 0 . 1% Tween-20 and added to the slides for 1 h at RT or ON at 4°C . After incubation with biotin-conjugated horse anti-mouse IgG , slides were stained using the Vector ABC and NovaRED system . Sections were counterstained with haematoxylin . JD3 . 4 and JD3 . 2 showed a comparable staining in intensity , specificity and sensitivity . JD3 . 2 gave a slightly lower unspecific background staining of the surrounding tissue and was used for IHC analysis . IFA was carried out as described previously [25] . Briefly , a pellet of M . ulcerans bacteria ( OD600~0 . 6 ) was resuspended in 1 . 5% low-melting agarose ( BioWhittaker Lonza , Basel Switzerland ) and transferred to cryomodules ( Applied BioSystems ) . Agarose blocks were embedded into paraffin , cut in 3 μm sections and transferred onto microscopy glass slides ( Thermo Scientific ) . Bacteria were stained with mAb JD3 . 2 and Alexa fluor488 ( Invitrogen ) conjugated goat anti-mouse IgG and mounted in ProLong Gold anti-fade reagent containing 4′ , 6-Diamidino-2-phenylindole ( DAPI; Invitrogen ) . The system described for investigating protein interactions by the functional reconstitution of a murine dehydrofolate reductase domain in M . tuberculosis [26] was modified here for use in M . ulcerans . N-terminal and C-terminal fusions of the bait domains to full length MUL_3720 were constructed using pUAB400 or pUAB200 . Cloning was facilitated by the MfeI and ClaI restriction sites in the pUAB multiple cloning sites . All cloning was performed using E . coli DH10B and confirmed by Sanger sequencing . Plasmids were extracted from E . coli using mini-prep columns ( Qiagen ) and plasmid DNA was used to transform M . smegmatis MC2155 by electroporation as previously described [27] . M . ulcerans Agy99 genomic DNA libraries were prepared by partial AciI digestion . Digested DNA between 500 bp and 3 Kbp was purified using a gel purification kit ( Qiagen ) , ligated into ClaI digested pUAB300 and used to transform E . coli DH10B . A number of colonies were randomly selected for PCR using primers F102 ( 5′-agaaccaccacgaggagctcat-3′ ) and R102 ( 5′-tgatgcctggcagtcgatcgta-3′ ) that flank the multiple cloning site on the vector to check for insertions containing inserts within the desired size range [26] . Approximately 2 × 105 clones were subsequently collected and cultured in LB ON . Plasmid DNA maxi-preps were performed on ON cultures according to the manufacturer’s instructions ( Sigma-Aldrich ) . Bacteria co-transformed with plasmids containing interacting , complementary mDHFR fragments were selected on 7H11 kanamycin ( 25 μg/ml ) and hygromycin ( 50 μg/ml ) plates . Colonies were patched onto 7H11 kanamycin-hygromycin-trimethoprim plates and colonies resistant to trimethoprim were selected for PCR . Using primers F102 and R102 , PCR products were sequenced . The sequences were used to perform BLAST against the M . ulcerans Agy99 genome . Inserts containing open reading frames in the incorrect orientation were discarded . Also removed were inserts that matched non-coding genomic DNA or the dehydrofolate reductase from M . ulcerans . M . ulcerans strain S1013 used for experimental infection of mice was isolated in 2010 from the ulcerative lesion of a Cameroonian BU patient [28] . Bacteria were cultivated in Bac/T medium for 6 weeks , recovered by centrifugation and a stock suspension in sterile PBS of 125 mg/ml wet weight was prepared . 30 μl of a 1:1000 dilution of the stock solution was injected subcutaneously into the left hind foot pad of 14 week old female BALB/c mice . On day 87 after infection , mice were euthanized and foot pads were aseptically removed . Foot pads were dipped into 70% ethanol , dried under the laminar flow , cut into four pieces with a scalpel and transferred to reinforced hard tissue grinding tubes ( MK28-R , Precellys ) containing 750 μl of Bac/T medium ( bioMérieux ) . Tissue homogenization was performed with a Precellys 24-dual tissue homogenizer ( 3 × 20 s at 5000 rpm with 30 s break ) . After transferring the supernatant to a fresh tube , the residual tissue remains were homogenized a second time in 750 μl of Bac/T medium . Tissue lysates were pooled and stored at -80°C until further use . 500 μl of thawed tissue lysate was transferred into tough microorganism lysis tubes ( VK05–2ml , Precellys ) , inactivated for 1 h at 85°C and centrifuged at 17′000 × g for 5 min . The pellet was resuspended in 250 μl PBS containing protease inhibitors ( Roche , EDTA—free ) and cells were disrupted with Precellys 24-dual tissue homogenizer ( 2 × 30s at 6800rpm with 1 min break in between ) . Lysates were cleared by centrifugation and tested by ELISA . Nunc-Immuno Maxisorp 96-well plates ( Thermo Scientific ) were coated with 10 μg/ml JD3 . 4 mAb ( 50 μl per well ) in PBS and incubated ON at 4°C . Plates were washed three times with washing buffer ( 2 . 5% Tween 20 in dH2O ) prior to incubation with blocking buffer ( 5% non-fat dry milk in PBS ) for 2 h at RT . After washing as described above , 50 μl of different dilutions of the purified recombinant full length MUL_3720 , M . ulcerans lysate ( NM20/02 ) , or lysates from M . ulcerans infected tissue samples in PBS were added and incubated for 2 h at RT . Following an additional washing step , 50 μl anti-MUL_3720 rabbit IgG ( 5 μg/ml ) in blocking buffer with detergent ( 0 . 5% non-fat dry milk in PBS containing 0 . 05% Tween 20 ) was added and incubated for 2 h at RT . After washing as described above , 50 μl goat anti-rabbit IgG coupled to horseradish-peroxidase ( Milan ) diluted 1:10′000 in blocking buffer with detergent was added and incubated for 1 h at RT . Plates were washed and TMB peroxidase substrate solution was added . After 10 min the reaction was stopped with 2 M sulfuric acid and absorbance was measured at 450 nm with a microplate reader ( Tecan Sunrise ) .
For identification of suitable proteins that could be used as targets in diagnostic test formats , an M . ulcerans whole protein lysate was analysed by 2D gel electrophoresis ( S1 Fig . ) . In total , 384 protein spots were detected , processed and subsequently subjected to MALDI-TOF-MS . Among the 384 spots , 118 peptide fragments were identified and attributed to 36 different genes . In order to select for proteins without orthologs in M . tuberculosis , M . bovis or M . leprae , a BLAST search against the Uniprot database was performed for all 36 proteins , resulting in the identification of three potential targets ( MUL_3720 , MUL_0343 and MUL_4023 ) suitable for a selective antigen capture assay . However , MUL_0343 and MUL_4023 presented very weak protein spots in the 2D gel , while MUL_3720 showed a high expression level and was therefore selected for further analysis . The 624 bp MUL_3720 gene encodes a protein of 207 amino acids , with a molecular mass of 22 kDa . MUL_3720 is predicted to possess an N-terminal bulb-type mannose-specific lectin domain and a C-terminal peptidoglycan-binding Lysin Motif ( LysM ) linked by a proline-rich sequence ( Fig . 1 ) . Database comparisons revealed the presence of orthologs with a similar domain organisation in M . abscessus ( MAB_2373 ) , M . avium , M . colombiense , M . fortuitum , M . kansasii ( MKAN_05370 ) , M . marinum ( MMAR_3773 ) , M . smegmatis ( MSMEG_3662 ) and M . xenopi . The M . marinum ortholog displayed a sequence identity of 99% ( S2 Fig . ) . For the generation of antibodies against MUL_3720 , required for the detection of the protein in diagnostic assays , we immunized mice and rabbits with the full length protein , recombinantly expressed as a His-tagged fusion protein ( predicted molecular mass 24 kDa ) in E . coli BL21 . Hybridoma cell lines producing antibodies against different epitopes of the protein were identified by analyzing their reactivity against MUL_3720 ( aa 1–207 ) as well as the truncated version of MUL_3720 ( aa 115–207 ) , lacking the lectin domain and consisting only of the LysM motif and the proline-rich sequence . Five mAbs ( JD3 . 2 , JD3 . 3 , JD3 . 4 , JD3 . 6 and JD3 . 7 ) , all of them mouse IgG1 ( κ ) isotype , were generated , purified and further characterized . All the mAbs recognized recombinant full length MUL_3720 ( aa 1–207 ) in ELISA . While all antibodies except for JD3 . 6 recognized recombinant full length MUL_3720 ( aa 1–207 ) in Western Blot analysis ( Fig . 2A ) , only JD3 . 2 and JD3 . 4 also reacted with recombinant truncated MUL_3720 ( aa 115–207 ) ( Fig . 2B ) and the endogenous protein in M . ulcerans lysates ( Fig . 2C ) ( S1 Table ) . The ability of mouse mAbs JD3 . 2 and JD3 . 4 to detect endogenous MUL_3720 in lysates of M . ulcerans strains from different geographical regions was examined by Western Blot analysis . While the protein was recognized by JD3 . 2 and JD3 . 4 in all M . ulcerans strains , isolates belonging to the classical M . ulcerans lineage ( Ghana , Côte d’Ivoire , Togo and Australia ) showed higher expression levels as compared to isolates belonging to the ancestral lineage ( China and Japan ) ( Fig . 3 ) . Interspecies cross-reactivity of MUL_3720 was determined by Western Blot analysis with lysates of a range of different mycobacterial species ( Fig . 4 ) . In accordance with the BLAST search for MUL_3720 orthologs ( S2 Fig . ) , rabbit polyclonal anti-MUL_3720 IgG reacted with proteins in lysates of M . fortuitum , M . marinum , M . smegmatis and M . xenopi . In agreement with a shorter linker between the lectin and the LysM domains ( S2 Fig . ) , the M . xenopi ortholog was detected at a lower molecular weight . The predicted orthologous proteins in M . abscessus , M . avium and M . kansasii were not recognized by the rabbit polyclonal IgG . Furthermore , a protein band was observed in lysates of M . gordonae , M . malmoense and M . terrae for which no sequence information is available ( Fig . 4A ) . The detected proteins in M . malmoense and M . terrae were slightly smaller than MUL_3720 . Analysis with JD3 . 4 led to a similar staining pattern among the mycobacterial lysates , except for the protein expressed by M . gordonae , which was not recognized ( Fig . 4B ) . JD3 . 2 only reacted with protein in lysates of M . malmoense and M . marinum , suggesting that the two mAbs JD3 . 2 and JD3 . 4 recognize different epitopes of MUL_3720 ( Fig . 4C ) . In order to confirm the expression of MUL_3720 by M . ulcerans and the ability of the anti-MUL_3720 mAb JD3 . 2 to detect the protein in vivo , we performed immunohistochemistry and immunofluorescence stainings . M . ulcerans could be detected in punch biopsies of human BU patients with the mAbs JD3 . 2 ( Fig . 5 ) and JD3 . 4 . ZN staining ( Fig . 5 A , C , E ) and JD3 . 2 staining ( Fig . 5 B , D , F ) of serial sections showed identical localization at the same tissue region . AFBs detected by ZN staining in tissue sections revealed a homogeneous staining pattern , whereas immuno-staining with mAb JD3 . 2 exhibited a heterogeneous staining pattern of the bacteria with intensively stained poles ( Fig . 5 ) , indicating a higher expression of the protein in these areas in the natural environment of the bacteria . In contrast , IFA of in vitro cultivated bacteria showed a more homogeneous distribution of the protein on the bacterial cell surface . This localization was confirmed in a MUL_3720 overexpressing M . ulcerans strain ( Fig . 6 ) . As a first step to begin to understand the role of MUL_3720 we used a bait and prey approach to identify other M . ulcerans proteins that interacted with this protein . We employed the mycobacterium-specific protein fragment complementation ( M-PFC ) system . An M-PFC bait clone using a N-terminal fusion of MUL_3720 and co-transformation with a random library of M . ulcerans genomic DNA fragments in pUAB300 ( prey ) resulted in approximately 150 trimethoprim resistant colonies . Subsequent clones were patched and screened using primers F102 and R102 to determine the identity of the DNA sequence present in pUAB300 ( Table 1 ) . Multiple independent clones were identified for sequences encoding DesA1 ( MUL_0445 ) and a PE-PGRS protein ( MUL_0572 ) , together with 13 other single-hit CDS , including an interaction with MUL_3720 itself . Many of the putative interacting proteins had a predicted cell wall location or role in cell wall biosynthesis , in line with the localization data for MUL_3720 revealed by mAb staining ( Table 1 and Fig . 6 ) . No interacting proteins were identified using the C-terminal MUL_3720 bait fusion , consistent with the predicted cell wall location for this domain of MUL_3720 . The M-PFC detects cytoplasmic protein-protein interactions only [26] . We analyzed different combinations of the generated mAbs and polyclonal IgG as MUL_3720 capturing and detecting antibodies in an antigen capture sandwich ELISA . The application of mAb JD3 . 4 as capturing and polyclonal rabbit IgG as detecting reagent enabled a highly sensitive detection of recombinant MUL_3720 ( Fig . 7A ) and the endogenous protein present in lysates of in vitro cultivated M . ulcerans ( Fig . 7B ) . In order to test if the antigen capture ELISA is able to detect MUL_3720 expressed by bacteria in infected tissue samples , we analyzed lysates of M . ulcerans infected mouse foot pads . MUL_3720 could be detected in lysates of all five infected tissue samples analyzed , while only background readouts were obtained for lysates of uninfected foot pads ( Fig . 8 ) .
Attempts to develop a diagnostic tool based on serological approaches have been equivocal [9–11] , so we decided to focus on direct detection of M . ulcerans antigens in BU patient specimens . In the present study , we identified the MUL_3720 protein as a promising target in antigen capture-based diagnostic tests for M . ulcerans . Based on 2D gel electrophoretic analyses , MUL_3720 is one of the most highly expressed proteins in vitro . The high expression of MUL_3720 is considered an advantage with respect to developing a sensitive antigen detection test for the diagnosis of BU . While the biological role of MUL_3720 is not known , clues to its function are suggested by its two-domain structure—a conserved bulb-type mannose-binding lectin domain and a Lysin Motif ( LysM ) domain—predicted to be involved in alpha-D-mannose recognition and in binding to peptidoglycan , respectively . Some bacterial species retain certain proteins attached to peptidoglycan by their LysM domains [29] . We used a mycobacteria-specific two-hybrid system to search for M . ulcerans proteins interacting with MUL_3720 and we had hits to a range of proteins known or predicted to be cell-wall associated or involved in cell wall synthesis ( Table 1 ) . Many of the interacting proteins—such as DesA1 ( Table 1 ) —are involved in biosynthesis or modification of cell wall molecules . In other mycobacteria , the resulting double bonds from the DesA1-mediated catalysis of a desaturation reaction of saturated alkyl chains that arise during mycolic acid synthesis are required for subsequent position specific modifications such as epoxidation and cyclopropanation of this key cell wall metabolite [30 , 31] . MUL_3720 appears to be arranged in an operon structure with two adjacent putative cell wall-associated protein coding genes ( MUL_3721 , MUL_3722 ) . Immunofluorescence stainings of M . ulcerans bacilli confirmed the cell wall localization of MUL_3720 . With its cell wall location , the two-domain structure including a mannose-binding N-terminal cytoplasmic component and C-terminal peptidoglycan-binding component , its operon structure and a substantial list of potential interacting proteins , MUL_3720 may be an adaptor protein for multiple cell wall biosynthetic pathways . MUL_3720 might play a role in cell attachment and cell-cell interactions given its presence at the cell surface as revealed by immunofluorescence microscopy and immunohistochemical analyses . The cell-surface localization of MUL_3720 is an additional advantage with respect to developing a sensitive diagnostic test , since the protein is expected to be easily accessible and detectable in tissue specimens of BU lesions . Potential shedding of the protein from the cell surface may facilitate ready detection in body fluids , which will be examined in future experiments . Monoclonal and polyclonal antibodies against MUL_3720 were generated for the development of antigen capture assays . These antibodies recognized in vitro grown M . ulcerans bacilli as well as bacteria in biopsies of human BU patients , proving the expression of MUL_3720 in BU lesions . Since these antibodies did not react with orthologs of MUL_3720 in other pathogenic mycobacterial species prevalent in the BU endemic regions , prospects for the development of a test with the desired specificity , excluding in particular cutaneous tuberculosis [5] , are good . The monoclonal antibodies used for the antigen capture test bind to an epitope on the proline/rich linker and/or the LysM domain . Since the LysM domain is a widespread protein module present in more than 4000 proteins of both prokaryotes and eukaryotes [29] , the potential cross-reactivity of the anti-MUL_3720 antibodies with those proteins remains to be analyzed . Importantly , this capture assay specifically detected MUL_3720 protein in tissue lysates of M . ulcerans infected mouse footpads . Furthermore , initial results revealed that MUL_3720 could be detected in swab samples from human BU lesions with a high bacterial burden ( manuscript in preparation ) . Ongoing optimization of the applied reagents as well as the assay format is aiming at the development of a simple test format appropriate for low-resource laboratory settings with suitable test sensitivity . Antibiotic treatment of BU in its early stages leads in most of the cases to complete healing of the lesions with little or no trauma , whereas treatment at later stages often requires adjunct surgical treatment and is associated with prolonged hospitalization and long-term sequelae . The development of a simple and rapid diagnostic test , whose key elements are provided in the work presented here , will be of immediate benefit to BU patients in rural endemic communities . Clinical findings could directly be reconfirmed by this point-of-care test helping to avoid a false diagnosis and to facilitate a prompt onset of adequate treatment .
|
According to the recommendations of the World Health Organization , the clinical diagnosis of BU should be reconfirmed by at least two laboratory techniques . However , out of the four currently available tests , three ( PCR , histopathology and cultivation of M . ulcerans ) can only be performed at centralized reference laboratories; the fourth ( microscopic detection of acid fast bacilli ) lacks the required sensitivity and specificity . Therefore , a simple tool for early diagnosis of the disease , which can be implemented in rural health care facilities of the endemic countries , is of urgent need . In this study we aimed at the identification of M . ulcerans proteins as potential targets for the development of a simple and rapid diagnostic antigen detection assay . Among 36 proteins , MUL_3720 best met the predefined criteria of being highly expressed by M . ulcerans and not having orthologs in other pathogenic mycobacterial species prevalent in the endemic regions . Here we generated monoclonal and polyclonal antibodies against this protein and carried out pilot studies for the development of an antigen capture-based diagnostic test .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Identification of the Mycobacterium ulcerans Protein MUL_3720 as a Promising Target for the Development of a Diagnostic Test for Buruli Ulcer
|
Tissue organization in epithelial organs is achieved during development by the combined processes of cell differentiation and morphogenetic cell movements . In the kidney , the nephron is the functional organ unit . Each nephron is an epithelial tubule that is subdivided into discrete segments with specific transport functions . Little is known about how nephron segments are defined or how segments acquire their distinctive morphology and cell shape . Using live , in vivo cell imaging of the forming zebrafish pronephric nephron , we found that the migration of fully differentiated epithelial cells accounts for both the final position of nephron segment boundaries and the characteristic convolution of the proximal tubule . Pronephric cells maintain adherens junctions and polarized apical brush border membranes while they migrate collectively . Individual tubule cells exhibit basal membrane protrusions in the direction of movement and appear to establish transient , phosphorylated Focal Adhesion Kinase–positive adhesions to the basement membrane . Cell migration continued in the presence of camptothecin , indicating that cell division does not drive migration . Lengthening of the nephron was , however , accompanied by an increase in tubule cell number , specifically in the most distal , ret1-positive nephron segment . The initiation of cell migration coincided with the onset of fluid flow in the pronephros . Complete blockade of pronephric fluid flow prevented cell migration and proximal nephron convolution . Selective blockade of proximal , filtration-driven fluid flow shifted the position of tubule convolution distally and revealed a role for cilia-driven fluid flow in persistent migration of distal nephron cells . We conclude that nephron morphogenesis is driven by fluid flow–dependent , collective epithelial cell migration within the confines of the tubule basement membrane . Our results establish intimate links between nephron function , fluid flow , and morphogenesis .
Organs form by a sequential process of cell type–specific differentiation and subsequent morphogenetic cell rearrangements . Examples of cell rearrangement and motility during early development include the movement of groups of epithelial cells during gastrulation that is coupled to the formation of the germ layers [1] , and convergent extension cell movements that drive elongation of the embryonic axis [2] . Cell rearrangements during organ morphogenesis are observed in the forming heart , where bilateral groups of specified cells migrate to the embryo midline and fuse to form the primitive heart tube [3] . Epithelial organs including the kidney , lung , and liver undergo a process of branching morphogenesis where an out-pouching growth from an epithelial tube is reiteratively subdivided to form ductal organ systems [4] . In several instances of morphogenesis , epithelial cells move as intact clusters , sheets , or tubes , in a process termed “collective cell migration” [5] . This type of cell migration is observed in the caudal migration of lateral line primordia in zebrafish [6] , border cell migration in Drosophila ovaries [7] , dorsal closure in Drosophila embryos [8] , wound healing in epithelial monolayers [9] , and recently , in an in vitro model of mammary gland branching morphogenesis [10] . Collective cell migration is characterized by the movement of epithelial cells while still connected by apical cell–cell junctions and the maintenance of the polarized epithelial cell phenotype [5] . Collective cell migration can be directed by leading edge cells that extend actin-rich lamellipodial extensions in the direction of movement and transmit directional information to following cells by way of cell–cell adhesions [11] . While much is known about individual , mesenchymal cell migration , the mechanics of collective cell migration and the extent to which this process underlies organ morphogenesis are not fully known . Morphogenesis of the vertebrate kidney results in the formation of multiple functional units called nephrons . Each nephron contains a glomerulus , or blood filter , and a tubular component that processes filtered blood plasma [12] . The organization of each nephron into discrete tubule segments that serve specialized transport functions is essential for the efficient recovery of ions and metabolites and for tissue fluid homeostasis [12] . In the mammalian kidney , the most proximal nephron segment , the proximal tubule , is responsible for the majority of salt , metabolite , and water recovery [13] . The function of nephron segments is reflected in their morphology . The reabsorptive capacity of the proximal tubule is enhanced by maximizing cell surface area in contact with vasculature and convolution of the proximal tubule within a rich vascular bed [13] . Tubule segment dysfunction underlies several human kidney pathologies including Fanconi syndrome ( Online Mendelian Inheritance in Man ( OMIM; http://www . ncbi . nlm . nih . gov/omim/ ) accession code 134600 ) , renal tubular acidosis ( OMIM 179830 ) , and renal tubular dysgenesis ( OMIM 267430 ) . Despite the importance of nephron morphogenesis and tubule segment patterning to kidney function , little is known about how nephron segment boundaries are defined during development or what morphogenetic events drive tubule convolution . The zebrafish pronephros is a simplified model for in vivo studies of kidney morphogenesis . Similar to the mammalian nephron , the zebrafish pronephric nephron is a segmented tubule [14–18] . Initial definition of nephron segments and kidney cell type differentiation has been linked to retinoic acid signaling and to Notch/Jagged2 interactions [15 , 18] . During maturation of zebrafish pronephros , nephron segment boundaries shift in a proximal direction . For example , segments defined by markers of the late proximal tubule are located in the mid-nephron shortly after kidney epithelial differentiation but are found adjacent to the glomerulus in older embryos [18] . Over the same time period , the pronephric nephron becomes convoluted in its proximal segment which later becomes closely associated with the cardinal veins [19] . Currently , nothing is known about the mechanism of these morphogenetic events . We present here evidence that collective cell migration of differentiated pronephric epithelial cells accounts for both the proximal shift in nephron segment boundaries and proximal tubule convolution . We find that fully polarized epithelial cells initiate a concerted , proximal-directed cell migration that occurs within the confines of the tubular basement membrane . Pronephric epithelial cell migration in turn depends on lumenal fluid flow , thus linking kidney morphogenesis to kidney function .
Examination of the zebrafish pronephros at 24 and 72 hours post-fertilization ( hpf ) revealed a proximal-directed shift in cells expressing markers of specific nephron segments ( Figure 1 ) . The proximal tubule , identified by expression of the sodium bicarbonate co-transporter nbc1 , spanned more than half of the total nephron length at 24 hpf ( Figure 1A ) but by 72 hpf occupied only the most proximal nephron domain ( Figure 1B ) . Similarly , the late proximal nephron segment , characterized by trpM7 expression , occupied a domain of the nephron over the yolk extension at 24 hpf ( Figure 1C ) , but by 72 hpf had shifted in proximal direction ( Figure 1D ) . The most distal nephron segment , homologous the collecting duct in mammals , expresses ret1 , which at 30 hpf , occupies the distal one-fifth of the nephron ( Figure 1E ) . The proximal boundary of ret1 expression was dramatically shifted in a proximal direction by 6 days post-fertilization ( dpf ) , and ret1-expressing cells occupied about one-half the nephron length ( Figure 1F ) . Over the same time period , the initially ( at 24 hpf ) straight proximal kidney segment ( Figure 1G ) became convoluted by 72 hpf , folding into a hairpin structure adjacent to the glomerulus ( Figure 1H ) . The proximal shift in nephron segment-specific gene expression could be due to a gradual re-patterning of the nephron , with induction of new gene expression in more proximal cells . Alternatively , the shift of segment boundaries could result from segment-specific proliferation or apoptosis . It is also possible that final segment boundaries could be established by a reorganization of existing nephron segment cells without changes in gene expression . To discriminate between these possibilities , we followed the behavior of individual kidney epithelial cells by time-lapse imaging in embryos expressing green fluorescent protein ( GFP ) under the control of nephron segment-specific promoters . Using transgenic lines that express GFP in specific pronephric segments ( Figure 2 ) , we tracked individual GFP-expressing cells and neighboring dark cells at segment boundaries and determined whether or not changes in GFP expression ( from segment-specific promoters ) occurred during the period of observation . For each transgenic line , fluorescent cells at segment boundaries at the start of the time lapses remained fluorescent and , conversely , neighboring dark cells did not start expressing GFP during the time period between 36 and 96 hpf ( Figure 2A–2E ) . These results indicate that re-patterning of pronephric epithelial cells by new induction of segment-specific gene expression is not the principle mechanism underlying the shift in nephron segment boundaries ( Figure 1 ) . Further , we did not observe apoptosis in time lapse images or by acridine orange staining ( unpublished data ) , indicating that nephron segment shift was not due to cell death . Instead , we found that after nephron segments were specified , pronephric epithelial cells engaged in a concerted proximal-directed cell migration toward the glomerulus ( Figure 2 ) and that this cell migration was sufficient to account for the displacement of nephron segment boundaries and proximal tubule convolution . We imaged live pronephric cells in five different GFP transgenic lines that mark populations of epithelial cells in different nephron segments ( Figure 2 ) . The ET33-D10 line expresses GFP in the proximal nephron segment ( Figure 2A and Video S1 ) , ET11–9 expresses GFP in the mid proximal to distal segments ( Figure 2B and Video S2 ) , CD41:GFP expresses GFP in multiciliated cells ( Figure 2C , Video S3 , and Figure S1 ) , NaK ATPase:GFP expresses GFP in transporting epithelia but is excluded from intermixed multiciliated cells ( Figure 2D and Video S4 ) , and the zcs/ret1 line expresses GFP in the most distal segment analogous to the collecting system ( Figure 2E and Video S5 ) [20] . Individual cells in all five transgenic lines demonstrated migratory behavior toward the anterior or proximal nephron . Still frames of time-lapse movies from each transgenic line show displacement of individual cells ( tracked with white arrowheads; Figure 2 ) toward the anterior . Long-term imaging of the NaK ATPase:GFP line over a period of 22 h ( Figure 2F and Video S6 ) demonstrated that cells move continuously over the time period of 2–3 dpf and accumulate in the proximal tubule near the glomerulus ( Figure 2F , small arrows ) . The rate of cell migration exhibited a biphasic dependence on the position of the migrating cell along the length of the nephron ( Figure 2G ) . Migration was slowest posteriorly , in the ret1-positive distal segment near the cloaca . It increased and plateaued in more proximal segments ( ET11–9– and ET33-D10–positive segments ) and dropped near the glomerulus ( Figure 2G and 2H ) . This distribution of the migration rates resulted in the “piling up” of migrating cells anteriorly , as fast-moving cells decelerated near the glomerulus while more cells continued to arrive . Both ends of the pronephros remain fixed in position; the proximal end is fixed at the glomerulus that remains stationary ventral to somites 2–3 while the distal end is anchored at the cloaca [19] . The continuous arrival of migrating cells at the fixed proximal end of the pronephros resulted in folding of the proximal pronephric tubule into a hairpin-like structure , the proximal convolution ( Figure 2F and Videos S6 and S7 ) . The most distal , ret1-expressing segment , in contrast , became long and straight . This distal segment spanned as much as half of the nephron length by 6 dpf ( Figure 1F ) . The concerted movement of tubule cells relative to other tissue implied that individual epithelial cells would be polarized in the direction of movement , would show protrusive membrane activity , and would exert traction on the tubule basement membrane for forward movement . We examined single cell behavior in time-lapse videos to test whether pronephric cells exhibited any of these behaviors . An example of an individual CD41:GFP-positive cell imaged every 20 min is shown in Figure 3A and Video S8 . Migrating cells extend basal membrane protrusions in the direction of forward movement and appear to make transient contacts with the tubule basement membrane ( Figure 3A ) . Membrane domains engaged in dynamic reorganization of focal adhesions are often positive for phosphorylated Focal Adhesion Kinase ( phospho-FAK ) . Basal surfaces of tubule segments exhibiting movement were positive for phospho-FAK by antibody staining ( Figure 3B ) , further supporting the idea that basal cell protrusions were associated with adhesion to the tubule basement membrane . The migration of epithelial cells often involves a transition to a more mesenchymal phenotype and loss of cell–cell adhesion [21] . We examined migrating cells by electron microscopy and found instead that the pronephric epithelial cells remained fully polarized with brush border membranes and joined together by adherens junctions ( Figure 3C ) . Interestingly , we also observed that a majority of cells extended lamellipodia along the tubule basement membrane at their forward edge and under the cell in front ( Figure 3C; white arrowheads and inset ) . In electron microscopy of one complete tubule , 21 out of 32 basal cell junctions studied ( 66% ) showed forward-directed basal lamellipodia . Thus , tubule epithelial cells maintain apical connections to their neighbors in front and behind while exhibiting free movement of their basal surfaces , engaging in a form of collective cell migration . Since both the distal and proximal ends of the pronephros are fixed in position at the cloaca and glomerulus , respectively , the proximal cell migration we observe would be expected to cause an overall lengthening the pronephros while depleting the number of cells in distal segments . Measurements of the pronephros did reveal a steady lengthening of the tubule over the period of observation ( 1 , 264 ± 15 μm at 48 hpf ( n = 9 tubules ) ; 1 , 564 ± 25 μm at 96 hpf ( n = 7 tubules ) ) however we did not observe a significant “thinning” of cells in the distal segment . We therefore asked whether the lengthening of the distal segment was accompanied by an increase in the number of cells in that segment . We examined the total number of DAPI-stained nuclei in the distal , ret1:GFP-positive segment , comparing embryos at 36 hpf and at 4 dpf . The number of ret1:GFP cells doubled over this time period ( 207 nuclei at 36 hpf to 411 at 4 dpf ) . This increase in cell number was proportional to the increase in length of the ret1-GFP positive segment ( from 286 μm to 570 μm ) . In contrast , the number of proximal , ET33-D10-GFP–positive cells did not change significantly over the same time window ( 208 nuclei at 36 hpf to 242 nuclei at 4 dpf ) , whereas the length of the GFP-positive domain shortened from 662 μm to 300 μm . This compression of the proximal segment without a change in cell number is consistent with the transition from a cuboidal to columnar cell morphology ( Figure 3D and 3E ) . To test whether disproportionate cell proliferation in the distal nephron segment played a role in stimulating forward migration by “pushing” cells proximally , we imaged tubules in embryos incubated in the presence of 60 μM camptothecin to block cell proliferation . Short-term ( <8 h ) treatment of the embryos with camptothecin had no effect on the migration rate of the pronephric epithelial cells ( Video S9 ) . Camptothecin incubation of a similar duration arrested early embryo development ( Figure S2C and S2D ) and blocked BrdU incorporation ( Figure S2A and S2B ) , indicating that it effectively blocked cell division . We conclude the following: ( 1 ) proximal-directed cell migration is not driven by distal cell proliferation , but ( 2 ) the lengthening of the distal nephron segment is compensated by cell proliferation and ( 3 ) compression of the proximal tubule occurs without significant cell proliferation in this segment . In our time-lapse studies we observed that prior to 24 hpf or after 4 dpf , epithelial cells migrated at a slow rate ( <2 μm/h , Figure 4B and 4D ) . In contrast , between 36 hpf and 4 dpf , the migration rate was markedly increased ( 6–8 μm/h; Figure 2 ) . To determine when the transition between the slow migratory and fast migratory phenotype occurred , we imaged tubules starting around 24 hpf . In three independent time-lapse studies , we observed a reproducible sharp increase in cell migration rate at 28 . 5 hpf from 2 μm/h to 6 μm/h ( Figure 4A , 4C , and 4D , and Video S10 ) . Interestingly , this increase in motility was preceded by the onset of active fluid transport into the pronephros . Fluid transport into the pronephric nephron lumen could be detected by mechanically obstructing the pronephros at 24 hpf , prior to the formation of the glomerulus , and observing rapid dilation of the pronephric tubules and the formation of proximal tubule cysts within one hour ( unpublished data ) . Fluid output at the cloaca could also be detected by covering the posterior yolk extension of an unobstructed 30hpf embryo with petroleum jelly and mineral oil and imaging pronephric excretion form fluid droplets at the outside surface of the cloaca ( Video S11 ) . Since fluid accumulation in the pronephros occurred before the onset of glomerular filtration [19] , fluid input to the tubule lumen must occur by active solute transport across the pronephric epithelium [22] . The correlation between onset of cell migration and initiation of nephron fluid flow suggested that the fluid flow could act as an initiating signal for proximal cell migration . All nephron fluid flow can be eliminated by simple mechanical obstruction of the distal nephron [23] . Remarkably , distal nephron obstruction effectively abolished convolution of the proximal tubule ( Figure 5 ) and proximal cell migration ( Figure 6 ) . In 4-dpf NaK ATPase:GFP transgenic larvae , the proximal tubule has a hook-like structure in control larvae ( Figure 5A ) , whereas in obstructed larvae , the proximal tubule remained straight with no sign of convolution ( Figure 5B ) . Similar morphological results were observed in the proximal tubule-specific transgenic line ET33-D10 ( Figure 5C and 5D ) . The quantitation of these results in a larger number of NaK ATPase:GFP-transgenic embryos confirmed that convolution was effectively abolished by nephron obstruction ( Figure 5F ) . We also examined whether distal obstruction would block progression of the proximal nephron segment boundary toward the glomerulus . The distance between the posterior border of the ear and the posterior edge of the ET33-D10 GFP-positive segment served as a measure of segment migration ( Figure 5E ) . Complete obstruction blocked the shortening and convolution of the proximal segment ( Figure 5C , 5D , and 5G; see also Figure 1A and 1B ) . Time-lapse imaging of tubule cells in obstructed larvae confirmed that the lack of tubule convolution and proximal progression was due to lack of proximal cell migration ( Figure 6A and 6B ) . In unilaterally obstructed larvae , migration was blocked specifically on the obstructed side while proximal migration continued on the unobstructed side ( Figure 6A and Video S12 ) . Interestingly , while obstruction blocked proximal cell migration , in several instances pronephric cells continued to move circumferentially such that the tubule appeared to rotate on the lumenal axis ( Figure 6B and Video S13 ) . Quantitation of migration rates of multiple cells revealed that obstruction severely reduced or abolished proximal migration . However , the rate of circumferential cell motility was similar to the rate of proximal cell migration in control larvae ( Figure 6B and 6C ) . We and others have shown that morpholino knockdown of the transient receptor potential ( TRP ) channel polycystin2 results in constriction of the distal nephron lumen , reduced fluid output from the pronephros , and proximal tubule cyst formation [24] . In cystic polycystin2 knockdown embryos , proximal cell migration was completely arrested ( Figure 7A and 7E ) , which is consistent with the lack of cell migration due to nephron obstruction . However , similar to the obstructed larvae , on occasion we observed persistent circumferential movement of cells , indicating that polycystin2 loss of function did not inhibit cell motility in general ( Video S14 ) . Similar to polycystin2 knockdown , interfering with cilia assembly by knockdown of intraflagellar transport protein ( IFT ) expression results in functional obstruction of the nephron due to loss of fluid propulsion with consequent tubule dilatation and proximal cyst formation [23] . Knockdown of ift88 using antisense morpholino oligos arrested proximal migration of nephron epithelial cells similar to that seen in mechanically obstructed and polycystin2 knockdown nephrons ( Figure 7B and 7E , Video S15 ) . Consistent with inhibition of cell migration , both polycystin2 and ift88 knockdown resulted in failed shortening and lack of convolution of the ET33-D10 GFP-positive proximal nephron segment ( Figure 7D ) . During this stage of development , the two pronephric glomerular primordia are also moving toward the embryo midline to eventually fuse and form the glomerulus . Neither disrupting expression of polycystin2 or ift88 , nor inducing physical obstruction of the pronephros , prevents glomerular fusion ( see Figure S3 and [23 , 24] ) . These data suggest that glomerular morphogenesis occurs by a mechanism that is distinct from the mechanisms driving proximal cell migration . Both mechanical obstruction and knockdown of polycystin2 or ift88 result in cystic dilation of the pronephros [23 , 24] , raising the possibility that cell migration could be affected by cell stretch . To assay cell migration independently of tubule dilatation and cell stretching , we induced a proximal obstruction by transecting the nephron at the proximal edge of the yolk extension . This would be expected to eliminate fluid input to the distal ( post-obstruction ) tubule without causing tubule dilatation . Proximal obstruction completely eliminated cell migration in the remaining tubule segment distal to the obstruction , whereas cells in the contralateral non-obstructed tubule continued to migrate ( Figure 7C and 7E ) . These data indicate that nephron fluid flow is essential for directed migration and that lumenal distension did not play a role in arresting proximal migration . Fluid flow in the zebrafish pronephros can be generated by at least two mechanisms: fluid input at the proximal nephron by glomerular filtration , and propulsion of fluid more distally by lumenal cilia bundles acting as local “fluid pumps” [23] . Fluid input by glomerular filtration is driven by blood pressure . We eliminated glomerular filtration by arresting heartbeat using an antisense morpholino against cardiac troponin T ( tnnt2/silent heart; [25] ) . The absence of glomerular filtration markedly inhibited collective cell migration and nephron convolution in the proximal , ET33-D10 GFP-positive nephron segment ( Figure 8C and Video S16 ) compared to control ( Figure 8A and Video S1 ) . While cell migration was blocked proximally , it persisted in the more distal ET11–9 GFP-positive nephron segment ( Video S17 ) . The proximal end of the ET11–9 segment showed the most significant decrease in the migration rate , which dropped from 5 . 5 μm/h to 2 . 5 μm/h across the anterior ( proximal ) 100 μm of the ET11–9 GFP-positive segment . Strikingly , elimination of glomerular filtration shifted the position of nephron convolution distally to an ectopic position at the boundary of the ET33-D10 and ET11–9 tubule segments ( Figure 8C , 8D , and 8N ) , suggesting that convolution occurs at a point where further proximal cell migration is blocked . In addition , epithelial morphology was significantly changed in tnnt2 morphants compared with control embryos at 84 hpf . In controls , the most proximal , convoluted segment showed a distended tubule filled with columnar epithelial cells ( Figure 8G and 8H; also Figure 3 ) whereas more distal segments were not distended and contained cuboidal epithelial cells ( Figure 8G and 8I ) . In tnnt2 morphants , this pattern was reversed and the distended portion of the tubule was found at the position of ectopic convolution—at the boundary of ET33-D10 and ET11–9 segments ( Figure 8J , 8L , and 8N ) —and was filled with columnar cells , whereas the proximal tubule was not distended and contained cuboidal epithelial cells ( Figure 8J , 8K , and 8N ) . This boundary between the ET33-D10 and ET11–9 tubules also roughly marks the most proximal extent of the CD41:GFP-positive multiciliated cell population ( Figure 2 and Figure S4 ) . To test whether persistent migration of the more distal cells in tnnt2 morphants was dependent on cilia-driven fluid flow , we disrupted cilia function with an ift88 ( polaris ) antisense morpholino [23] . Importantly , when combined with knockdown of tnnt2 , ift88 knockdown eliminated the ectopic tubule convolution seen in tnnt2 morphants ( Figure 8E , 8F , and 8M ) . The results show that lumenal fluid flow is essential for proximal-directed , collective cell migration and that tubule convolution occurs specifically at the point of resistance to further cell movement .
Collective cell migration describes the coherent movement of cell groups , while the cells themselves remain connected by cell–cell junctions and maintain constant positions while migrating [5] . Examples include the caudal migration of clusters of lateral line progenitors in zebrafish [6] , border cell migration on Drosophila oocytes [7] , and wound healing in cultured epithelial cell monolayers [9] . In each of these cases , epithelial cells adhere to each other by apical adherens junctions , while their basal surfaces are free to engage in dynamic , directed lamellipodial extensions associated with matrix adhesion . In wounded monolayers , basal lamellipodia are seen not only in leading edge cells but also in cells behind the edge , indicating that these cells are also actively involved in maintaining the migration of the monolayer [26] . Similarly , we found that kidney epithelial cells along the entire mid-portion of the nephron maintain cell–cell attachments while they extend basal lamellipodia in the direction of migration . This suggests that all tubule cells in this segment are actively involved in proximal migration as opposed to being passively “pulled along” by a smaller group of actively motile leading cells . Alternatively , it remains possible that a subset of proximal cells convey directional information to more distal cells and organize their proximal-directed migration by way of mechanical forces applied to cell–cell contacts [11] . Unlike the case of migrating lateral line primordia and wound healing in monolayers , in the pronephros , there is no free leading edge to the epithelial cell group that directs other cells to fill a gap or area of open matrix . Instead , all kidney tubule cells migrate proximally and compress cells at the leading edge into a columnar morphology , distorting tubule shape and creating the characteristic convolutions of the proximal tubule . Convolution of the proximal tubule also occurs in the mammalian kidney , where proximal tubule cells appear compressed and are more columnar in shape whereas in the more distal nephron , the cells are low cuboidal in shape [13] . The similarities in cell morphology between mammalian and zebrafish nephrons raise the intriguing possibility that collective cell migration of tubular epithelial cells may be a general feature of vertebrate kidney morphogenesis . Collective cell migration in the zebrafish pronephros required lumenal fluid flow , suggesting that fluid shear stress in the tubule lumen may be essential for nephron morphogenesis . Similar arguments have been made for biomechanical effects of fluid shear forces on cardiac morphogenesis . Physical obstruction of the cardiac inflow tract results in heart chamber malformation , decreased heart looping , and atrioventricular ( AV ) valve malformation [27] . The absence of endocardial cushions in blocked hearts lead Hove et al . to suggest that fluid shear stress was essential for the early steps in cardiac valve formation [27] . Subsequent studies using pharmacological inhibition of heart function have suggested an alternative mechanism of endocardial cushion formation that involves myocardial contractions per se , independent of shear forces [28] . We have previously presented evidence implicating fluid shear force in morphogenesis of the glomerulus in zebrafish [29] . Mutants lacking blood flow fail to express MMP2 in endothelial cells and do not invade and remodel glomerular podocytes [29] . Perhaps the best-studied example of cellular response to fluid shear stress is the response of endothelial cells to blood flow . Endothelial cells sense and respond to fluid shear using a system of adhesion molecules including PECAM and VE-cadherin , integrin activation , activation of receptor tyrosine kinases , calcium influx , and modulation of the cytoskeleton by Rho family GTPases [30] . Fluid shear first induces lamellipodial cell extensions followed by basal protrusions and new focal adhesion formation in the direction of flow , which are controlled by polarized distribution of rac and cdc42 [31 , 32] . Subsequent migration requires remodeling of adhesions and release of cell substratum attachments at the rear of the migrating cell [33] . Migration of pronephric epithelial cells is likely to involve similar basic mechanisms . For instance , we observe a strong correlation between the presence of directed lamellipodial extensions of epithelial cells on the tubule basement membranes and basal phospho-FAK staining , suggesting that pronephric epithelial cells actively remodel their matrix attachments as they migrate . It is also likely that specific integrin subunits are required for pronephric cell migration , as they are in endothelial cells [34 , 35] . A notable difference between pronephric cell migration and endothelial cell migration is that pronephric cells migrate against flow instead of in the direction of flow . The similarities and differences between these two systems are likely to prove useful in resolving how mechanical forces establish self-perpetuating cell movement . It is currently not clear how kidney epithelial cells might initially sense and respond to fluid flow . Manipulation of two known members of the mechanosensory TRP ion channel family , polycystin2 and trpM7 , did not conclusively link motility to TRP channel activity . polycystin2 morphants have an obstructed phenotype [24] , which precludes the analysis of Polycystin2 in fluid flow sensing . Also , polycystin2 morphant tubule cells continue to move in a circumferential fashion , similar to other obstructed models , indicating that polycystin2 is not required for motility per se . Knockdown of jagged2 re-patterns the nephron and eliminates trpM7 expression [15]; however , this did not affect the extent of tubule cell migration ( unpublished data ) . Further studies will be required to determine whether mechanosensory channels are involved in tubule cell migration , or whether shear forces are detected by other mechanisms , as has been described in endothelial cells [36] . While the data is compelling for a role of shear stress in nephron epithelial cell migration , an alternative mechanism for stimulation of motility could involve the delivery of a chemotactic peptide to the nephron lumen by glomerular filtration or secretion that is then carried down the nephron by fluid flow . Nephron epithelial cells could internalize or otherwise inactivate such a ligand and establish a linear concentration gradient along the length of the nephron . While we cannot completely rule out this potential mechanism , aspects of our data suggest that it is unlikely . First , elimination of glomerular fluid input by blocking cardiac contractions did not prevent cell migration in distal nephron segments . This indicates that delivery of a chemotactic peptide by glomerular filtration is not required for distal cell motility . If a hypothetical chemoattractant were instead secreted by the epithelial cells themselves , the silent heart/tnnt2 morphant data would indicate that proximal tubules do not secrete the factor , since these cells are not motile in tnnt2 morphants . However , proximal tubule cells are highly motile in normal embryos with filtration-driven fluid flow . Also , it is difficult to imagine how a gradient of secreted chemoattractant could generate the circumferential cell movement observed in obstructed nephrons . It is more likely ( but not proven ) that this curious cell behavior could result from local vortex fluid forces generated by cilia beating in the tubule lumen . Indeed , we consistently observed the presence of such vortex currents in obstructed kidneys as evidenced by the movement of lumenal debris ( unpublished data ) . Does collective cell migration play a role in organ pathology and tissue repair ? In utero obstruction of the kidney outflow that is induced experimentally or due to congenital anomalies results in kidney dysplasia that involves both cystic changes with the failure of nephrons to mature [37] . Interestingly , urogenital obstruction often results in a reduction in the ratio of proximal-to-distal tubule segments , as assayed by cell morphology in kidney biopsy specimens [37] . This phenomenon would be consistent with lack of nephron fluid flow preventing normal cell migration and proximal tubule convolution . Autosomal renal tubular dysgenesis ( OMIM 267430 ) is a severe disorder characterized by lack of proximal tubule differentiation [38] . Low blood pressure and hypoperfusion of the fetal kidney are proposed to be common features of several conditions that lead to renal tubular dysgenesis [39] . A reduction in filtration-driven nephron fluid flow would be expected in this context , again raising the possibility that failure to stimulate fluid flow and cell migration could contribute to nephron pathology . The phenomenon of intraepithelial migration may also play a role in the adult kidney homeostasis . Kidney tubules are known to regenerate after damage from acute insult [40] . In this case , however , proliferating tubule cells undergo a mesenchymal transformation before repolarizing to replace missing epithelial cells [41] . Nonetheless , it is possible that the final re-establishment of overall tubule morphology and nephron segment boundaries after injury could involve collective tubule cell migration , similar to what we describe .
The NaK ATPase alpha1A4:GFP transgenic line [15] , the ET ( krt8:EGFP ) sqet11–9 line and the ET ( krt8:EGFP ) sqet33-d10 line [42–44] , the CD41:GFP line [45] , and ret1:GFP line ( zcs ) [20] were raised and maintained as described in ( Westfield , 1995 ) . The ET ( krt8:EGFP ) sqet11–9 and ET ( krt8:EGFP ) sqet33-d10 lines are referred to as ET11–9 and ET33-D10 . Embryos were kept at 28 . 5°C in E3 solution until 24 hpf and E3 solution containing 0 . 003% PTU ( 1-phenyl-2-thiourea , Sigma ) after 24 hpf . To create transgenic lines ET33-D10 and ET11–9 , the embryos of primary transgenic lines ET33 –D10 and ET11–9 were injected with mRNA encoding Tol2 transposase resulting in transposition of Tol2 transposon within a genome as described before and F1 heterozygote embryos were screened for new GFP expression patterns [42–44] . Five transgenic lines expressing GFP in the pronephros were used to image cell migration in time lapse videos: ET33-D10 , ET11–9 , ret1:GFP , CD-41:GFP , and NaK ATPase:GFP . Embryos from incrossed adults of each line were anesthetized using 0 . 2mg/ml tricaine , immobilized/oriented in 2% low melting point agarose , and mounted on the stage of a Zeiss LSM5 Pascal confocal microscope . At 20–45 minute intervals the pronephros was imaged in a z-series stack using a 40X water dipping lens . Maximum intensity projections of each stack were generated and assembled into a time lapse videos using Zeiss pascal software . Frames were additionally processed to adjust contrast using Adobe photoshop and reassembled into quicktime movies using Graphic Converter ( Lemke software ) . The following morpholino oligos were used: ift88 ( GenBank [http://www . ncbi . nlm . nih . gov/Genbank/] accession code NM_001001725 ) exon1d: 5′-AGCAGATGCAAAATGACTCACTGGG-3′ 0 . 2 mM; polycystin2 ( NM_001002310 ) exon12d: 5′-CAGGTGATGTTTACACTTGGAACTC-3′0 . 25 mM; Standard Control: 5′-CCTCTTACCTCAGTTACAATTTATA-3′ 0 . 25 mM; and tnnt2 ( NM_152893 ) ATG: 5′-CATGTTTGCTCTGATCTGACACGCA-3′ 0 . 125 mM . Morpholinos were diluted in 100 mM KCl , 10 mM HEPES , 0 . 1% Phenol Red ( Sigma ) to the following concentrations: 0 . 2 mM ift88 , 0 . 125 mM tnnt2 , 0 . 25 mM polycystin2 , 0 . 25 mM Standard Control . When used together , ift88 and tnnt2 morpholino concentrations were 0 . 15 and 0 . 125 mM , respectively . A fixed volume of 4 . 6 nl was injected into each embryo at 1–2 cell stage using a Nanoliter2000 microinjector ( World Precision Instruments ) . Morpholino efficacy was confirmed by observing expected phenotypes: the ift88 morpholino induced glomerular cysts [23] , the tnnt2 morpholino eliminated heart contractions [25] , and the polycystin2 morpholino induced a characteristic spiral dorsal axis curvature [24] . 24-hpf embryos were dechorionated and anesthetized with tricaine . An incision just anterior to the cloaca and perpendicular to the long axis of the embryo was made using a razor blade . For anterior obstruction , the incision was made at the level of yolk-to- yolk extension interface . The embryos were allowed to heal for few minutes and then were transferred into new , clean E3 egg water . The ET33-D10 GFP transgenic line was used to measure the extent of proximal cell migration in groups of embryos at fixed times of development . The embryos were anesthetized using 0 . 2 mg/ml Tricaine and immobilized/oriented in 2% low melting point agarose . The embryos were photographed using Leica DFC 300 FX camera mounted on a Leica MZ 16F fluorescent dissecting microscope using Leica Application Suite version 2 . 4 . 0 R1 ( Leica Microsystems ) . Images were imported into Aperio ImageScope viewer ( Aperio Technologies , Inc . ) and the distance from the posterior edge of the otic vesicle to the distal edge of the GFP positive pronephric domain was determined for each image . Measurements were normalized to a stage micrometer measurement . The measurement of the proximal tubule convolution was performed using NaK ATPase:GFP transgenic fish . Embryos ( with or without obstruction ) were anesthetized at 96 hpf , immobilized , and photographed as described above . The images were imported into NIH ImageJ software and curved line lengths were measured using polygon approximation . The ratios c = ( b – a ) /a were generated as shown in Figure 5F . The measurement of migration rates was performed using Zeiss Image Examiner software ( Carl Zeiss , Inc . ) . Individual cells were traced in time-lapse images and the distances traveled were measured in relationship to arbitrary stationary reference points in the skin ( skin GFP expressing cells or iridophores ) . Four–ten cells were traced per experiment , and the rates of migration were averaged . Morphometric results were plotted using Excel ( Microsoft Corp . ) . Regression fitting of the data was performed using Matlab ( The Mathworks , Inc . ) Camptothecin was purchased from Sigma and applied to a final concentration of 30 μM or 60 μM in presence of 1% DMSO at various stages of embryo development [46] . For time-lapse study , the embryo was mounted in agarose and preincubated in 60 μM camptothecin for 1 h prior to the start of time lapse . To test the BrdU incorporation , the embryos were incubated in 60 μM camptothecin for 2 h followed by addition of 10 mM BrdU ( Sigma ) and further incubated for 2 h . The embryos were then fixed in 4% PFA , washed with PBST ( 0 . 1% Tween 20 in PBS ) and treated with 10 μg/ml proteinase K for 30 min . The embryos were washed again and treated with 2N HCl for 1 h . The embryos were then prepared for the whole-mount immunohistochemistry using anti-BrdU antibody . Whole-mount in situ hybridization of zebrafish embryos at 24 hpf and 72 hpf was performed by using digoxigenin labeled RNA probes as described previously [47] . Probes used: nbc1 ( NM_001034984 ) , trpM7 ( NM_001030061 ) , and wt1a ( NM_131046 ) . Stained embryos were photographed using Leitz MZ12 microscope and Spot Image digital camera . Whole-mount immunohistochemistry was performed as described in [19] . Anti-GFP , monoclonal antibody alpha6F ( [48]; Developmental Studies Hybridoma Bank ) , anti-acetylated tubulin ( 6–11-B1; Sigma ) , and anti-BrdU ( clone BU-133 , Sigma ) primary antibodies were used . For confocal imaging , Alexa-labeled secondary antibodies ( Invitrogen ) were used and imaged using a Zeiss LSM5 Pascal confocal microscope .
|
The kidney's job is to maintain blood ion and metabolite concentrations in a narrow range that supports the function of all other organs . Blood is filtered and essential solutes are recovered in a structure called the nephron . Human kidneys have one million nephrons , while simpler kidneys like the zebrafish larval kidney have only two . Nephrons are segmented epithelial tubules; each segment takes on a particular shape ( such as convoluted , straight , or U-shaped ) and plays a specific role in recovering filtered solutes . How the nephron is proportioned into segments and how some tubule segments become convoluted is not known . This work takes advantage of the simple zebrafish kidney to image living cells during nephron formation . Unexpectedly , we found that nephron cells are actively migrating “upstream” toward the filtering end of the nephron . The cells remain connected to each other and migrate as an intact tube . This is similar to a process called “collective cell migration . ” We find that collective cell migration establishes the final position of nephron segment boundaries and drives convolution of the tubule . We also find that cell migration is dependent on fluid flow in the tubules , supporting the idea that organ function is important in defining its final form .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cell",
"biology",
"developmental",
"biology"
] |
2009
|
Collective Cell Migration Drives Morphogenesis of the Kidney Nephron
|
Functional neuroimaging research provides detailed observations of the response patterns that natural sounds ( e . g . human voices and speech , animal cries , environmental sounds ) evoke in the human brain . The computational and representational mechanisms underlying these observations , however , remain largely unknown . Here we combine high spatial resolution ( 3 and 7 Tesla ) functional magnetic resonance imaging ( fMRI ) with computational modeling to reveal how natural sounds are represented in the human brain . We compare competing models of sound representations and select the model that most accurately predicts fMRI response patterns to natural sounds . Our results show that the cortical encoding of natural sounds entails the formation of multiple representations of sound spectrograms with different degrees of spectral and temporal resolution . The cortex derives these multi-resolution representations through frequency-specific neural processing channels and through the combined analysis of the spectral and temporal modulations in the spectrogram . Furthermore , our findings suggest that a spectral-temporal resolution trade-off may govern the modulation tuning of neuronal populations throughout the auditory cortex . Specifically , our fMRI results suggest that neuronal populations in posterior/dorsal auditory regions preferably encode coarse spectral information with high temporal precision . Vice-versa , neuronal populations in anterior/ventral auditory regions preferably encode fine-grained spectral information with low temporal precision . We propose that such a multi-resolution analysis may be crucially relevant for flexible and behaviorally-relevant sound processing and may constitute one of the computational underpinnings of functional specialization in auditory cortex .
Understanding how natural sounds and scenes are processed in the human auditory cortex remains a major challenge in auditory neuroscience . Current models of auditory cortical processing describe the sound-evoked neural response patterns at the level of preferential regional activations for certain behavioral tasks ( e . g . localization vs recognition [1] , [2] ) , sound categories ( e . g . voices , speech [3] ) and ( complex ) acoustic features [4] , [5] . However , the computational and representational mechanisms underlying these responses remain largely unknown . The overall aim of the present study is to derive a computational model of how natural sounds are encoded in the human brain by combining high-resolution fMRI ( 3 and 7 Tesla ) with computational modelling . Most natural sounds are characterized by modulations of acoustic energy in both the spectral and temporal dimensions ( Figure 1A ) . These modulations occur at multiple scales [6] and are crucial for behaviorally relevant auditory processing such as speech intelligibility [7]–[10] . Psychophysical investigations indicate that humans are able to detect and discriminate modulations that occur in one dimension alone ( temporal: [11]; spectral: [12] ) as well as combined spectro-temporal modulations [9] . Similarly , neurophysiological studies in animals and humans have revealed neuronal tuning for temporal modulations [13]–[15] and spectral modulations [16] alone , and the combination of the two [17]–[21] . This evidence suggests that spectral and temporal modulations are critical stimulus dimensions for the processing of sounds in the auditory cortex . Just as the cochlea generates multiple “views” of the sound pressure wave at different frequencies , an explicit encoding of spectral and temporal modulations would allow the cortex generating multiple “views” of the sound spectrogram with different degrees of spectral and temporal resolution [22] ( Figure 1B ) . Multiple simultaneous representations of the same incoming sounds may be crucially relevant for enabling flexible behavior , as different goal-oriented sound processing ( e . g . sound localization or identification ) may benefit from different types of representations . Furthermore , the representations of sounds at multiple resolutions may provide the computational basis for binding acoustic elements in sound mixtures and solve complex auditory scenes [23] . Despite extensive investigations in a variety of experimental settings , the specific computational mechanisms used by the human auditory cortex to represent energy modulations in the spectrogram of natural sounds are still a matter of speculation . Here , we use an fMRI “encoding” approach [24] to compare competing computational models of sound representations and select the best model as the one that can predict most accurately fMRI response patterns to natural sounds . We focus on three well-defined aspects of the representation of spectral and temporal modulations: ( 1 ) dependency , ( 2 ) frequency specificity , and ( 3 ) spatial organization . Dependency refers to the relation between spectral and temporal processing . The spectrogram of natural sounds is characterized by concurrent spectral and temporal modulations and these sound qualities might be represented jointly or independently of each other . An independent representation implies separate processing mechanisms for spectral and temporal modulations , such that the response to one dimension is invariant to a change in the other dimension . By contrast , a joint representation relies on combined selectivity for the conjunction of spectral and temporal modulations . The joint representation can be modeled as an array of spectro-temporal filters that are selective for combinations of spectral and temporal modulations ( Figure S1A ) , whereas the independent representation can be seen as a bank of filters that are selective for either temporal or spectral modulations ( Figure S1B ) . In other words , the two models differ with respect to the dimensions employed by the auditory cortex to encode natural sounds ( combined spectro-temporal modulations , and spectral and temporal modulations alone , respectively ) . Testing for the interdependency of spectral and temporal modulation processing has relevant implications , as the superiority of such a model would indicate that results obtained using sounds that only vary along one dimension ( e . g . amplitude modulated tones or stationary ripples ) cannot be generalized to mechanisms of representation and processing of natural sounds . The analysis of the spectro-temporal modulation content of the sound spectrogram can be global ( 2D Fourier transform ) or localized ( e . g wavelet transform ) . A global representation indicates integration along the frequency axis , while in a local analysis spectral and temporal modulations are encoded in a frequency-specific fashion . Frequency specific responses are ubiquitous in the auditory cortex; yet it is not clear how this dimension is exploited for the representation of natural sounds . Understanding the nature of the modulation analysis performed by the human auditory cortex can provide insights about the functional role of this representational mechanism . Finally , the third aspect that we consider is the existence and layout of a large-scale spatial organization of spectro-temporal modulation tuning . Topographic maps of stimulus dimensions are a well-established organizational principle of the auditory cortex [25] . In humans , the primary [26] as well as the non-primary [27] auditory cortex contain multiple topographic representations of sound frequency ( tonotopic maps ) . Beyond tonotopy , however , the spatial organization of other sound features remains elusive [25] . Our methodological approach provides the possibility to obtain maps of multiple sound features and feature-combinations from the same set of fMRI responses and within the ecologically and behaviorally-relevant context of natural sounds processing . Here , we exploit this possibility to study the regional specificity and the spatial organization of spectro-temporal modulation tuning . Such knowledge can reveal the representational and computational basis underlying the functional specialization of auditory cortical subdivisions . Our results show that the human brain forms multiple representations of incoming natural sounds at distinct spectral and temporal resolutions . The encoding of spectral and temporal modulations is joint and frequency-specific and is governed by a trade-off between spectral and temporal resolution . Regional variations of voxels modulation preference put forward the hypothesis that the functional specialization of auditory cortical fields can be partially accounted for by their modulation tuning .
We applied an “encoding” approach ( see [24] and Figure S2 ) and compared several computational models of auditory processing . A first model we tested describes auditory cortical neurons as a bank of frequency-localized filters with joint selectivity for spectral and temporal modulations ( see [22] and Materials and Methods ) . Considering that one voxel reflects the mass activity of a great number of neurons , we modelled each voxel's receptive field as a combination of modulation selective filters , each tuned to a different spectral modulation , temporal modulation and frequency ( Figure 2 , panel A ) . Using a subset of fMRI data ( training ) , we estimated a modulation transfer function ( MTF , Figure 2 , panel A1 ) for each voxel ( see Figure 3 for two MTF examples ) . We then assessed the ability of this MTF-based model to accurately predict the fMRI responses in new , independent data sets ( testing ) . In the 3T experiment , training and testing data involved a single set of natural sounds , whereas two completely distinct sound sets were used for the 7T training and testing datasets . We quantified model's prediction accuracy by performing a sound identification analysis [24] . Namely , we used the fMRI activity patterns predicted by the estimated models to identify which sound had been heard among all sounds in the test set . Each testing sound was assigned with a score ranging between 0 and 1 and indicating the rank of the correlation between sound's predicted and measured activity patterns ( 0 indicates that the predicted activity pattern for a given stimulus was least similar to the measured one among all test stimuli; 1 indicates correct identification ) . The overall model's accuracy was obtained as the average score across all test sounds ( see Materials and Methods ) . For both the 3T and 7T datasets , the accuracy of the joint frequency-specific MTF-based model was significantly higher than chance ( 0 . 5 ) both at group level ( 3T: mean [SE] = 0 . 66 [0 . 02] , p = 0 . 003; 7T: mean [SE] = 0 . 78 [0 . 03] , p = 0 . 002; two-tailed paired t-test; Figure 4 ) and for each individual subject ( p = 0 . 01 for subject S4 , p = 0 . 005 for all other subjects , permutation test; Figure 5 ) . Remarkably , for the 7T dataset the joint frequency-specific MTF-based model was able to generalize to stimuli not used for parameter estimation . FMRI activity from voxels in primary and non-primary auditory regions reflects the tonotopic organization of neural responses . Therefore , as a control analysis we compared the prediction accuracy of the MTF-based model against the prediction accuracy of a tonotopy model , which incorporates the hypothesis that voxels simply reflect information about the frequency content of the stimuli ( see Materials and Methods and Figure 2 , panel C ) . The tonotopy model performed above chance both at group level ( 3T: mean [SE] = 0 . 62 [0 . 02] , p = 0 . 002; 7T: mean [SE] = 0 . 69 [0 . 03] , p = 0 . 004; two-tailed paired t-test; Figure 4 ) and for each individual subject ( p = 0 . 015 for subject S4 , p = 0 . 005 for all other subjects , permutation test; Figure 5 ) . However , the tonotopy model performed significantly worse than the joint frequency-specific MTF-based model ( 3T: p = 0 . 009; 7T: p = 0 . 007; two-tailed paired t-test ) . The significant improvement of the MTF-based over the tonotopy model indicates that a model accounting for the joint , frequency-specific modulation content of the spectrogram is a better representation of fMRI responses to natural sounds . To assess the relevance of frequency-localization in the encoding of joint spectro-temporal modulations , we trained a model that represents frequency and joint modulation content independently of each other ( see Materials and Methods and Figure 2 , panel A2 ) . The joint frequency non-specific MTF-based model performed above chance both at group level ( 3T: mean [SE] = 0 . 63 [0 . 02] , p = 0 . 004; 7T: mean [SE] = 0 . 71 [0 . 02] , p = 0 . 0003; two-tailed paired t-test; Figure 4 ) and for each individual subject ( p = 0 . 02 for subject S4 , p = 0 . 01 for subject S6 , p = 0 . 005 for all other subjects , permutation test ) . However , the frequency non-specific model performed significantly worse than the frequency-specific MTF-based model ( 3T: p = 0 . 002; 7T: p = 0 . 021; two-tailed paired t-test ) . In order to quantify the contribution of joint selectivity to identification performance , we trained an independent frequency-specific MTF-based encoding model . We modelled each voxel's receptive field as a combination of purely temporal and purely spectral modulation selective filters , operating in a frequency-specific fashion ( see Materials and Methods and Figure 2 , panels B and B1 ) . The independent model performed above chance both at group level ( 3T: mean [SE] = 0 . 63 [0 . 01] , p = 0 . 001; 7T: mean [SE] = 0 . 72 [0 . 02] , p = 0 . 0007; two-tailed paired t-test; Figure 4 ) and for each individual subject ( p = 0 . 015 for subject S4 , p = 0 . 01 for subject S7 , p = 0 . 005 for all other subjects , permutation test ) . However , the independent model performed significantly worse than the joint MTF-based model ( 3T: p = 0 . 012; 7T: p = 0 . 011; two-tailed paired t-test ) . As an additional control , we tested a model that simulates independent selectivity for spectral modulations , temporal modulations and frequency ( see Materials and Methods and Figure 2 , panel B2 ) . The independent frequency non-specific model performed above chance both at group level ( 3T: mean [SE] = 0 . 63 [0 . 02] , p = 0 . 002; 7T: mean [SE] = 0 . 71 [0 . 02] , p = 0 . 0008; two-tailed paired t-test; Figure 4 ) and for each individual subject ( p = 0 . 01 for subject S1 , S4 and S9 , p = 0 . 005 for all other subjects , permutation test ) . However , the independent frequency non-specific model performed significantly worse than the joint frequency-specific MTF-based model ( 3T: p = 0 . 011; 7T: p = 0 . 016; two-tailed paired t-test ) . To investigate the cortical topography of voxels tuning properties , we computed maps of voxels characteristic spectral modulation ( CSM ) , temporal modulation ( CTM ) and frequency ( CF ) . For each feature , the estimated MTF was marginalized across irrelevant dimensions ( i . e . spectral and temporal modulations for CF ) and the point of maximum of the marginal sum was assigned as the voxel's preferred feature value ( see example in Figure 3 ) . We obtained maps of CSM , CTM and CF by color-coding the voxels' preferred values and projecting them onto an inflated representation of the subject's cortex ( see Materials and Methods ) . Maps of CF confirmed the presence of multiple tonotopic gradients in primary auditory regions ( Heschl's gyrus - HG ) and surrounding superior temporal cortex [27] ( Figure S3 and S4 ) . The spatial distribution of voxels CSM and CTM appeared to be more complex and variable across subjects ( Figure 6 for the group and Figure S5 and S6 for all individual subjects ) . However , the group data and the majority of the individual subjects suggested distinct regional sensitivities to modulation frequencies ( see schematic summary in Figure 7 ) . In both hemispheres , clusters with a preference for fine spectral modulations ( high CSM , purple colors ) were primarily and consistently localized along the HG and anterior superior temporal gyrus ( STG ) ( see circles on group maps - Figure 6 ) , while clusters with a preference for coarse spectral modulations ( low CSM , orange color ) were mostly located posterior-laterally to HG , on the planum temporal ( PT ) and on STG ( see squares on group maps – Figure 6 ) . Bilaterally , a preference for slow temporal modulations ( low CTM , orange color ) was found along HG and STG , whereas clusters with a preference for fast temporal modulations ( high CTM , purple ) were observed on the PT , posteriorly to HG and in a region medially adjacent to HG . Supporting the spatial dissociation between spectral and temporal modulation at map level , we found a significant negative correlation between voxels characteristic spectral and temporal modulation ( 3T: mean [SE] = −0 . 19 [0 . 01] , p = 0 . 02; 7T: mean [SE] = −0 . 11 [0 . 01] , p = 0 . 01; group level random effects two-tailed t test , see Materials and Methods ) .
Our results show that the representation of natural sounds in the human auditory cortex relies on a frequency-specific analysis of combined spectro-temporal modulations . By showing superior performance of the joint MTF-based model over the independent model , we have demonstrated that the hypothesis of independent tuning for spectral [16] and temporal modulations [30] is insufficient to account for the representation of natural sounds in the human auditory cortex . Furthermore , the frequency-specificity that we revealed indicates that the organization of the auditory cortex according to frequency extends beyond the representation of the spectral content of incoming sounds . We show that , at least for spectro-temporal modulations , the integration along the whole range of frequencies occurs at a later stage than the extraction of the feature itself . The encoding mechanism that our results support is consistent with a recent study showing that a frequency-specific representation of combined spectro-temporal modulations allows the accurate reconstruction of speech in the human posterior superior temporal gyrus [31] . The present study generalizes these observations to sounds from natural categories other than speech . Furthermore , our results are in line with psychophysics studies showing that tuning for combined spectro-temporal modulations provides a better account of human behavior during the performance of auditory tasks [32] , [33] . Previous neuroimaging studies had examined the processing of spectral and temporal modulations by measuring the tuning to synthetic stimuli with varying spectral modulation frequency , temporal modulation frequency or the combination of the two . This approach suffers from two main limitations . First , natural sounds are complex stimuli with characteristic statistical regularities [6] , [34]–[36] and it has been suggested that the auditory system is adapted to such regularities in order to efficiently encode sounds in natural settings [37] . Even the most complex synthetic stimuli lack both the statistical structure and the behavioral relevance of natural sounds; therefore there is not guarantee that they engage the auditory cortex in processing that is actually used during the analysis of natural sounds . Second , tuning per se only allows indirect inference on cortical encoding mechanisms: proofing a general computational strategy requires building a model that is able to predict brain responses to a broad range of natural stimuli [38] . The approach that we followed in the present study allowed overcoming these limitations , therefore providing direct evidence for a specific encoding mechanism . However , two important caveats should be mentioned . First , by estimating a linear mapping between modulation acoustic space and fMRI responses , we only modeled the linear response properties of voxels . One might argue that because of the linear approximation , the use of natural sounds provides no advantage over synthetic stimuli ( e . g . dynamic ripples ) . However , it has been shown that tuning properties of both auditory [39]–[41] and visual [42] , [43] neurons differ significantly under natural and synthetic stimulus condition and that linear models obtained from natural stimuli predict neurons responses significantly better . This shows that natural and synthetic stimuli activate neurons in a different manner and that , despite being an incomplete description , linear models estimated from responses to natural stimuli may be more accurate . We suggest that this is true also for models of voxels receptive fields . Second , it might be possible that some auditory cortical locations are selective to higher-level sound attributes ( i . e . sound categories ) that co-occur with specific spectro-temporal modulations . As a consequence of this co-occurrence , these locations would then be assigned with a preferred temporal and spectral modulation frequency , only in virtue of their category selectivity . To examine the role of category selectivity on our results , we performed additional analyses on the 7T dataset and tested a model that included categorical predictors together with the original MTF-based model ( Text S1 ) . The results showed that predictions of new sounds do not improve with the inclusion of categorical information ( mean [SE] = 0 . 76 [0 . 03] ) and that estimated CTM and CSM maps do not change ( Figure S8 ) . This analysis suggests that category tuning may result from preference to specific lower level features or combination of features . However , it would be important to further investigate this issue and compare responses and voxels receptive fields obtained with both natural and synthetic sounds ( see [27] for a similar comparison for frequency responses ) . Such an investigation is experimentally challenging , as it would require as many stimuli ( dynamic ripples ) as model parameters used in the present study . However , it could be crucial for understanding the relation between acoustic and perceptual levels of sound representation in the auditory cortex . On the basis of positron emission tomography responses to tone sequences that differed either in the temporal or spectral dimension , Zatorre and Belin [44] reported a left-hemispheric preference for rapid temporal processing and complementary preference in the right hemisphere for fine-grained spectral analysis . While the analyses we conducted cannot exclude that hemispheric differences exist at regional level , our maps - obtained at a much higher spatial resolution and with natural sounds - suggest a more complex spatial pattern of spectral and temporal modulation preference within each hemisphere . The most evident characteristic is that – in both the hemispheres - regions located posterior-laterally to HG ( see squares in Figure 6 and the schematic summary in Figure 7 ) preferably encode coarse spectral information with high temporal precision while regions located along HG or antero-ventrally ( see circles in Figure 6 and the schematic summary in Figure 7 ) preferably encode fine-grained spectral information with low temporal precision . Both the two previous human neuroimaging studies that investigated tuning for combined spectro-temporal modulations with dynamic ripples ( [20] , [21] ) reported a role of anterior auditory regions in the analysis of fine spectral details , which is consistent with our observations , whereas results are less coherent for temporal modulation maps . Again , a direct comparison between maps obtained with dynamic ripples and natural sounds would be required to address this issue . Our results of spatial topographies for CTM and CTF support the view that the auditory cortex forms multiple ( parallel ) representations of the incoming sounds at different spectro-temporal resolutions ( [45] , [46] ) . We suggest that this may be relevant for enabling flexible behavior , as different goal-oriented sound processing may benefit from different types of auditory representations . Importantly , this suggestion can be tested empirically in future experiments and studies where ( natural ) sounds are presented in the context of multiple behavioral tasks . A spectral-temporal resolution “trade-off” analogous to the one reported here has previously been described for neurons in the inferior colliculus of the cat [47] , [48] and is in agreement with the low-pass behavior of the MTF of the human auditory cortex [21] and the psychophysically derived detection thresholds for spectro-temporal modulations [9] . Furthermore , modulation spectra of natural sounds exhibit a similar trade-off , i . e . natural sounds rarely present both high spectral and high temporal modulation frequencies [6] , [10] . A match between stimulus statistics and neuronal response properties is generally interpreted as an evidence for the theory of efficient coding [19] , [36] , [37] , [48] , [49] . Thus , our data provide further support to the idea that the auditory system has adapted in order to efficiently encode the statistical regularities of natural sounds . Besides providing insights into the representation of natural sounds in the human auditory cortex , our results pave the way to future research aiming at testing increasingly complex encoding models of auditory processing . The combination of fMRI and “encoding” techniques has proven to be a successful tool to investigate the representation of natural images in the human visual cortex [24] , [50] , [51] , as well as to predict the brain activity associated with the meaning of words [52] . In the auditory domain , the application of such powerful method has lagged behind . We have recently demonstrated that “encoding” makes it possible to detect the spectral tuning of voxels in the human auditory cortex from fMRI responses to natural sounds [27]–[29] . In the present study , we show that models embedding more complex representations than frequency selectivity can be learned from fMRI activity . The challenge for future studies is to explore more sophisticated voxels receptive field models . Here we only considered voxels tuning along three stimulus dimensions ( frequency , spectral modulations and temporal modulations ) . However , natural sounds vary in a higher dimensional acoustic space and interactions with parameters not considered here might occur . Interestingly , we consistently observed higher prediction accuracy for the 7T compared to the 3T dataset ( Figure 4 ) , despite the fact that at 7T the model was trained and tested on independent sound ensembles ( while different presentations of the same sounds were used for the 3T data set ) . We interpret this difference as a result of the interplay between two important factors , namely the number of stimuli and the functional contrast to noise ratio ( CNR ) . The larger amount of different sounds employed in the 7T experiment has probably increased the variance along the dimensions represented by the model; this , together with the higher CNR and the higher spatial specificity achieved at 7T , has likely led to a more accurate model estimation , which in turn has resulted in higher prediction accuracy . These observations provide important guidelines for the design of future experiments in this framework . It should be mentioned that in our study , accuracy based on percent correct was significantly above chance ( [12 . 5% , 12 . 5% , 16 . 7% , 20 . 8% , 25%] for subjects S6–S10 for the best performing model at 7T; chance = 4 . 2% ) , but still quite small compared to the outstanding results reported in similar encoding studies in the visual domain ( e . g . [24] ) . However , the distribution of ranks was skewed towards 1 ( correct identification ) , indicating that for most sounds the correlation between predicted and measured response was ranked very high ( e . g . second or third ) . The lower percent correct performance for sound identification can be ascribed to a variety of reasons . It might be due to the lower functional CNR , as BOLD responses observed in the auditory cortex are substantially lower than those in the visual cortex , probably because of the effects of the scanner noise [53] . Furthermore , our clustered fMRI acquisition with a silent gap between scans limits the number of sounds used for training/testing the model ( compared e . g . to the number of images in [24] ) . Finally , the model of receptive field based on spectro-temporal modulations might be too simple for allowing distinguishing two acoustically similar sounds ( e . g . two speech sounds ) . Although the proposed combination of high field fMRI with the encoding approach is valuable for testing well-defined hypotheses on sound processing in the human brain , there are intrinsic limitations . A voxel - even at the high spatial resolution achievable with 7T fMRI - samples a large number of neurons and the relation between the measured BOLD signal and the neural activation is only partly understood . Results based on BOLD fMRI ( and thus fMRI encoding ) reflect a complex mixture of neuronal ( spiking and synaptic activity , excitation , inhibition ) as well as neurovascular phenomena . In particular , neural inhibition may be associated with both positive and negative BOLD , depending on the specific neural network configuration [54] . Understanding the neuronal dynamics underlying our fMRI observations would thus require combining electrophysiological ( at single-cell and neuronal population level ) and fMRI investigations in animal models [55] and/or humans [40] . In summary , our study represents a first demonstration of how fMRI data and “encoding” techniques can be successfully combined to test competing computational models of auditory processing and to concurrently estimate response properties of cortical locations along multiple dimensions within an ecologically valid framework . Also , by using a biologically inspired computational model , we pave the way for linking electrophysiology in animals and non-invasive research in humans .
The Ethical Committee of the Faculty of Psychology and Neuroscience at Maastricht University and the Institutional Review Board for human subject research at the University of Minnesota granted approval for the study at 3T and 7T respectively . Subjects , stimuli , experimental design , MRI parameters , and data preprocessing have been reported in previous publications from our group [27]–[29] ( see Text S1 ) . In the following , the most relevant details of the experimental design will be briefly described . We used 60 ( 168 ) recordings of natural sounds for the 3T ( 7T ) experiment . Stimuli included human vocal sounds ( both speech and non-speech , e . g . , baby cry , laughter , coughing ) , animal cries ( e . g . , dog , cat , horse ) , musical instruments ( e . g . , piano , flute , drums ) , scenes from nature ( e . g . , rain , wind , thunder ) , and tool sounds ( e . g . , keys , scissors , vacuum cleaner ) . Sounds were sampled at 16 kHz and their duration was cut at 1000 ms . Sound onset and offset were ramped with a 10 ms linear slope , and their energy ( RMS ) levels were equalized . The 3T and 7T experiments consisted of 3 and 8 runs , respectively; in the 3T ( 7T ) experiment , each run lasted approximately 25 ( 10 ) minutes . In the 7T experiment , data were subdivided into six train runs and two test runs . In the train runs , 144 of the 168 stimuli were presented with 3 repetitions overall ( i . e . each sound was presented in 3 of the 6 train runs ) . The remaining 24 sounds were presented in the test runs and repeated 3 times per run . Sounds were presented in the silent gap between acquisitions with a randomly assigned inter-stimulus interval of 2 , 3 , or 4 TRs - plus an additional random jitter . Zero trials ( trials where no sound was presented; 10% of the trials in the 3T experiment; 6% ( 5% ) of the trials in train ( test ) runs in the 7T experiment ) , and catch trials ( trials in which the sound which was just heard was presented; 6% of the trials in the 3T experiment; 6% ( 3% ) of the trials in train ( test ) runs in the 7T experiment ) were included . Subjects responded with a button press when a sound was repeated . Catch trials were excluded from the analysis . The stimulus representation in the modulation space was obtained as the output of a biologically inspired model of auditory processing [22] , that explicitly encodes the modulation content of a sound spectrogram . The auditory model consists of two main components: an early stage that accounts for the transformations that acoustic signals undergo in the early auditory system , from the cochlea to the midbrain; and a cortical stage that simulates the processing of the acoustic input at the level of the ( primary ) auditory cortex . The spectral analysis performed by the cochlea is mimicked by a bank of 128 overlapping bandpass filters with constant-Q ( Q10 dB = 3 ) , equally spaced along a logarithmic frequency axis over a range of 5 . 3 oct ( f = 180–7040 Hz ) . The output of each filter enters a hair cell stage , where it undergoes high-pass filtering , optional non-linear compression and low-pass filtering . A midbrain stage models the enhancement of frequency selectivity as a first-order derivative with respect to the frequency axis , followed by a half-wave rectification . Finally , a short-term temporal integration ( time constant τ = 8 ms ) accounts for the loss of phase locking observed in the midbrain . The auditory spectrogram generated by the early stage is further analyzed by the cortical stage , where neurons are modeled as 2-dimensional ( 2D ) modulation selective filters that are tuned to a specific combination of spectral and temporal modulations , and operate over a limited range of frequencies along the tonotopic axis . These filters have constant Q and are directional , i . e . they respond either to upward or downward frequency sweeps . Computationally , the cortical filter bank performs a complex wavelet decomposition of the auditory spectrogram . The magnitude of such decomposition yields a phase-invariant measure of modulation content . Ultimately , the model's output is a multi-resolution representation of the spectrogram envelope as a function of time , frequency , spectral and temporal modulations , and directionality . We derived the auditory spectrogram and its modulation content using the “NSL Tools” package ( available at http://www . isr . umd . edu/Labs/NSL/Software . htm ) and customized Matlab code ( The MathWorks Inc . ) . Pilot analyses showed that model performance was not significantly affected by changes in the parameters of the early stage . Accordingly , parameters for the spectrogram estimation were fixed ( i . e . not estimated in the fitting procedure ) and set as described above and in [22] . The modulation content of the auditory spectrogram was computed through a bank of 2D modulation selective filters tuned to spectral modulation frequencies of Ω = [0 . 5 , 1 , 2 , 4] cyc/oct and temporal modulation frequencies of ω = [1 , 3 , 9 , 27] Hz . The filter bank output was computed at each frequency along the tonotopic axis and then averaged over time . In order to avoid overfitting , a reduced modulation representation was obtained as follows ( 3T: 3 tonotopic frequencies×4 spectral modulations×4 temporal modulations = 48 parameters to learn; 7T: 8 tonotopic frequencies×4 spectral modulations×4 temporal modulations = 128 parameters to learn; note that we chose a different number of parameters for the 3T and 7T datasets due to the different number of stimuli used for model's estimation - 60 and 144 stimuli , respectively ) . First , the time-averaged output of the filter bank was averaged across the upward and downward filter directions ( note that this corresponds to assuming that sweep direction does not affect voxels activation levels ) . Then , we divided the tonotopic axis in ranges with constant bandwidth in octaves and averaged the modulation energy within each of these regions . We defined three frequency ranges in the 3T experiment and eight in the 7T experiment . The above processing steps were applied to all stimuli , resulting into an [S×N] feature matrix F of average modulation energy , where S is the number of sounds , and N is the number of features in the reduced modulation representation . The stimuli representation in the frequency space was obtained using only the input stage of the auditory model . The spectrogram was computed at 128 logarithmically spaced frequency values ( f = 180–7040 Hz ) and averaged over time . In the 3T experiment , we generated a reduced frequency representation in order to restrain the effects of overfitting ( note that in the 7T experiment the number of observations in the train set was already higher than the number of parameters to estimate ) . We divided the tonotopic axis in 48 bins with constant bandwidth in octaves and averaged the frequency content within each of these regions . We chose 48 bins in order to have the same number of parameters for both the MTF-based and the tonotopy model . The above processing steps were applied to all stimuli , resulting into an [S×N] feature matrix F of time-averaged frequency content , where S is the number of sounds , and N is the number of frequency bins . We generated the non-localized modulation representation by averaging the frequency-specific joint representation along both time and frequency ( this is similar to performing a 2D Fourier transform of the spectrogram ) . This resulted in a representation with 16 features ( 4 temporal modulations×4 spectral modulations ) . However , frequency specific information is indeed reflected in voxels' activity [26] , [27]; therefore , we concatenated the modulation representation with a tonotopic representation obtained as described above for the tonotopy model . We employed 32 frequency bins for the 3T dataset and 112 for the 7T dataset , resulting in a final representation with 48 and 128 features , respectively . We generated the independent modulation representation by filtering the auditory spectrogram with one-dimensional purely spectral and purely temporal modulation filters . Filters were tuned to spectral modulation frequencies of Ω = [0 . 5 , 1 , 2 , 4] cyc/oct and temporal modulation frequencies of ω = [1 , 3 , 9 , 27] Hz . The output of each filter bank was averaged over time and within frequency ranges with constant bandwidth in octaves . In order to have a representation with the same number of features as for the joint model , we defined 6 frequency ranges in the 3T experiment and 16 in the 7T experiment . Finally , the outputs of the purely spectral and purely temporal filter banks were concatenated , resulting in a representation with 48 features for the 3T dataset ( 6 tonotopic frequencies×4 temporal modulations+6 tonotopic frequencies×4 spectral modulations ) and 128 for the 7T dataset ( 16 tonotopic frequencies×4 temporal modulations+16 tonotopic frequencies×4 spectral modulations ) . The above processing steps were applied to all stimuli , producing an [S×N] feature matrix F of average modulation energy , where S is the number of sounds , and N is the number of features . We generated the non-localized independent representation by averaging across frequency the frequency-specific independent representation . This resulted in a representation with 8 features ( 4 temporal modulations+4 spectral modulations ) . The final model was obtained by concatenating the modulation representation with a tonotopic representation obtained as described above for the tonotopy model . We employed 40 frequency bins for the 3T dataset and 120 for the 7T dataset , resulting in a final representation with 48 and 128 features , respectively . In the 7T experiment , independent train and test runs involving two completely distinct sound sets were used to train and assess the model , whereas leave run out cross-validation was performed for the 3T dataset ( the final model parameters and the overall prediction accuracy were computed as the average across cross validations ) . For each voxel i , the response vector Yi [ ( S×1 ) , S = number of sounds] was obtained in two steps . First , a deconvolution analysis with all stimuli treated as a single condition was used to estimate the hemodynamic response function ( HRF ) common to all stimuli . Then , using this HRF and one predictor per sound , we computed the beta weight of each sound [56] . Further analyses were performed on voxels with a significant response to the sounds ( p< . 05 , uncorrected in order not to be too stringent at this stage of the process ) within an anatomically defined mask , which included HG , HS , PT , PP , and STG . The fMRI activity Yi [Strain×1] at voxel i was modeled as a linear transformation of the feature matrix Ftrain [Strain×N] plus a noise term n [Strain×1] as follows: ( 1 ) where Strain is the number of sounds in the training set , and Ci is an [N×1] vector of model parameters , whose elements cij quantify the contribution of feature j to the overall response of voxel i . Note that Equation 1 does not include a constant term as columns of matrices Ftrain and Yi were converted to standardized z-scores . Z-scoring of the features and responses does not affect the expressive capacity of the linear regression model . However , in a regularized regression framework like ridge regression ( see below ) , z-scoring does affect the estimated model parameters ( weights ) . In the present study , z-score was performed because the energy content of natural sounds varies on different scales across frequencies and modulations . As a consequence , the estimated model parameters would not be comparable without performing the z-score normalization . The solution to Equation 1 was computed using ridge regression [57] . The regularization parameter λ was determined independently for each voxel by automatically inspecting the stability of the ridge trace , that is changes in the parameter estimates as a function of λ [58] . Namely , parameter estimates were obtained for a range of increasing λ values [λ1 , λ2 , … , λp] , and the regularization parameter was set at the value λ* where all parameter estimates consistently changed less than 20% of their initial value : ( 2 ) The inspection of the ridge trace represented an advantage in terms of trade-off between accurate model estimation and computational load . Namely , we observed that the selection of the regularization parameter via cross validation was computationally slower , while not yielding any significant improvement on models performance . We quantified model's prediction accuracy by performing a sound identification analysis [24] . Namely , we used the fMRI activity patterns predicted by the estimated models to identify which sound had been heard among all sounds in the test set . Because model parameters were estimated in z-score units , we converted to standardized z-score the columns of the feature and response matrices for the stimuli in the test set . Given the trained model [N×V] ( where V is the number of voxels ) , and the feature matrix Ftest [Stest×N] for the test set , the predicted fMRI activity [Stest×V] for the test sounds was obtained as follows: ( 3 ) Then , for each stimulus si we computed the correlation between its predicted fMRI activity [1×V] and all measured fMRI responses [1×V] , j = 1 , 2 , … , S . The rank of the correlation between predicted and observed activity for stimulus si was selected as a measure of the model's ability to correctly match with its prediction . The matching score m for stimulus si was obtained by normalizing the computed rank between 0 and 1 as follows ( m = 1 indicates correct match; m = 0 indicates predicted activity pattern for stimulus si was least similar to the measured one among all stimuli ) : ( 4 ) Normalized ranks were computed for all stimuli in the test set , and the overall model's accuracy was obtained as the mean of the matching scores across stimuli . Note that the metric we used ( Eq . 4 ) generalizes the more straightforward percent correct , a rank-based metric that considers only stimuli that are ranked first , i . e . stimuli that are correctly identified . Percent correct is a comprehensive metric when models identify new stimuli with high accuracies ( close to 100% ) . As this was not the case in our data ( see Discussion ) , it is informative to look at the whole distribution to assess the degree of incorrect identification . Statistical significance of the observed accuracy was assessed with permutation testing . Specifically , the empirical null-distribution of accuracies was obtained by randomly permuting ( P = 200 permutations ) the stimulus labels ( i . e . S in matrix Y ) and repeating the training and testing procedures . In order to preserve the spatial correlations among cortical locations , the same permutations were applied to all voxels . The regularization parameter was constant across permutations and was set to the value derived when the model was estimated on the unpermuted set of responses . When compared by means of t-test , accuracies were converted to z-scores via Fisher's transformation in order to reduce deviations from normality . For all voxels , response profiles for temporal modulation , spectral modulation and frequency were computed as marginal sums of the estimated stimulus-activity mapping function C of the joint frequency-specific MTF-based model , as follows: ( 5 ) ( 6 ) ( 7 ) where tMTF and sMTF are the temporal and spectral modulation transfer functions , respectively , and fTF is the frequency transfer function . Voxels characteristic values ( CTM , CSM , CF ) were defined as the point of maximum of the tMTF , sMTF and fTF , respectively . A continuous representation of preferred values was obtained by spatial smoothing using a 2-neighbor ( 3-neighbor ) voxels filter for the 3T ( 7T ) dataset . Cortical maps were generated by color-coding the voxels' preferred values and projecting them onto an inflated representation of the subject's cortex . Individual maps were subsequently transformed to functional cortex based aligned ( fCBA ) space ( see below ) where group maps were obtained as the mean across subjects . Only voxels that had been included in the analysis of at least 3 out of the 5 subjects were considered when computing group maps . To assess the reliability of the estimated voxels tuning preference , we computed the signal-to-noise ratio ( SNR ) of the MTFs estimates via a bootstrap resampling procedure applied to all individual subjects ( see Text S1 and Figure S7 ) . For each subject , we computed the Spearman's rank correlation coefficient between voxels characteristics CSM and CTM ( prior to spatial smoothing ) . In order to take into account any possible bias introduced by the model's estimation procedure , we derived the empirical expected value of no correlation by computing the correlation coefficient between voxels CSM and CTM as obtained after permuting the stimulus labels ( see above ) . Statistical significance of the Fisher-transformed correlation coefficients was assessed via a group level random effect two-tailed t test . Additionally to the main experiments , localizer data were collected as responses to amplitude modulated tones ( see Text S1 ) . Tonotopy maps were computed with best-frequency mapping [26] , and resulting maps were used for fCBA [59] as follows . In each subject and hemisphere , we delineated the low frequency region consistently present in the vicinity of Heschl's gyrus as region of interest . FCBA was partially driven by this functional region ( weighting decreased over iterations ) , and partially by anatomical information ( weighting increased over iterations; [60] ) . The resulting alignment information was used for calculating and displaying group cortical maps .
|
How does the human brain analyze natural sounds ? Previous functional neuroimaging research could only describe the response patterns that sounds evoke in the human brain at the level of preferential regional activations . A comprehensive account of the neural basis of human hearing , however , requires deriving computational models that are able to provide quantitative predictions of brain responses to natural sounds . Here , we make a significant step in this direction by combining functional magnetic resonance imaging ( fMRI ) with computational modeling . We compare competing computational models of sound representations and select the model that most accurately predicts the measured fMRI response patterns . The computational models describe the processing of three relevant properties of natural sounds: frequency , temporal modulations and spectral modulations . We find that a model that represents spectral and temporal modulations jointly and in a frequency-dependent fashion provides the best account of fMRI responses and that the functional specialization of auditory cortical fields can be partially accounted for by their modulation tuning . Our results provide insights on how natural sounds are encoded in human auditory cortex and our methodological approach constitutes an advance in the way this question can be addressed in future studies .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2014
|
Encoding of Natural Sounds at Multiple Spectral and Temporal Resolutions in the Human Auditory Cortex
|
Hantavirus Cardiopulmonary Syndrome ( HCPS ) is a disease caused by Hantavirus , which is highly virulent for humans . High temperatures and conversion of native vegetation to agriculture , particularly sugarcane cultivation can alter abundance of rodent generalist species that serve as the principal reservoir host for HCPS , but our understanding of the compound effects of land use and climate on HCPS incidence remains limited , particularly in tropical regions . Here we rely on a Bayesian model to fill this research gap and to predict the effects of sugarcane expansion and expected changes in temperature on Hantavirus infection risk in the state of São Paulo , Brazil . The sugarcane expansion scenario was based on historical data between 2000 and 2010 combined with an agro-environment zoning guideline for the sugar and ethanol industry . Future evolution of temperature anomalies was derived using 32 general circulation models from scenarios RCP4 . 5 and RCP8 . 5 ( Representative greenhouse gases Concentration Pathways adopted by IPCC ) . Currently , the state of São Paulo has an average Hantavirus risk of 1 . 3% , with 6% of the 645 municipalities of the state being classified as high risk ( HCPS risk ≥ 5% ) . Our results indicate that sugarcane expansion alone will increase average HCPS risk to 1 . 5% , placing 20% more people at HCPS risk . Temperature anomalies alone increase HCPS risk even more ( 1 . 6% for RCP4 . 5 and 1 . 7% , for RCP8 . 5 ) , and place 31% and 34% more people at risk . Combined sugarcane and temperature increases led to the same predictions as scenarios that only included temperature . Our results demonstrate that climate change effects are likely to be more severe than those from sugarcane expansion . Forecasting disease is critical for the timely and efficient planning of operational control programs that can address the expected effects of sugarcane expansion and climate change on HCPS infection risk . The predicted spatial location of HCPS infection risks obtained here can be used to prioritize management actions and develop educational campaigns .
Global average temperatures are projected to increase between 1 . 7 and 4 . 8°C by the end of this century [1 , 2] , with potential effects on human health , including mortality from extreme heat and cold , and changes in the ecology of infectious diseases [3–5] . Climatic variability and extreme weather events have profound impacts on infectious diseases since fluctuations in temperature and precipitation influence both infectious agents ( such as protozoa , bacteria , and viruses ) and population dynamics of their vectors ( such as mosquitoes , ticks , and rodents ) [3 , 6–8] . Outbreaks of some diseases such as Ross River virus disease [9] , malaria [10] , meningitis [11] and Hantavirus Cardiopulmonary Syndrome ( HCPS ) [12] have been associated with climate anomalies . At the same time , increasing evidence suggests that land cover and land use change affect disease incidence by altering the interactions , abundance , and movement patterns of hosts , vectors , and people [13 , 14] . For instance , outbreaks of Hantavirus , Lyme disease and tick-borne encephalitis have been associated not only with climate-related changes in the density of host rodent and tick populations [15–17] , but also with shifts in the extent and type of land use [17–23] . Hantavirus ( Bunyaviridae ) is a virus transmitted by small mammals [17] which causes two syndromes in humans: Hantavirus Cardiopulmonary Syndrome ( HCPS ) , restricted to the Americas , and hemorrhagic fever with renal syndrome ( HFRS ) present in Eurasia and Africa [24 , 25] . HCPS was first identified in 1993 in both the United States and Brazil [26 , 27] and exhibits lethality rates as high as 50% [26 , 28] . Unlike HFRS , a vaccine is not available for HCPS . Transmission to humans occurs through inhalation of the aerosolized form present in the urine , saliva and feces of infected rodents [29–31] . Climate conditions can influence Hantavirus host population abundance and disease transmission dynamics [32] . A number of studies in arid and semi-arid region of the U . S . have uncovered a positive association between precipitation , population size of rodent hosts and prevalence of Hantavirus [33–36] . Anomalously high precipitation increases vegetation growth , boosting rodent densities and enhancing the probability of encounters between humans and infected rodents and consequently Hantavirus transmission [12 , 37] . Temperature can influence rodent abundance and disease risk by altering vegetation growth [38] , reproduction and survival rates of small rodents [38–40] , as well as the time the virus remains infectious in the environment [41] . The capacity of Hantavirus to survive outside its host plays a critical role in transmission dynamics [41] . High temperatures have been associated with more frequent Hantavirus outbreaks [16 , 37 , 42 , 43] , most likely because high temperature leads to greater aerosolization of the virus and higher rates of inhalation by both humans and rodents [29 , 31] . There is evidence that variation in temperature , but not precipitation , affect HCPS risk in Brazil [32] . Sugarcane plantations may be also associated with increases in Hantavirus infection risk [19 , 32 , 44] . Experimental studies have shown that small mammal populations are frequently food-limited [45]; thus the presence of an abundant , highly nutritious food resource , such as sugarcane , with yields as high as 120 tons·year·ha−1 [46] , might allow the increase and maintenance of large populations of these species , relative to other land uses , either natural or agricultural [19 , 47 , 48] . Furthermore , sugarcane offers protective cover for feeding , burrowing and breeding activities , throughout the year [49] . Many developing countries are expanding sugarcane plantation areas to produce biofuel , as a strategy to reduce their dependence on petroleum , to increase opportunities for the agricultural sector , and to mitigate global warming [50] . In Brazil , the creation of the pro-alcohol program , developed to replace a significant percentage of fossil-fuel consumption with ethanol produced from sugarcane [51] , was triggered by an increase of 428% in oil prices in 1973 [52] . This program and the recent interest in alternative energy sources have fostered an expansion in the extent of sugarcane cultivation , making the country the world’s leader in ethanol production [53] and sugarcane ( ~ 490 million tons per year ) exports [54] . The majority of this production ( ~74% ) comes from the southeastern region , with the state of São Paulo producing 60% of the total yield [55 , 56] . The combined consequences of bio-energy expansion and climate variability and change on Hantavirus infection risk remain unexamined . Understanding how these factors impact infectious disease risk is essential to fully evaluate the actual costs of the biofuel programs and is critical for timely and efficient planning of operational control programs . In this paper we analyze how sugarcane expansion and temperature changes under two climate scenarios can potentially influence HCPS risk in the state of São Paulo by 2050 . To do so , we relied on a baseline model we previously developed [32] , which evaluated the effects of landscape , climate and social predictors , including historical climate and sugarcane as predictor variables , on Hantavirus risk between 1993 and 2012 . In this paper , we used climate and sugarcane data derived from various scenarios ( see methods section ) to test the independent and combined effect of these two factors on HCPS risk . We hypothesize that HCPS incidence will show an increase under all scenarios because both changes in climate and sugarcane expansion are expected to increase HCPS risk through their positive effects on rodent abundance and virus survival and aerosolization , and that their combined effect can exacerbate their individual impacts .
We focused our analyses on the state of São Paulo , the wealthiest Brazilian state , where HCPS was first identified in 1993 and where the risk of disease increase is particularly high , due to both sugarcane expansion and climate change . São Paulo state is located in southeastern Brazil , in an area of approximately 248 , 210 km2 ( Fig 1 ) , and has a population of about 42 million , representing 21% of the Brazilian population [57] . The probability of Hantavirus infection risk in the state of São Paulo was calculated as a function of landscape , social and climatic factors using a Bayesian model , and is described in detail in [32] . Given that Hantavirus exhibits high host specificity , with each region having different reservoir host species and virus strains [73] , Hantavirus transmission risk was modeled separately for Atlantic forest and Cerrado biomes . Although some geographic overlap occurs [74] , Araraquara virus ( ARAV ) is the dominant Hantavirus in Cerrado [74] , whereas Juquitiba ( JUQV ) is the dominant one in Atlantic forest [74] . Municipalities were assigned to Cerrado or Atlantic forest if >50% of their surface area fell inside one of the biome . The biome distribution map was obtained from IBGE ( www . ibge . gov . br ) . HCPS infection risk was predicted using a Bernoulli distribution and the model ( baseline model ) contained 7 predictor variables as fixed covariates: proportion of sugarcane , proportion of native vegetation cover , density of native vegetation patches , HDI , mean annual temperature ( °C ) , total annual precipitation ( mm ) , and rural male population >14 years old [32] . Risk was defined as the annual probability of HCPS infection . Municipality was included as a random effect to account for differences among these administrative units that are not captured in the fixed covariates [32] . To facilitate interpretation , all estimated parameters were standardized by centering them on their mean and dividing by two standard deviations [75] . We tested models of raw HPS incidence as well as model residuals for spatial autocorrelation using Moran’s I . We used the spatial contiguity matrix based on the Queen´s case neighborhood relation and treated each year separately . This test is commonly used and accepted as a fair evaluation of spatial autocorrelation and dependence [76] , especially in disease studies [77 , 78] . For all models and most years , we found no spatial autocorrelation , justifying the use of a non-spatial model . To evaluate changes in Hantavirus infection risk , estimated probability of HCPS infection under current conditions ( baseline model ) was compared to the predicted probability under five scenarios: two possible future climate change scenarios ( RCP4 . 5 and RCP8 . 5 ) , one possible sugarcane expansion scenario , and the combinations of each climate scenarios and sugarcane expansion ( RCP4 . 5 + sugarcane; RCP8 . 5+ sugarcane ) ( Table 1 ) . To estimate the predicted probability of HCPS infection under the five future scenarios we used the parameter estimates from the baseline model [32] and used sugarcane and climate data derived for each scenario . We made the simplifying assumption that the biological relationships governing disease transmission would remain largely unchanged over the estimation period . Uncertainty was measured using lower and upper limits of risk estimates for each scenario , derived from the 2 . 5% and 97 . 5% quantiles of the baseline model parameters for sugarcane and temperatures . Results are presented in S3 and S4 Figs . The covariates percent of native vegetation cover , number of patches , total annual precipitation , human development index , and people at risk were assumed to be the same as the covariates from the previous year , available from the baseline model ( year of 2012 ) , and were kept constant for the predictions . These are reasonable assumptions considering that trends of urban-rural migration in São Paulo are constant [79] , deforestation has been drastically reduced in the state [62] and precipitation is not relevant for HCPS risk [32] . Despite the increase in sugarcane mechanization , manual harvest is still necessary and present in some parts of the process [80] . Additionally , skilled workers are replacing unskilled workers , while temporary workers are still being hired at the same rates as before [81] . The number of people employed in sugarcane areas is not diminishing with sugarcane mechanization . To obtain a clear view of the probability of change in Hantavirus risk , we created a map with the change in infection risk for each scenario that was calculated using the difference between the current Hantavirus risk and the predicted risk for each scenario . We also used model simulations to generate a map of Hantavirus infection risk for the State of São Paulo for each scenario , where Hantavirus infection risk is classified as small ( <5% ) , medium ( ≥5 and ≤10% ) , high ( ≥10 and ≤ 20% ) and extremely high ( ≥20% ) . We considered that a municipality with a risk higher than 5% should be a target for preventive measures due to the high disease lethality ( maps are shown in supplementary material—S5 Fig ) . By associating the estimated probability of HCPS infection risk generated for each scenario ( baseline model and the five future scenarios ) with the at risk population for each municipality ( rural men older than 14 years ) , we predicted current and future human exposure to HCPS . We also calculated the percent increase in the number of people that could be infected in each scenario , by comparing each scenario with the baseline ( Table 2 ) .
According to our sugarcane expansion scenario , this crop cover will increase to ~30% on average in the state of São Paulo by 2050 . Sugarcane area will increase from 26% to 34% in the Cerrado region ( 11 , 200 to 14 , 500 ha ) and from 23% to 31% in the Atlantic Forest region ( 8 , 200 to 11 , 100 ha ) ( S6 Fig ) . Considering climate change scenarios , there is a general consensus among the 32 models evaluated , for both RCP4 . 5 and RCP8 . 5 scenarios , in the direction of the projected temperature change for the state of São Paulo , with both experiments presenting increases . Also , RCP8 . 5 presents a smaller variation and lower standard deviation between the 32 models analyzed than RCP4 . 5 models , especially from 2013 to 2050 ( Fig 2 ) . After 2050 , the anomalies of RCP8 . 5 become larger than those from the RCP4 . 5 experiment , showing larger increases in temperature anomalies . Under current conditions , the state has an average Hantavirus infection risk of 1 . 3% , with 6% of the municipalities classified as high risk ( HCPS ≥ 5% ) . Hantavirus infection risk increases under all scenarios evaluated ( 0 . 25% to 0 . 37% ) ( Table 1 ) . Sugarcane expansion is the scenario that predicted the smallest increase in Hantavirus risk , with a 0 . 25% increase on average . The most pronounced changes are expected to occur in the west and mid-west parts of the state where almost all municipalities exhibit an increase of 1 . 5% in HCPS infection risk ( Fig 3B ) . Also , sugarcane scenario will lead to a risk greater than 5% for HCPS for about 6 . 6% of all municipalities ( 43 municipalities ) . Projected temperature anomalies for both climate change scenarios predicted similar average increases of HCPS in the state ( 0 . 35% for RCP4 . 5 and 0 . 37% for RCP8 . 5 ) , with larger increases concentrated in the northeast region , but with RCP4 . 5 predicting smaller increases than RCP8 . 5 ( Fig 3C and 3D ) . Moreover , there is a significant increase in the risk of infection for some municipalities that already had a high risk , especially in the mid-west region , with HCPS risk reaching 52 . 3% in RCP4 . 5 and 52 . 7% in RCP8 . 5 . Under the RCP4 . 5 scenario there were 42 municipalities ( ~6 . 5% of the state ) with a HCPS risk greater than 5% , while under the RCP8 . 5 scenario there were 44 municipalities ( ~6 . 9% of the state ) . The HCPS risk simulated using the temperature anomalies ± 1 standard deviation showed that the uncertainty of models simulations is small , and that disease risk is similar for all three experiments ( mean temperature anomalies , mean temperature anomalies + 1 standard deviation and mean temperature anomalies—1standard deviation ) . Therefore , our confidence interval of predictions due to temperature change analysis is narrow , showing similar and consistent trends ( S2 Fig ) . Additionally , the confidence interval of risk estimates for most of municipalities is narrow , except for some municipalities , where upper limits are high ( S3A and S3B Fig ) . When combining climate change scenarios and sugarcane expansion , the average increase and the maximum HCPS risk for the state is the same as under the climate change scenarios alone ( RCP4 . 5 and RCP8 . 5 ) , showing that there is no additional effects between temperature and sugarcane . However , the increase in Hantavirus risk became more homogeneous throughout the state when considering the combined sugarcane-climate change ( Fig 3E and 3F ) , with the inclusion of ~7% of the municipalities of the state with HCPS risk greater than 5% . When we consider the number of people that can be infected by HCPS ( based on the number of population at risk ) , the sugarcane expansion scenario alone presents an increase of 20% . For RCP4 . 5 and RCP8 . 5 temperature scenarios alone and combined with sugarcane expansion the number of people is the same , presenting an increase of 31% and 35% respectively ( Table 2 ) .
Our results confirmed previous studies showing that increases in the amount of sugarcane can augment HCPS risk [32 , 44 , 83] . Our scenario predicted an increase in ~6 , 000 ha of land occupied with sugarcane on average , for both the Cerrado and Atlantic forest regions , until 2050 , forecasting increases of ~15% in HCPS risk . This expansion can be considered small , since the area planted with sugarcane in the state has tripled from 1990 to 2010 , increasing from 3 , 000 to 9 , 000 ha on average for the entire state [56 , 84] . Over this same time period HCPS risk in the state has also increased almost four times ( 382% ) from 0 . 34 to 1 . 3% . Therefore , the increase in disease risk , predicted by our model and according to the expansion of sugarcane , is concordant with the historical increase in risk experienced from 1993 to 2012 . This increase in disease risk , without any change in temperature , is on average low , but can be as high as 6 . 6% in some municipalities . Overall our sugarcane expansion scenario predicts an increase of 20% in the number of people that can acquire HCPS . The main underlying mechanism to explain this pattern is that sugarcane provides a highly nutritious food , leading to increased recruitment and a rapid population growth of rodents [85] . Sugarcane plantations are a suitable habitat for these generalist rodent species , as Hantavirus reservoirs , supporting greater abundances of rodents than other ecosystems , whether natural or agricultural [47] , with sugarcane becoming a predominant part of their diets [86] . Land-use changes also indirectly influence local temperature [87] and alter albedo and evapotranspiration , which can directly influence local climate [88] . Sugarcane plantations have cooler temperatures and more moisture than pasture and other crops , being micro-climatically more similar to areas of natural vegetation near to the soil level [87] . This microclimate changes may contribute to the increase in HCPS risk , since it makes sugarcane an even better habitat for rodents . This climate aspect can also affect the indirect path of transmission , extending the time the virus remains infectious in this environment and augmenting HCPS risk , since virus inactivation happens only in dry conditions and above 37°C [41] . Climate change scenarios predict larger increases in HCPS risk when compared with sugarcane expansion alone . Increases in temperature may be more important than sugarcane expansion , because temperature interacts with disease transmission through multiple mechanisms . Temperature positively affects vegetation growth [36 , 89] , leading to increases in the abundance of reservoir rodent species , since small mammal populations are food-limited [45] . Temperature also affects reproduction and survival of small rodents [36 , 40] , which may have a positive or negative effect depending on the magnitude of temperature change [36 , 90] . In addition , temperature directly affects process of HCPS transmission , determining virus survival and aerosolization in the environment [41] . There is a lack of studies involving reservoir rodent species and Hantavirus related to HCPS and climate variables , but for HFRS , mild temperatures ( 10–25°C ) are most favorable for breeding of reservoirs rodents [91] and for the time the virus remains infectious in the environment [41] . Increases in temperature lead to greater aerosolization of the virus and higher rates of inhalation by both humans and rodents [92] . In this way , increases in temperature may have a positive effect on reservoir rodent abundance and virus survival and aerosolization until reaching a certain threshold ( around 40°0°CC ) from where temperature will exert a negative effect . The relatively low magnitude of the effect of climate change on HCPS risk in our study maybe explained by the fact that temperature anomalies , until 2050 , can be considered small and similar for both RCP4 . 5 and RCP8 . 5 , with larger increases being observed only after 2050 . Nevertheless , it is important to highlight that increases in temperature anomalies lead to increase in HCPS risk , though small , in all the 645 municipalities of the state of São Paulo . Therefore , higher increases for disease risk are expected after 2050 if carbon emissions are not controlled and climate change mitigation actions are not successful . Individual evaluation of climate and sugarcane could have resulted in a better understanding of the individual contributions of each factor on disease risk . However , evaluating these scenarios together is a more realistic approach , given that they will occur and act together . Sugarcane expansion and temperature anomalies together showed no additionality , predicting the same average increase in HCPS risk when compared to climate change scenarios alone . This may have happened because there are multiple mechanisms through which temperature influences HCPS risk , some of which overlaps sugarcane mechanisms ( i . e . , effect on rodent densities ) . Particularly , even in conditions where rodent abundances and prevalence are high , if temperature conditions are not ideal for virus survival and aerosolization , transmission to humans will not occur . Therefore , the ability of the virus to survive outside the host is critical for the transmission within rodent populations and to humans , with temperature being one of the determining factors of this survival . This effect , may have contributed to the lack of additionality between temperature and sugarcane , since sugarcane effects will only occur when temperature conditions are also adequate . Land cover and land use change are at the origin of the outbreaks of Hantavirus and can also be an important component to reduce or mitigate its spread . Given that temperature increase will lead to increases in HCPS risk , forest restoration can be an alternative to attenuate the effects of higher temperature on HCPS risk for three main reasons . First , forest regrowth , especially in tropical regions , can sequester atmospheric carbon , absorbing about 30% of all CO2 emissions from fossil fuel burning and net deforestation [88 , 93] , contributing to climate change mitigation . Second , forest regeneration can mitigate the creation of warmer and drier climates in agricultural systems [88] , reducing the ideal conditions for hantavirus survival . Third , increasing forest cover could also reduce HCPS risk arising from sugarcane expansion , since it would lead to increased suitable habitat for habitat specialist species , leading to a more diverse community , with decreased abundance of habitat generalist species [94] , such as hantavirus reservoir species . We note that the use of ethanol from sugarcane as a gas substitute leads to a very important reduction in greenhouse gases emissions , which can reach up to 85% [95 , 96] . This is mostly due to the fact that they replace fossil fuels [97] , and sequester carbon through the growth of the feedstock [98 , 99] , especially when pastures are converted to sugarcane fields and managed without fire [95] . The use of ethanol from sugarcane , produced in landscapes with a large amount of connected forest cover , could ameliorate disease risk , since it would increase diversity community , diminishing reservoir rodent abundance , and would contribute to climate mitigation . Public health costs will also increase under the expected increase in temperature and sugarcane expansion . At least part of these costs should be factored into sugarcane production , including expenditures associated with rodent control , education and preventive campaigns targeting how to avoid virus inhalation and contact with infected rodents excreta . These control measures are likely to yield additional benefits since rodents are considered major pests of this crop [86] , leading to a loss of 825 . 000 tons of sugarcane in one year in India [100] . States and municipalities considering sugarcane expansion should also plan for costs involved with educational campaigns and preventive measures , for example , educating workers and residents from rural areas about how to avoid Hantavirus inhalation and contact with infect rodents excreta . This could be crucial to avoid disease propagation to places where HCPS risk is currently low or absent . This type of information should be incorporated into the costs of land use management . Sugarcane expansion can provide a solution to one specific problem , such as supplying the oil market , but can on the other hand create a human health problem by increasing risks of acquiring HCPS . Our results reinforce the links between climate change and rises in incidence of diseases , such as Lyme , West Nile Virus and Echinococcus [101–103] . These findings should be considered as an additional argument to encourage governments , companies and citizens to sign agreements and start massive campaigns in order to mitigate climate change impacts . Our scenarios of future sugarcane expansion and climate change RCP4 . 5 and RCP8 . 5 predicted a low but significant increase in HCPS risk in the state of São Paulo by 2050 . Despite the lack of additive effects of sugarcane and climate in HCPS risk , we suggest that prevention and mitigation actions should focus on land use planning and forest restoration programs , and by concentrating healthcare effort in areas that are predicted to be at higher HCPS risk and have a high variation in the confidence interval . To better explore the underlying mechanisms of the observed pattern , we suggest future studies should test the effects of sugarcane production , temperature , and moisture on reservoir rodent population dynamics and on virus survival and aerosolization . Understanding those relationships is crucial to better understand HCPS transmission dynamics in different environments and situations , which is important for the effective design of preventive health strategies .
|
Hantavirus , hosted by rodent species , causes HCPS , a disease with a 50% mortality rate in humans . The conversion of native vegetation to sugarcane increases the abundance of hantavirus reservoir rodent species , augmenting disease risk . Additionally , temperature also has positive effects on disease risk because it affects rodent population and the time the virus remains infectious in the environment . Here we evaluate the impacts of climate change and sugarcane expansion on HCPS risk . Expansion of sugarcane increases average HCPS risk placing 20% more people at risk for acquiring HCPS than under current conditions . Temperature anomalies increase HCPS risk even more and place 31% and 34% more people at risk ( RCP4 . 5 and RCP8 . 5 , respectively ) . Our results confirm the impacts of climate change and agriculture expansion on disease risk and highlight the need for timely and efficient planning of operational control programs in order to avoid disease propagation in the future .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"ecology",
"and",
"environmental",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"atmospheric",
"science",
"pathogens",
"geographical",
"locations",
"microbiology",
"vertebrates",
"social",
"sciences",
"animals",
"mammals",
"viruses",
"hantavirus",
"land",
"use",
"rna",
"viruses",
"crops",
"infectious",
"disease",
"control",
"plants",
"bunyaviruses",
"human",
"geography",
"sugarcane",
"climate",
"change",
"infectious",
"diseases",
"geography",
"grasses",
"crop",
"science",
"south",
"america",
"ecosystems",
"medical",
"microbiology",
"microbial",
"pathogens",
"brazil",
"agriculture",
"people",
"and",
"places",
"rodents",
"climatology",
"ecology",
"forests",
"viral",
"pathogens",
"earth",
"sciences",
"biology",
"and",
"life",
"sciences",
"amniotes",
"organisms",
"terrestrial",
"environments"
] |
2017
|
Climate change and sugarcane expansion increase Hantavirus infection risk
|
Patterning of C . elegans vulval cell fates relies on inductive signaling . In this induction event , a single cell , the gonadal anchor cell , secretes LIN-3/EGF and induces three out of six competent precursor cells to acquire a vulval fate . We previously showed that this developmental system is robust to a four-fold variation in lin-3/EGF genetic dose . Here using single-molecule FISH , we find that the mean level of expression of lin-3 in the anchor cell is remarkably conserved . No change in lin-3 expression level could be detected among C . elegans wild isolates and only a low level of change—less than 30%—in the Caenorhabditis genus and in Oscheius tipulae . In C . elegans , lin-3 expression in the anchor cell is known to require three transcription factor binding sites , specifically two E-boxes and a nuclear-hormone-receptor ( NHR ) binding site . Mutation of any of these three elements in C . elegans results in a dramatic decrease in lin-3 expression . Yet only a single E-box is found in the Drosophilae supergroup of Caenorhabditis species , including C . angaria , while the NHR-binding site likely only evolved at the base of the Elegans group . We find that a transgene from C . angaria bearing a single E-box is sufficient for normal expression in C . elegans . Even a short 58 bp cis-regulatory fragment from C . angaria with this single E-box is able to replace the three transcription factor binding sites at the endogenous C . elegans lin-3 locus , resulting in the wild-type expression level . Thus , regulatory evolution occurring in cis within a 58 bp lin-3 fragment , results in a strict requirement for the NHR binding site and a second E-box in C . elegans . This single-cell , single-molecule , quantitative and functional evo-devo study demonstrates that conserved expression levels can hide extensive change in cis-regulatory site requirements and highlights the evolution of new cis-regulatory elements required for cell-specific gene expression .
Developmental systems operate in the presence of stochastic , environmental and genetic perturbations . While the output of a developmental system may be under stabilizing selection and remain mostly invariant , many internal variables such as the expression of a key gene or the activity of signalling pathways can be sensitive to perturbations . To reach a quantitative understanding of developmental systems , a key approach is to measure the sensitivity of the developmental system output to induced variation in an intermediate developmental phenotype . Whether and how this intermediate developmental phenotype varies within and among species then becomes a relevant evolutionary question [1] . The present work addresses the evolution of the expression level of the inducer of vulval development , lin-3 , on which we previously performed a sensitivity analysis by manipulating its genetic dosage and addressing the phenotypic consequences for the developmental system [2] . The site and level of transcription of a gene can be modulated both in cis to the gene through cis-regulatory DNA sites directly influencing its transcription , or in trans due to evolution of trans-factors modifying the cellular context in which the gene is acting [3] . cis-regulatory sites containing binding sites for transcription factors often occur upstream of the coding region or in introns . These binding sites are often organized in modules , hence the designation as cis-regulatory modules ( CRMs ) , acting in concert to enhance or repress gene expression in a given tissue at a given time . Changes in the number , relative order , orientation and spacing of transcription factor binding sites can affect transcription , often in a tissue-specific manner [4–6] . Tissue-specificity of CRMs is important for organismal evolution as it is thought to contribute to evolutionary novelty by minimizing pleiotropy [7–12] . Comparative studies in closely related species have revealed that transcriptional regulation can evolve through either extensive rewiring , or quantitative variation in the molecular components of a conserved network [11 , 13–17] . In particular , changes in cis-regulatory elements directly influencing the expression of critical developmental regulators have been shown to be a driving force for evolutionary innovation and phenotypic novelty in a variety of organisms . One example in Caenorhabditis concerns evolution between C . elegans and C . briggsae in the expression pattern of the transcription factor lin-48 in the excretory system , resulting in a morphological change in excretory cell position . In this case , lin-48 expression was gained in the excretory duct cell of C . elegans due to the acquisition of upstream binding sites for the transcription factor CES-2 [18 , 19] . Several features now make nematodes excellent experimental systems to understand gene expression evolution . First , rhabditid nematode species present a great advantage because homologous cells are easy to identify [20] so gene expression can be measured in a given cell . Second , the model organism Caenorhabditis elegans and other congeneric nematodes are amenable to functional genetics , transgenesis and now genome editing [21–26] . While transgenesis in C . elegans has long relied on formation of extra-chromosomal arrays containing many copies of the injected DNA that rearrange in an uncontrolled manner [27] , the integration of a single copy at a defined locus is now possible , either at the endogenous locus using CRISPR/Cas9-mediated replacements [24–26 , 28] or at a controlled insertion locus using Mos1-mediated single-copy insertions ( MosSCI ) [29] . Third , Caenorhabditis species are highly divergent at the molecular level [30 , 31] . For example , C . elegans is as molecularly distant to C . briggsae as human is to mouse , and C . angaria as far as zebrafish to mouse [31] , providing an opportunity to study the turnover of regulatory sequences at a large evolutionary scale where the nucleotide turnover is many times saturated yet the cellular context unchanged [32] . Many new Caenorhabditis species have recently been found and fully sequenced genomes are now available [33 , 34] ( M . Blaxter , pers . comm . ) . Finally , the recent advent of quantitative methods , such as single-molecule fluorescent in situ hybridisation ( smFISH ) [35 , 36] , allows to compare gene expression across species . The intensity of the conventional in situ hybridization signal cannot be meaningfully compared among species ( regardless of whether the same probes or different probes targeting orthologs are used ) , while in the smFISH method the number of spots reflecting individual mRNA molecules can be counted , allowing a quantitative study of gene expression evolution . Here , we take advantage of these recent developments to study the expression and requirement of lin-3 , a model developmental gene involved in C . elegans vulval induction . The vulva is the egg-laying and copulatory organ of nematodes , and the C . elegans vulva is now a ‘textbook’ example of animal organogenesis [37] . C . elegans vulval development involves induction of three ventral epidermal cells ( P5 . p-P7 . p ) in response to the secretion of the LIN-3 signal from the anchor cell of the somatic gonad . LIN-3 activates the EGF receptor in the vulval precursor cells closest to the anchor cell and thereby acts as the upstream major inducer of vulval fates , in three precursor cells out of the six competent cells ( Fig 1A ) . Induction of vulval fates involves interactions between EGF-Ras-MAPK , Notch and Wnt signalling , including some established pathway crosstalks [38] . We previously showed by modulating lin-3 expression via single-copy transgenesis that the genomic level of lin-3 expression is limited within a four-fold range for the vulva to develop normally in the C . elegans N2 background [2] . The C . elegans lin-3 gene has two alternative promoter regions , each including transcriptional and translational start sites . The lin-3 anchor cell isoform is driven by a specific cis-regulatory module lying immediately 5' of the second promoter , which is located in the first intron of the mRNA driven by the upstream promoter . Within this region , a 59 bp element was shown to be sufficient to drive expression in the anchor cell , acting as a transcriptional enhancer if placed upstream of a minimal promoter [39] . Anchor cell expression was shown to rely on two types of transcription factor binding sites in this 59 bp element , conserved in C . briggsae [39] ( Fig 2 ) : an NHR-binding site and two E-boxes . The lin-3 ( e1417 ) mutation substitutes a single nucleotide within the NHR-binding site and results in a strong reduction of lin-3 expression in the anchor cell [2 , 39] . This site can be bound in vitro by nuclear hormone receptors such as C . elegans NHR-25 . The two E-boxes surround the NHR-binding site ( E-boxL for left to the NHR and E-boxR for right ) , each consisting of the conserved sequence “CACCTG” but on opposite DNA strands to each other . When either of them is mutated in a lin-3::GFP transgene context , GFP expression in the anchor cell is strongly reduced [39] . We refer for simplicity to the ensemble of these three regulatory elements as the “regulatory triplet” . We show here that a relative stability in lin-3 mRNA expression in the anchor cell and conservation of LIN-3 vulval induction activity contrasts with the turnover of cis-regulatory binding sites at the lin-3 locus . We show that the difference in requirement of regulatory elements for anchor cell expression is due to evolution in cis to the lin-3 locus without a need to infer evolution in trans . This evolution in cis occurs in a very short 58bp region upstream of the lin-3 vulval specific isoform . This study uncovers the evolution of new cis-regulatory motifs required for cell-specific gene expression .
To determine the level of intraspecific variation in lin-3 expression , we quantified lin-3 expression in different C . elegans wild isolates . In the reference strain N2 , a mean level of 25 . 4 lin-3 mRNA spots was detected using smFISH [2 , 40] ( Fig 1B; S1A Table ) . We found that the mean and range of lin-3 expression in the anchor cell at the time of vulval induction are comparable between the C . elegans reference strain N2 and the most genetically divergent C . elegans isolates such as DL238 and QX1211 ( S1A Fig; S1A Table ) . We further explored lin-3 expression in different rhabditid species . First , we searched for the lin-3 ortholog in other available genomes ( S2 Fig ) . The LIN-3 proteins can be aligned along their whole length , with a conserved signal peptide , EGF and trans-membrane domains . Interestingly , the most conserved parts of the proteins are the N-terminal part following the signal peptide and the intracellular domain [41] . We designed smFISH probes for the lin-3 gene of C . briggsae , C . afra , C . angaria and Oscheius tipulae and found that lin-3 is expressed in a single cell within the somatic gonad , immediately dorsal to P6 . p , which we identified by DAPI staining as the anchor cell ( Fig 1C–1E; S1B Fig; S1B Table ) . Similar to C . elegans , we also detected lin-3 expression at a lower level in the gonad outside the anchor cell and in the pharynx . We quantified fluorescent spots in the anchor cell and found no significant difference between C . elegans and C . briggsae ( mean of 26 . 5±1 standard error in C . elegans vs . 25±1 in C . briggsae ) ( Fig 1F ) . In C . angaria and O . tipulae , we only found a small decrease compared to C . elegans ( Fig 1F ) . Although lin-3 was clearly detected in the anchor cell of C . afra ( S1B Fig ) , the inferior quality of the hybridisation signal compared to the background did not allow us to quantify fluorescent spots in this species . We conclude that despite the great genetic distance between these nematodes [31] , the mean number of lin-3 mRNAs is remarkably conserved at least in C . briggsae and may only vary within a narrow range in C . angaria and O . tipulae . The vulval cell fate pattern is conserved throughout the Rhabditidae family , to which the Caenorhabditis and Oscheius genera belong [42] , nevertheless molecular underpinnings of vulval induction in species other than C . elegans remain mostly unknown . lin-3 RNAi experiments in C . briggsae so far produced a weak effect [43] . In Pristionchus pacificus , an outgroup and the only nematode species for which we currently have substantial molecular information related to vulval induction , vulval formation relies on Wnt signalling and is thought to be independent of the EGF pathway [44 , 45] . To address whether the lin-3 homolog plays a functional role in vulval induction in different Caenorhabditis species , we used a combination of RNAi and pharmacological inhibition . First , we used recently established strains of C . remanei and C . briggsae that are rendered sensitve to RNAi administered by feeding due to the expression of the C . elegans intestinal transporter sid-2 [21 , 46] . lin-3 RNAi treatment in these C . briggsae and C . remanei strains resulted in substantial reduction in vulval induction ( Fig 2A; S3A–S3D Fig ) . We observed vulval cell fate phenotypes upon lin-3 RNAi that are not found in C . elegans , but are in keeping with published results revealing cryptic variation in vulval fate patterning following anchor cell laser ablations . Specifically , we found that P ( 5–7 ) . p adopted a 2°-3°-2° cell fate pattern in C . remanei and a 2°-2°-2° pattern in C . briggsae [17 , 43] . Second , we used the MAP kinase ( MEK ) inhibitor U0126 that inhibits the downstream signalling events following EGF receptor activation . Application of this inhibitor has been previously shown to decrease vulval induction in O . tipulae [47] . Consistent with this result , we also obtained evidence for loss of overall vulval induction both in C . angaria and C . afra ( Fig 1B; S3D Fig ) . Thus , we conclude that lin-3 is expressed in the anchor cell and plays a conserved role in inducing vulval fates in the Caenorhabditis genus . Three transcription-factor binding sites , an NHR-binding site and two E-boxes , are required for lin-3 expression in the anchor cell of C . elegans [39] . In light of the conserved expression pattern and level , we wondered whether these regulatory elements required for AC expression of lin-3 are also conserved . The regulatory triplet was found to be present in different species of the Elegans group of Caenorhabditis including C . briggsae ( Figs 3 , S4 and S5 ) . However , in the sister clade , called the Japonica group , we were able to find the two E-boxes , but no putative NHR-binding site within a window of 2 . 5 kb upstream of the translational start site of the vulval isoform of lin-3 . In further outgroup species , such as C . angaria , we only found a single E-box , and no NHR-binding site in this region . One E-box within the lin-3 CRM was also detected in the outgroup Oscheius tipulae ( Fig 3 ) . In C . sp . 1 , we were able to detect a single ATG and the first E-box was only found 2 kb upstream . Overall , these observations suggest that the NHR-binding site was acquired in the branch leading to the Elegans group of the Caenorhabditis genus . The evolution of the second E-box at the base of the Caenorhabditis genus remains unclear: the second E-box may have been acquired in the branch leading to the Elegans supergroup or else be lost in the Drosophilae supergroup . No other sequence similarity could be found in the region upstream of the ATG of the vulva-expressed isoform of lin-3 ( S4 Fig ) . The above results raised an interesting conundrum . How is it possible that some elements that are required for lin-3 anchor cell expression in C . elegans are completely missing in related species , without any significant consequence for lin-3 spatial and quantitative expression ? We first aimed to confirm that one E-box is not sufficient for lin-3 expression in the anchor cell in C . elegans . We used CRISPR-mediated genome editing [48] to select deletions of cis-regulatory elements of the C . elegans lin-3 gene . We generated a variety of alleles , in which either all three elements are deleted ( mf90 ) , or NHR and E-boxR are deleted leaving E-boxL intact ( mf72-mf74 ) or only E-boxR is left intact ( mf75 ) , the latter recapitulating the cis-regulatory context of the C . angaria lin-3 upstream module ( Fig 4A ) . All these alleles result in fully penetrant vulvaless phenotypes with no cell induced to a vulval fate , thus a stronger phenotype than the lin-3 ( e1417 ) allele with one-nucleotide substitution in the NHR binding-site ( Fig 4B ) . We used smFISH to detect lin-3 transcripts and found no lin-3 expression in the anchor cell , which was visualised by the unperturbed expression of lag-2 . Interestingly , we still detected lin-3 expression in the gonad of these mutant animals ( Fig 4C and 4D ) . We conclude that these new lin-3 alleles are anchor cell-specific null alleles . These results confirmed that one E-box in the upstream cis-regulatory module of lin-3 is not sufficient for lin-3 expression in the anchor cell of C . elegans—whereas it appears sufficient in species of the Drosophilae group such as C . angaria . The evolution in the requirement of transcription-factor binding sites for lin-3 expression in the anchor cell could be due to changes in cis or in trans to the lin-3 locus or both . We reasoned that if differences in trans were important , we would expect lin-3 genomic fragments derived from species missing one or two cis-regulatory elements from the regulatory triplet to be unable to be expressed in the anchor cell of C . elegans . We tested this hypothesis and obtained multiple lines of evidence suggesting no role for changes in trans to the lin-3 locus in explaining the differential binding site requirement . First , we overexpressed in C . elegans a C . angaria lin-3 genomic fragment containing 200 bp of upstream sequence , the coding region and the 3’ UTR . This fragment drove anchor cell expression of Can-lin-3 and triggered vulval hyperinduction in C . elegans , further showing that the Can-LIN-3 protein could activate the C . elegans LET-23/EGF receptor ( S6A and S6B Fig ) . Vulval hyperinduction was also observed when an equivalent genomic fragment from C . elegans was expressed in C . angaria or a fragment from C . afra was expressed in C . elegans ( S6C and S6D Fig ) . These results indicate that the injected lin-3 fragments from different Caenorhabditis species contain the necessary information for anchor cell-specific expression , despite the fact that a superficially equivalent C . elegans fragment with only one E-box , as in the new lin-3 alleles described above , cannot be expressed in this cell . Since the regulatory triplet for C . elegans anchor cell expression is missing in these transgenes , we tested whether sequences in the introns , exons or 3'UTR sequences were required for expression of the C . angaria transgene in the anchor cell . To this end , we fused the Can-lin-3 upstream sequences to a fragment containing the C . briggsae lin-3 coding sequence and 3’ UTR . We expressed this fragment in C . elegans N2 and again observed clear expression in the anchor cell . As expected , in control injections containing only the promoterless C . briggsae fragment , the transgene was not expressed anywhere in the body ( S7 Fig ) . To further strengthen these results , we fused the lin-3 cis-regulatory modules amplified from C . elegans , C . briggsae , C . afra and C . angaria to sequences encoding an unrelated protein , the fluorescent protein Cherry , and the unrelated unc-54 3'UTR . In all cases , we observed clear expression in the anchor cell ( Fig 5A ) , indicating again that these short cis-regulatory modules alone contain the necessary information for anchor cell-specific expression in C . elegans . We conclude that evolution within the 200 bp upstream cis-regulatory module of lin-3 is sufficient to explain the difference in requirement of regulatory elements for anchor cell expression within Caenorhabditis . Above , we used multicopy transgenesis , which may cause sufficient expression and hyperinduction due to summing of weak transcriptional activity of many copies . We thus next asked whether the C . angaria lin-3 fragment had quantitatively a similar activity to that of its C . elegans counterpart when introduced in single copy at a targeted genomic location outside the lin-3 locus ( using MosSCI transgenesis , see Methods ) . We found that a single-copy Can-lin-3 insertion in C . elegans N2 is expressed in the anchor cell ( Fig 5B ) and does not cause hyperinduction , like an equivalent Cel-lin-3 transgene copy [2] . Most interestingly , this single copy transgene could completely rescue the induction and brood size of lin-3 ( e1417 ) mutants , both in homozygous and hemizygous states ( Figs 5C , S8 ) . This quantitative behavior of the Can-lin-3 transgene ( rescue in the hemizygous and homozygous state , no effect when added to the endogenous locus ) recapitulates the activity of a C . elegans copy inserted at the same genomic location [2] . This experiment shows that the C . angaria lin-3 gene driven by its cis-regulatory element acts in a similar quantitative manner to the C . elegans fragment , even in the absence of the regulatory triplet . To pin down the regulatory elements in the C . angaria transgene that are required for anchor cell expression , we mutated the E-box , which is the only distinguishable regulatory element in this short upstream region . We found that Can-lin-3 genomic fragments with a mutated E-box lose their ability to be expressed in the anchor cell of C . elegans and to trigger vulval hyperinduction when expressed as multi-copy transgenes ( Fig 6B , 6D and 6E ) . This shows that the single E-boxR of C . angaria is necessary for lin-3 expression in the anchor cell of C . elegans . Changes in the flanking sequences to core binding sites have been shown to contribute to binding efficiency of transcription factors , so we reasoned that perhaps the difference in requirement of regulatory elements for lin-3 expression in the anchor cell may rely on nucleotides adjacent to the single E-box . To this end , we synthesised a chimeric CRM , where a 58 bp central portion harbouring the regulatory triplet in C . elegans was replaced with 58 bp from C . angaria containing E-boxR ( Fig 6E ) . We first showed that this chimeric fragment can be expressed in the C . elegans anchor cell when used in multiple-copy extra-chromosomal array transgenesis ( Fig 6C ) . Furthermore , we used genome editing at the Cel-lin-3 locus to replace the endogenous lin-3 CRM with this chimeric CRM . We found that the genome-edited animals expressed lin-3 in the anchor cell at a normal level and produced a phenotypically wild-type vulva ( Fig 6F; S2 Table ) . These results demonstrate that the difference in requirement of cis-regulatory elements between C . elegans and C . angaria is explained by compensatory evolution within a very short cis-regulatory fragment ( 58 bp ) , rendering the presence of a second E-box and the NHR binding site unnecessary in C . angaria . Despite this loss of transcription factor binding sites , the activity of the cis-regulatory module in driving transcription in the anchor cell remains at the same quantitative level . The compensation could be explained by the gain of new transcription factor binding sites in the C . angaria 58 bp regulatory region . To identify putative transcription factor binding sites , we performed a motif discovery approach in the anchor cell cis-regulatory lin-3 regions of Caenorhabditis species close to C . angaria and an exhaustive search of transcription factors that could bind the 58 bp sequence ( see Methods ) . We found the GTTTATG sequence , a possible Forkhead-binding site , to be significantly over-represented . This sequence is only one bp to the right of the C . angaria E-box . We tested whether modifying this sequence in the 58 bp C . angaria replacement would change the lin-3 expression level . Indeed , when scrambling these 7 bp ( see Methods; S2 Fig ) , lin-3 expression was reduced significantly to about 60% of the wild-type level ( mf95 allele in Fig 6F; t-test , p-value < 6 10−8 ) . However , as expected from a less than two-fold decrease [2] , this new replacement , like the intact C . angaria CRM , produced phenotypically wild-type vulva cell fate induction ( Fig 6F ) . Thus , we could affect the expression of the C . angaria CRM by modifying a motif adjacent to the E-box . This motif contributes to the compensation in cis in the 58 bp , but does not explain all of it , as lin-3 expression in the mf95 mutated replacement allele was still much higher than with a single C . elegans E-box .
This study addressed the level of expression of a critical developmental regulator in a single cell . We showed that both lin-3 expression level in the anchor cell and its requirement for the induction of vulval cell fates are conserved in Caenorhabditis and Oscheius nematode species . We found that the mean lin-3 mRNA level in the anchor cell only varies within 30% , despite the vast genetic divergence in this group—corresponding to that found among the most diverged vertebrates . We previously showed using quantitative perturbations that the mean level of lin-3 expression in C . elegans needs to stay within a four-fold range for a correct vulva pattern to arise and that the mean C . elegans N2 level is in the very middle ( on a log scale ) of this permissible zone . Therefore , it is likely that stabilizing selection acting on vulva formation [49] leads to stasis both in lin-3 expression level and in its effect on vulval induction . By contrast with this evolutionary stasis in vulval pattern and in the lin-3 mRNA level , we showed that this cell-specific level of lin-3 expression involves substantial turnover of key cis-regulatory elements , namely the appearance of a novel binding site ( NHR ) and the turnover of a second copy of an existing binding site ( E-box ) . Each of these elements is required for anchor cell expression in C . elegans yet is absent in some Caenorhabditis species . We further focused on the difference in requirement of cis-regulatory elements for lin-3 expression between C . elegans and C . angaria . A 58 bp fragment from C . angaria with a single E-box can replace the three C . elegans binding sites , demonstrating that compensatory evolution within this short cis-regulatory fragment at the lin-3 locus is sufficient to explain this difference in transcriptional regulation Among evo-devo studies that center on comparisons of gene expression patterns and the evolution of cis-regulatory sequences , this is to our knowledge the first study taking advantage of the latest available capabilities to edit genomes and to quantify the level of mRNA expression at the single-cell level in a multicellular eukaryote . Gene expression may evolve due to changes in cis or in trans to a given locus , two possibilities that are not mutually exclusive . Cis-regulation may occur from sites quite distant to the transcriptional unit due to long-range chromatin interactions . Our data provided strong support for compensatory cis-changes , and this in a DNA fragment directly upstream of the translational start site of the vulva specific isoform of lin-3 . We cannot exclude that some further trans-changes facilitate the difference in requirement of regulatory elements between the two species . However , the cis-regulatory changes that we uncovered in this work are at least sufficient to explain the difference in requirement of regulatory elements for anchor-cell-specific gene expression in Caenorhabditis . We have narrowed down the compensatory changes that allow the C . angaria lin-3 to be expressed in the anchor cell in a very short region of 58 bp . To explain the compensatory changes , we performed an exhaustive search of transcription factor binding sites and found a putative Forkhead binding site immediately adjacent to the E-box in C . angaria and absent from the replaced 58 bp region of C . elegans . Mutation of this site significantly lowered lin-3 expression , but insufficiently to affect the vulval induction level and it thus only partially explained the compensatory evolution in cis ( Fig 6E ) . We further note that , because this putative Forkhead binding site is immediately adjacent to the E-box , we cannot distinguish between two scenarios: a role for another specific transcription factor binding site versus an alteration of the affinity of the E-box itself . An alternative model would indeed be that compensation occurs through a stronger affinity of the E-box in the C . angaria regulatory region , while the C . elegans E-box is insufficient to drive expression . Such differences in affinity may arise from changes in the sequences flanking the core binding sites as it has been shown for bHLH factors binding to E-boxes [50 , 51] . Variation in the flanking sequences next to core transcription factor binding sites has also recently been shown to influence both the levels and sites of gene expression for another developmentally important gene [52] . We conclude that the GTTTATG sequence contributes to the compensation , but does not explain it entirely . Here we described some evolution in cis-regulatory elements that occurs without consequences at the level of gene expression , as observed in many other genes and various groups of organisms [53–56] . This cis-regulatory element turnover in the absence of phenotypic consequence can be viewed as an extension to the notion of developmental systems drift , which posits that distinct molecular mechanisms may underlie the emergence of similar developmental phenotypes [57] . In a similar way , the conservation of gene expression pattern and level may depend on distinct molecular mechanisms due to the loss and gain of binding sites . Indeed , if the invariant output phenotype that we consider is lin-3 expression level in the anchor cell , the molecular events leading to it , such as transcription factor binding , do vary in evolution . The best-studied example for conservation of gene expression pattern despite turnover of cis-regulatory elements is the stripe 2 enhancer of the Drosophila pair-rule gene even-skipped . The minimal stripe 2 enhancer ( eve2 ) in D . melanogaster is a DNA region of approximately 500 bp that consists of multiple binding sites for activators such as Bicoid and Hunchback and for repressors such as Giant and Krüppel: their combination allows a confined expression in the second stripe along the antero-posterior axis of the early Drosophila embryo [58] . Compared to the described lin-3 cis-regulatory module , the eve2 stripe element involves more transcription-factor binding sites and results in expression in a group of cells ( nuclei ) rather than in a single cell . Similar to the lin-3 CRM , the transcription-factor binding sites change in Drosophila species in a way that binding sites required for correct expression in D . melanogaster are absent in the stripe 2 element of other species , though without leading to alteration in the expression domain , due to compensatory cis-changes [53 , 59] . Here we went further in replacing the endogenous cis-regulatory sequences at the locus by those of a distant species , and show a quantitative rescue of gene expression and vulval induction . One previous example in C . elegans of turnover of binding sites involves lin-48 expression in hindgut cells , which is conserved between C . elegans and C . briggsae despite turnover of EGL-38 upstream response elements [60] . This turnover shows both similarities and differences to the described evolution of lin-3 cis-regulatory elements . The similarity is that there is an increase in the number of EGL-38 response elements in C . elegans . However , in the lin-48 case , there is evolution towards redundancy because the gain in one EGL-38 response element decreases the reliance on the existing element for correct gene expression . More recently , evolution of cis-regulatory elements between C . elegans and C . briggsae has been studied by placing exogenous cis-regulatory elements from C . briggsae into C . elegans . A main result over several genes whose expression is conserved between the two species is the appearance of ectopic gene expression domains in these transgenic experiments , implying evolution both in cis and in trans [61 , 62] . In one case , the ability of the unc-47 proximal promoter from C . briggsae to drive ectopic expression in some C . elegans neurons was mapped next to a conserved cis-regulatory motif [61] . We note that the C . angaria fragment conveys the same level of transcriptional activity yet that a few vulval cell fate patterning "errors" occur in the replacement lines ( Fig 6F ) . We observed both hypoinduced and hyperinduced variants in each of the two replacement lines ( S2 Table ) , but the very low frequency of these variants make them difficult to study quantitatively . In the case of the eve2 enhancer , the minimal stripe element is embedded within a larger region of approximately 800 bp , and these flanking sites contribute to robustness to some genetic and environmental perturbations [63] . In Caenorhabditis , the distal promoter of unc-47 , although largely not conserved , is also important for robust gene expression , acting perhaps in a sequence-independent manner [64] . It remains unclear whether any regions within and/or outside the lin-3 CRM can play a similar role in stabilizing expression of lin-3 in Caenorhabditis to different perturbations . The distribution of lin-3 cis-regulatory elements in different Caenorhabditis nematodes and the mapping of changes on the phylogeny suggests as the most likely evolutionary scenario a gain of regulatory sites: the likely acquisition of an E-box before the common ancestor between the Elegans and Japonica groups and a gain of an NHR-binding site before the origin of the Elegans group . In addition , these sites not only appeared , but also became indispensable for lin-3 anchor cell expression at least in C . elegans . The acquisition of such new short regulatory motifs ( 6 bp ) is easy and gains of regulatory motifs have been observed in other systems as well [65] . Given the high robustness of vulval development to several perturbations , the evolution towards a dependence on a higher number of sites for anchor cell expression is counter-intuitive and suggestive of evolution towards fragility . It is currently unclear what drove the evolution of these novel motifs with a conserved gene expression , whether selection or drift . Gains in interconnectedness between components of transcriptional networks may often occur non-adaptively , for example if they do not disrupt the underlying regulation [66] . Such gains can also be reshaped in equivalent network configurations and eventually become necessary depending on the evolution of the transcriptional network [67] .
A complete list of strains used in this study is presented in the supplement ( S3 Table ) . All strains were maintained at 20°C and handled according to standard procedures [68] . We used the Bristol N2 strain as a reference C . elegans strain on standard NGM plates with OP50 as a food source . The U0126 treatments were performed by supplying the DMSO-dissolved inhibitor to NGM plates at a concentration between 10–150 μM and letting synchronised L2 stage nematodes develop into L4 larvae . Control treatments in this case were performed by growing nematodes on plates supplemented with DMSO only . For the Can-lin-3 rescue of the C . elegans lin-3 ( e1417 ) mutant , JU2495 hermaphrodites were crossed to JU2498 males and the F1 or F2 progeny were analysed for hemizygous or homozygous insertion phenotypic rescue , respectively . The lin-3 genomic sequences of the different species were accessed in WormBase ( www . wormbase . org; version WS252 ) or from the Caenorhabditis Genomes Project by Mark Blaxter's laboratory ( http://bang . bio . ed . ac . uk:4567 ) or from Matt Rockman’s laboratory . The Oscheius tipulae genome was sequenced and assembled as a collaborative effort between M . Blaxter's and our lab ( Besnard , Kotsouvolos et al . , in preparation ) and is available ( http://oscheius . bio . ed . ac . uk/ ) . We first used the TBLASTN algorithm conditioning only to the most identical hits , favouring those with high similarity in the N-terminal part and signal peptide , and lower e-value . Afterwards , we proceeded to predict gene bodies in these contigs using FGNESH ( http://www . softberry . com ) with a hidden Markov model specific to C . elegans . Finally , manual curation and annotation of the lin-3 sequences were performed using as a reference the amino-acid sequence of the closest available lin-3 ortholog . To study the evolution of the regulatory triplet in the Caenorhabditis clade , we analysed the promoter regions upstream of the downstream ATG corresponding to the N-terminal exon homologous to that known to be expressed in the AC of C . elegans ( S2 Fig ) . First , to address whether the cis-regulatory C . elegans NHR-binding sites and E-boxes were present in the other species , we performed a scan in the promoters with the position weight matrices of HLH-2 and NHR-proteins available in JASPAR [69] using matrix-scan [70] and a n = 2 Hidden Markov Model specific to C . elegans ( Fig 3 ) . Similarly , we looked in these regions for DNA patterns known to be binding sites of bHLH proteins [51] using the dna-pattern tool present in the RSAT suite [71] . Once we had the position of these sites across the promoter regions , we proceeded to plot their location using RSAT feature-map tool ( S5 Fig ) . Additionally , we looked for DNA motifs different from the cis-regulatory C . elegans NHR and E-boxes binding sites by performing a motif-discovery approach in Caenorhabditis lin-3 promoters using the RSAT tool oligo-analysis [71] . The top over-represented words of length 6 , 7 and 8 base pairs were compared to known motifs available in JASPAR . We thus identify the GTTTATG to the right of the E-box . Finally , to identify possible transcription factors acting on the AC lin-3 expression in the 58 bp C . angaria fragment , we performed an exhaustive search of the full JASPAR motif repertoire in the 58 bp replaced sequence using RSAT matrix-scan . This search found the putative Forkhead-binding motif and a putative overlapping bZIP-binding motif ( Fos/Jun repressors ) . The 7 bp modification in the mf95 replacement also affected this predicted binding site of bZip transcription factors . All lin-3 CRMs reside directly upstream of the ATG of the vulval isoform of lin-3 . To create the lin-3 CRM::Cherry::unc-54 constructs , we used a three-fragment Gateway approach merging the lin-3 CRMs cloned in pDONOR P4-P1R , the Cherry ORF cloned in pDONOR 221 and the unc-54 3’UTR cloned in pDONOR P2R-P3 . All primer sequences containing attB4 forward and attB1 reverse recombination sites used to amplify the CRMs from gDNA from different species are shown in S4 Table . unc-54 3’ UTR was amplified from N2 genomic DNA using primers unc-54attB2 and unc-54attB3 . Worm-optimised Cherry was amplified from pAA64 using primers containing the attB1and attB2 sites . All constructs were injected at 10 ng/μl with myo-2::GFP as co-injection marker and pBluescript as carrier DNA . To create the Can-lin-3 insertion by MosSCI , we amplified a 2 . 9 kb lin-3 fragment from C . angaria genomic DNA using primers Canlin-3AvrII and Canlin-3XhoI . The amplicon was cloned into pCFJ151 ( chromosome II targeting vector ) [29] as an AvrII/XhoI fragment . Injections and recovery of insertions were performed using the direct insertion protocol , as previously described . To overexpress lin-3 fragments in C . elegans or C . angaria , we amplified genomic fragments amplified from C . elegans ( 5 . 2 kb ) , C . angaria ( 3 . 2 kb ) and C . afra ( 5 . 1 kb ) using primer pairs RH9for/RH9rev , Canlin-3F2/Canlin-3R1 and Caflin-3oxF2/Caflin-3oxR1 , respectively . The PCR products were injected directly ( 30 ng/μl ) together with pBluescript as carrier and myo-2::GFP as co-injection marker . To mutagenize the E-box in the C . angaria lin-3 CRM , the above 3 . 2 kb fragment was cloned into pGEM-Teasy and the 5’-CAGGTG-3’ sequence was modified to 5’-CAGGAA-3’ using primers t211a_g212a/ t211a_g212a_anti and standard in vitro site directed mutagenesis . The chimeric construct replacing a 58 bp region containing the C . elegans regulatory triplet ( 5’-cacctgtgtattttatgctggttttttcttgtgaccctgaaaactgtacacacaggtg-3’ ) with a similar in length sequence from C . angaria containing only one E-box ( 5’-attttttgtcaaagatttttcggcgccaggtgtgtttatgactcatgttagggccgag-3’ ) was synthesised by Genewiz . This construct was used as PCR template to permute 7 bp to the right of the C . angaria E-box ( 5’-CAGGTGtGTTTATG-3’ to 5’-CAGGTGtTTGGATT-3’ ) . The chimeric construct to drive Cbr-lin-3 under the C . angaria CRM was built using fusion PCR . Briefly , the Can-lin-3 CRM was amplified from C . angaria genomic DNA with primers Canlin-3 F2 and CaACFusion and the Cbr-lin-3 region coding region and 3’ UTR from C . briggsae genomic DNA with primers Cbrlin-3F1 and Cbrlin-3R1 . The two amplicons were then fused together using a third PCR reaction with primers Canlin-3F2 and Cbrlin-3R1 . The final product was injected as a PCR fragment at 20 ng/μl concentration . smFISH was performed in synchronized populations of L3 stage animals using short fluorescently labelled oligos as probes , as previously described [2] . The animals were age-synchronized by bleaching , followed by hatching of embryos in M9 buffer . The L1 larvae were then placed onto culture plates with food until the L3 stage , as determined by Nomarski microscopy , and then fixed . The C . elegans lin-3 and lag-2 probes have been previously described [2] . The low level of genetic divergence within C . elegans allowed us to detect fluorescent spots while using the same FISH probe as in the N2 strain . For all other species we followed the same protocol as with C . elegans with the following two modifications to decrease the more pronounced background fluorescence . We used 20% formamide in the hybridisation and wash solutions and performed three washes post-hybridisation instead of two in C . elegans . Given that we are using different probes consisting of fewer oligos for the detection of lin-3 transcripts in these species together with slightly more stringent hybridisation conditions , the observed difference in the number of fluorescence spots may thus even be due to technical rather than biological reasons . The sequences of the new lin-3 probes can be found in S5 Table . The probes were labelled with Quasar 670 ( Biosearch Technologies ) and diluted to 100–200 nM for the overnight hybridisation . RNAi was performed by feeding the animals with dsRNA-expressing bacteria , as previously described [2] . The C . elegans lin-3 RNAi feeding clone used in this study is from the Ahringer RNAi library ( Source Bioscience ) . A Cre-lin-3 fragment was amplified using oligos Crelin-3RNAiF1 and Crelin-3RNAiR1 that contain an XhoI restriction site . The PCR product was cloned into L4440 as an XhoI fragment . To create the C . briggsae lin-3 RNAi clone , a fragment was amplified using primers Cbrlin-3RNAiF1 and Cbrlin-3RNAiR1 and then cloned into pDONR 221 ( Invitrogen ) using attB1F and attB2R universal oligonucleotides . The lin-3 fragment was sequence verified and transferred to a Gateway compatible L4440 plasmid . Both constructs were transformed into E . coli HT115 for use in C . elegans feeding . To score the vulval cell fate pattern , nematodes were mounted with M9 on 3% agar pads containing 10 mM sodium azide and analysed under Nomarski optics . Standard criteria were used to infer cell fates based on the topology and number of cells at the L4 stage [43 , 72] . Half fates were assigned when two daughters of the Pn . p cells acquired distinct fates after the first cell division . We followed the CRISPR/Cas9 target design and used reagents as previously described [48] . We targeted the following region at the C . elegans lin-3 CRM 5’-accctgaaaactgtacacacAGG-3’ with AGG representing the PAM motif . We replaced the unc-119 target site under the pU6 promoter [48] with the lin-3 target site using fusion PCR first with primers E-box2A gRNA-F/ U6prom HindIII and E-box2A gRNA-R/ oligos U6prom EcoRI F followed by amplification of the full sgRNA fragment with U6prom EcoRI F/ U6prom HindIII R . The only modification was that we did not clone the lin-3 sgRNA in a vector but injected it directly as a PCR product ( 40 ng/μl , together with 40 ng/μl eft-3::Cas-9 and myo-2::GFP as co-injection marker ) . To replace the endogenous lin-3 cis-regulatory element of C . elegans by a 58 bp lin-3 element from C . angaria , we first obtained a chimeric double-stranded DNA as homologous recombination template , using Gibson assembly of C . elegans lin-3 promoter extremities with 58 bp of the C . angaria lin-3 upstream sequence . In a similar fashion , we obtained a homologous recombination template identical to the previous but with modified bases next to the C . angaria E-box . Oligonucleotide sequences are found in S4 Table . C . elegans N2 animals were injected with a DNA mix containing the Peft-3::Cas9 plasmid , the pU6::dpy-10 sgRNA plasmid ( co-CRISPR marker ) , the Ebox-2A sgRNA containing plasmid and the double-stranded DNA repair templates ( independently ) , with final concentrations of 50 , 40 , 100 , and 30 ng/μl , respectively . On plates with a high number of animals displaying the Dpy phenotype , the F1 progeny were singled , and their progeny screened by PCR . Broods from independent P0 animals were found positive and rendered homozygous ( two independent lines for each replacement ) . Both replacements were confirmed by Sanger sequencing . The resulting lines were given allele names mf91 and mf92 for the first replacement , and mf95 and mf112 for the second one .
|
Diversification of mechanisms regulating gene expression of key developmental factors is a major force in the evolution of development . However , in the past , comparisons of gene expression across different species have often been qualitative ( i . e . ‘expression is on versus off’ in a certain cell ) without precise quantification . New experimental methods now allow us to quantitatively compare the expression of gene homologs across species , with single cell resolution . Moreover , the development of genome editing tools enables the dissection of regulatory DNA sequences that drive gene expression . We use here a well-established “textbook” example of animal organogenesis in the microscopic nematode , Caenorhabditis elegans , focusing on the expression of lin-3 , coding for the main inducer of the vulva , in a single cell called the anchor cell . We find that the lin-3 expression level is remarkably conserved , with 20–25 messenger RNAs per anchor cell , in species that are molecularly as distant as fish and mammals . This conservation occurs despite substantial changes and compensation in the regulatory elements required for cell-specific gene expression .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cell",
"binding",
"sequencing",
"techniques",
"cell",
"physiology",
"invertebrates",
"caenorhabditis",
"gene",
"regulation",
"regulatory",
"proteins",
"dna-binding",
"proteins",
"animals",
"animal",
"models",
"developmental",
"biology",
"caenorhabditis",
"elegans",
"model",
"organisms",
"transcription",
"factors",
"sequence",
"motif",
"analysis",
"molecular",
"biology",
"techniques",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"proteins",
"gene",
"expression",
"molecular",
"biology",
"evolutionary",
"genetics",
"biochemistry",
"cell",
"biology",
"genetics",
"nematoda",
"biology",
"and",
"life",
"sciences",
"evolutionary",
"biology",
"organisms",
"evolutionary",
"developmental",
"biology"
] |
2016
|
Evolution of New cis-Regulatory Motifs Required for Cell-Specific Gene Expression in Caenorhabditis
|
Prions enter the environment from infected hosts , bind to a wide range of soil and soil minerals , and remain highly infectious . Environmental sources of prions almost certainly contribute to the transmission of chronic wasting disease in cervids and scrapie in sheep and goats . While much is known about the introduction of prions into the environment and their interaction with soil , relatively little is known about prion degradation and inactivation by natural environmental processes . In this study , we examined the effect of repeated cycles of drying and wetting on prion fitness and determined that 10 cycles of repeated drying and wetting could reduce PrPSc abundance , PMCA amplification efficiency and extend the incubation period of disease . Importantly , prions bound to soil were more susceptible to inactivation by repeated cycles of drying and wetting compared to unbound prions , a result which may be due to conformational changes in soil-bound PrPSc or consolidation of the bonding between PrPSc and soil . This novel finding demonstrates that naturally-occurring environmental process can degrade prions .
Prion diseases , also known as transmissible spongiform encephalopathies ( TSEs ) , are a group of fatal neurodegenerative diseases which impact a number of species including cattle ( bovine spongiform encephalopathy , BSE ) , sheep and goats ( scrapie ) , deer , elk and moose ( chronic wasting disease , CWD ) , and humans ( Creutzfeldt-Jakob disease , CJD , and others ) [1] . The infectious agent of prion diseases , PrPSc , is a misfolded isoform of a non-infectious cellular prion protein , PrPC . CWD and scrapie prions can remain infectious over long time periods [2–5] in the environment . The increasing incidence and geographic range of CWD in cervids and its unknown host range makes this disease of particular concern in North America . Prions enter the environment from infected hosts . Prions are shed into the environment via antler velvet [6] , blood , saliva [7] , urine [8–10] , feces [11 , 12] , and birthing matter [13] . Prions also enter the environment through decomposition of infected animal carcasses [5] . Prions can be present in these excreta during the presymptomatic phase of disease , therefore , infected animals can shed prions into the environment over wide areas of the host’s home range . The amount of infectivity that is introduced into the environment is difficult to assess since prion titer is operationally defined with the route of infection , the age of the animal , the number of doses , and the PrP genotype of the host all making significant contributions in establishment of infection [14–17] . After release from an infected host , PrPSc binds to a wide range of soil and soil minerals [18–20] . Clay and clay soils have higher affinity for prions and adsorb PrPSc at a faster rate compared to sand or sandy soils [18 , 19] . Binding of PrPSc to soil in a competitive matrix such as brain homogenate is slow and reduced compared to non-competitive environments ( i . e . purified PrPSc ) [18 , 19] . Once bound to soil , prions remain highly infectious although soil-induced changes in in vitro PrPSc conversion efficiency and infectivity in animals have been measured [21 , 22] . Further work is needed to fully elucidate the effect of PrPSc binding to surfaces on prion infectivity and transmission . The contribution of soil bound prions to the natural transmission of prion disease is incompletely understood . Modeling studies conducted in CWD-endemic areas indicate that locations with a preponderance of organic soils correspond with an increased incidence of CWD [23–25] . Since the soil type that is bound to PrPSc has a relatively small influence on prion infectivity , other factors may play a role in prion transmission . Prions are primarily retained in surface soils [26] and the close contact of ruminant animals with soils renders soil-bound prions a likely source for prion disease transmission through ingestion or inhalation [27–30] . Therefore , PrPSc binding to soil may increase the bioavailability of prions for transmission . Inactivation of soil-bound prions will be required to control and prevent the spread of prion diseases in the environment . Prion degradation under environmentally-relevant conditions is poorly understood . To date , the majority of studies have investigated degradation and inactivation of prions that are not bound to soil . Microorganisms and isolated enzymes , sometimes associated with harsh digestion conditions ( high temperature and extreme pH ) , effectively reduce PrPSc abundance [31–34] . Exposure of prions to intact lichens at room temperature and neutral pH can reduce the abundance of PrPSc [35] . The loss of PrPSc immunoreactivity does not always correspond with a measurable reduction of prion infectivity [33 , 36] therefore , studies that rely solely on changes in PrPSc abundance must be interpreted with caution . Prionzyme , a commercially-available enzyme , degraded soil-adsorbed prions under environmentally-relevant conditions and is the first evidence to suggest that mitigation of soil-bound prions is possible [37 , 38] . Prions retained in surface soils are exposed to ambient environmental processes that have the potential to inactivate prions . Naturally-occurring cycles of drying and wetting alter soil aggregate stability and can influence interactions between soil particulate organic matter and dissolved organic compounds [39–41] . They can also change microbial activity and population dynamics [39 , 40 , 42] . Additionally , dehydration can unfold the native protein structure [43] . It is not known if these processes alter the biologic properties of soil-bound prions . To address this important question , we investigated the effects of repeated cycles of drying and wetting on the fitness of prions bound to various soil types .
The abundance of total protein in the brain homogenate ( BH ) of hamster infected with HY TME before or after binding to silty clay loam ( SCL ) was shown in Fig . 1 . The amount of total protein of unadsorbed BH was significantly reduced ( p<0 . 05 ) by 13% ( Fig . 1 ) . After binding to SCL , the amount of total protein remained unchanged ( Fig . 1 ) suggesting protection of proteins from degradation of repeated drying and wetting by adsorption to soil surface . Repeated cycles of wetting and drying did not result in changes in pH or conductivity . Soil- or soil mineral-adsorbed HY PrPSc ( HY ) was prepared as described in Table 1 and subjected to 0 ( control ) , 1 , and 10 repeated cycles of drying and wetting . Significant differences ( p>0 . 05 ) were not observed in normalized PrPSc immunoreactivity between samples after 1 drying and wetting cycle ( Dry 1 ) compared to the negative control ( no drying and wetting treatment , Dry 0 ) ( Fig . 2 ) . PrPSc abundance was significantly decreased ( p<0 . 05 ) between the negative control and 10 repeated cycles of drying and wetting of unbound HY , silty clay loam ( SCL ) - , bentonite- , and silicon dioxide ( SiO2 ) -adsorbed HY ( Fig . 2 ) . The average reductions are 51% , 53% , 72% , and 73% , respectively ( Fig . 2B ) . The PrPSc abundance of sandy loam soil ( SLS ) -adsorbed and sand-adsorbed HY PrPSc were not significantly ( p>0 . 05 ) changed following 10 repeated cycles of drying and wetting ( Fig . 2 ) . After 1 round of PMCA , PrPSc amplification in all samples subjected to 1 cycle of drying and wetting was not significantly ( p>0 . 05 ) different compared to unbound HY ( Fig . 3 ) . The amplification of unbound HY , SCL- , SiO2- , bentonite- , and sand-adsorbed HY subjected to 10 drying/wetting cycles was significantly ( p<0 . 05 ) reduced by 48% , 95% , 100% ( negative value for bentonite-HY was corrected to 0% ) , 74% , and 95% , respectively ( Fig . 3 ) . However , the PMCA conversion efficiency of SiO2 HA-adsorbed HY was not changed ( p>0 . 05 ) after 10 cycles of drying and wetting ( Fig . 3 ) . Sandy loam soil inhibits HY PrPSc PMCA conversion independent of HY adsorption resulting in low PrPSc abundance ( Fig . 3 ) . Samples were prepared as described in Table 1 . A significant ( p>0 . 05 ) difference in PrPSc immunoreactivity was not observed for unbound DY or SCL-adsorbed DY after 1 drying and wetting cycle compared to the control ( Fig . 4A and 4C ) . A significant ( p<0 . 05 ) reduction in PrPSc abundance of 48% was observed for unbound DY treated with 10 cycles of drying and wetting while the PrPSc abundance of SCL-bound DY was not significantly ( p>0 . 05 ) changed ( Fig . 4A and 4C ) . After 3 rounds of PMCA , DY and SCL-bound DY amplified to similar ( p>0 . 05 ) levels after 1 cycle of drying and wetting compared to controls ( Fig . 4B and 4D ) . When exposed to 10 drying/wetting cycles , a significant reduction ( p<0 . 05 ) in amplification was only observed for SCL-bound DY by 68% and not for unbound DY ( Fig . 4B and 4D ) . SCL-adsorbed CWD was prepared as described in Table 1 . Compared to the untreated samples ( Dry 0 ) , the PrPSc abundance did not significantly ( p>0 . 05 ) differ between SCL-bound and unbound CWD with up to 10 cycles of drying and wetting ( Fig . 5A and 5C ) . The PMCA conversion efficiency of unbound CWD after 3 rounds of PMCA was not significantly ( p>0 . 05 ) different through 10 repeated cycles of drying and wetting compared to controls ( Fig . 5B and 5D ) . In contrast , a significant ( p<0 . 05 ) reduction of 83% in PMCA conversion efficiency of SCL-bound CWD after 10 cycles of drying and wetting treatment was observed ( Fig . 5B and 5D ) . Unbound HY or SCL-adsorbed HY were subjected to 3 , 5 or 7 repeated rounds of drying and wetting . Samples were then subjected to 1 round of PMCA and the abundance of amplified PrPSc was quantified . Unbound HY had similar ( p>0 . 05 ) conversion efficiency at 1 , 3 , 5 and 7 repeated cycles of drying and wetting . However , 10 cycles of repeated drying and wetting resulted in a significant ( p<0 . 05 ) reduction in HY PrPSc amplification compared to the sample treated with 1 cycle of drying and wetting ( Fig . 6A and 6C ) . In contrast , after 3 repeated rounds of drying and wetting of SCL-bound HY amplification was significantly ( p<0 . 05 ) inhibited ( Fig . 6B and 6D ) . These results demonstrate that , under the conditions tested , binding to SCL enhances the reduction in conversion efficiency induced by repeated cycles of drying and wetting . Unabsorbed and SCL-adsorbed HY was subject to 0 or 10 serial rounds of wetting and drying . The samples were adjusted to equalize the abundance of PrPSc between the samples . Ten-fold serial dilutions , ranging from 10-2 to 10-8 , of these samples were subject to one round of PMCA . PMCA reactions seeded with untreated HY-SCL resulted in detectable PrPSc though the 10-4 dilution , while PMCA reaction seeded with an equal amount of HY-SLC PrPSc that was treated with 10 serial rounds of wetting and drying resulted in detectable PrPSc though the 10-2 dilution ( Fig . 7 ) . These results indicate that 10 serial rounds of wetting and drying reduce the specific activity of SCL absorbed HY PrPSc by two logs . Selected drying and wetting treated samples and untreated controls were intracerebrally inoculated into Syrian hamsters . Incubation periods for hamsters inoculated with HY , SCL- , SiO2- , and SLS-bound HY subjected to 0 , 1 , or 10 drying/wetting cycles are summarized in Table 2 and the survival results are presented in Fig . 8 . Consistent with PK-resistance and PMCA results ( Fig . 2 and 3 ) , the incubation period of hamsters inoculated with SCL-HY subjected to 1 cycle of drying and wetting did not significantly ( p>0 . 05 ) differ compared to hamsters inoculated with untreated SCL-HY ( Fig . 8 and Table 2 ) . The incubation period of HY- and SCL-HY-inoculated hamsters subjected to 10 cycles of drying/wetting was significantly ( p<0 . 05 ) extended 13 days compared to that of hamsters inoculated with the untreated control ( Fig . 8 and Table 2 ) . This extension of the incubation period is consistent with a 2 log reduction in prion titer [21] . The incubation period of hamsters inoculated with SLS-HY and SiO2-HY treated with 10 cycles did not significantly ( p>0 . 05 ) differ compared to hamsters inoculated with untreated control ( Fig . 8 and Table 2 ) .
Repeated cycles of drying and wetting degraded not only PrPSc but also other proteins in the brain homogenate whereas binding to soil prior to repeated cycles of wetting and drying eliminated this effect . ( Fig . 1 ) . While the exact mechanism of prion protein degradation due to repeated cycles of drying and wetting are not known , several possibilities exist . We hypothesize that exposure to repeated cycles of drying and wetting results in protein conformational changes that render PrPSc more susceptible to degradation . Loss of water and changes in ion concentrations and pH in solution may occur during dehydration which can affect the secondary structure of proteins [43 , 46–48] , however , changes in ionic strength or pH were not observed between cycles in this study . Although poorly understood , changes in soil properties such as surface charge or cation exchange capacity occur after repeated drying and wetting cycles [49 , 50] that can result in desorption and/or reorganization of adsorbed compounds including proteins . Alternatively , consolidation of the bonding between soil and the adsorbed PrPSc after drying may make PrPSc desorption more difficult resulting in reduced PrPSc detection and prion infectivity . The implication of this possibility is that PrPSc desorption is required for prion conversion and western blot detection . This explanation is consistent with previous findings that PrPSc attached to stainless steel surfaces ( dried onto surface or as incubation solution ) becomes more resistant to decontamination when exposed to extended drying compared with no drying or wet storage condition [51 , 52] . We speculate more compact stacking of PrPSc aggregates on the surface may occur during drying as water molecules evaporate , minimizing PrPSc exposure to the surroundings . The reduced PMCA conversion coefficient in combination with the extended incubation period in hamster bioassay compared to untreated prions ( Fig . 3 and 8 , Table 2 ) are consistent with a 2 log reduction in prion infectivity of soil-bound prions [21] after 10 cycles of drying/wetting . While both PMCA and bioassay data are in agreement with a 2 log reduction in titer following 10 cycles of drying/wetting , since titer was not directly calculated , this value should be interpreted with caution . It is unknown if additional cycles of wetting will further reduce infectivity or if complete prion inactivation is possible . The observed reduction in infectivity is not entirely due to loss of PrPSc . When standardized for the amount of starting PrPSc , the PMCA conversion coefficient of HY bound to SCL that has not been treated to repeated cycles of wetting and drying is two orders of magnitude greater compared to HY-SCL that has been repeatedly wetted and dried for 10 cycles ( Fig . 6 ) . Since the reduction in the specific activity of PrPSc is enhanced when HY is bound to SCL , we hypothesize at the PrPSc-SCL interface , the wetting and drying process is altering a property of PrPSc that renders it less infectious . Changes in PrPSc abundance and PMCA conversion efficiency after repeated cycles of drying and wetting are observed for a subset of unbound and soil-bound PrPSc . HY and DY PrPSc are more susceptible to PK digestion following repeated cycles of drying and wetting compared to CWD ( Figs . 2 , 3 , 4 , and 5 ) . Binding of DY PrPSc to SCL protects DY PrPSc by enhancing PK resistance but results in a reduction in DY and CWD PrPSc conversion activity ( Figs . 4 and 5 ) . Adsorption of the same PrPSc to different soils resulted in a greater or lesser effect of repeated drying/wetting on PrPSc properties ( Figs . 2 and 3 ) . Factors that may contribute to the variation include soil surface properties and soil-prion bonding . Since the percentage of sand and clay in a soil resulted in different soil stability changes in response to drying/wetting cycles we hypothesize that clay-clay and clay-sand interactions can indirectly affect the soil-prion interface [41] . Overall , this dynamic can affect which prion strains persist and the relative titer in any given environment . Since the relative ratios of prion strains can affect strain emergence , the environment would be expected to have a strong influence on the overall dynamics of transmission and strain emergence . Since prions are likely to be immobile in surface soils [26] , soil-bound prions are readily accessible to ambient weather conditions which can include changes in surface soil moisture . In some CWD-endemic areas such as Phantom Valley ( Latitude: 40 . 4°N; Longitude: 105 . 9°W ) close to Rocky Mountain National Park , the number of moisture change in surface soil ( soil depth is 0 . 5 m ) can be up to 28 per month [53] . Based on our findings , drying/wetting cycles which repeatedly change soil moisture may be a natural degradation pathway for soil-bound prions . Additionally , wetting and drying can change microbial activity in soils [39 , 40] which may affect prion-soil interactions . Ambient temperature likely does not contribute to natural prion degradation since most heat induced decontamination ( incomplete inactivation ) occurs at temperatures well over 100°C [54–57] . Enzymes secreted from soil microorganisms , such as serine proteases , may be auxiliary for soil-bound prion degradation and inactivation , however , they were only found to be effective at high temperature , high pH or both [58 , 59] . Overall , this study provides the first evidence that natural processes can reduce prion infectivity . Since the total environmental prion load is a balance between addition of prions to the environment and clearance of prions from the environment , efforts to limit prion input into the environment may positively affect this balance and have meaningful results in reducing environmental transmission of prion diseases . Additionally , the soil composition and hydrology of an area may shape the overall transmission dynamics and alter strain prevalence of prions .
Prion-infected brain tissues were collected from hamsters infected with either the hyper ( HY ) or drowsy ( DY ) strain of transmissible mink encephalopathy ( TME ) or from a naturally infected elk with CWD agent as described previously [20] . Infected brains were homogenized to 10% ( w/v ) in Dulbecco’s phosphate-buffered saline ( DPBS ) without Ca2+ or Mg2+ ( Mediatech , Herndon , VA ) using strain-dedicated Tenbroeck tissue grinders ( Kontes , Vineland , NJ ) . Uninfected Syrian hamster and uninfected Tg ( CerPrP ) 1536 mouse brain was homogenized to 10% ( w/v ) in ice-cold PMCA conversion buffer ( DPBS [pH 7 . 5] containing 5 mM EDTA , 1% ( v/v ) Triton X-100 , and a complete protease inhibitor tablet [Roche Diagnostics , Mannheim , Germany] ) . The homogenate was centrifuged at 460×g for 30 seconds and the supernatant was collected and stored at -80°C . Soils and soil minerals used in this study included sterile Rindasilty clay loam ( SCL ) soil ( a VerticEpiaqualf ) ; sterile Dickinson sandy loam soil ( SLS , a TypicHapludoll ) ; sodium bentonite clay ( CETCO , Arlington Heights , IL ) ; silicon dioxide powder ( Sigma Aldrich , St . Louis , MO ) ; humic acid ( HA ) -coated silica gel particles ( SiO2 HA ) ; and gamma-irradiated fine white sand ( Fisher Scientific , Pittsburgh , PA ) . Physicochemical properties of these soils and soil minerals have been described previously [19 , 38] . To obtain soil bound prions , 10% brain homogenate ( BH ) was added to soil and for each soil or soil mineral , the incubation time and prion to soil ratios , were selected based on previous studies [19 , 20] ( Table 1 ) . Each BH-soil combination was prepared in triplicate . The BH-soil mixture was rotated at 24 rpm ( Mini Labroller , Edison , NJ ) at room temperature . Samples were removed after incubation and centrifuged at 100× g for 5min . The supernatant was removed and the pellets were washed a minimum of three times with 1× DPBS . The soil pellets were resuspended in 1× DPBS at concentrations described in Table 1 and were stored at -80°C until use . HY TME , DY TME , and elk CWD BH were used as unbound controls . Each sample was placed in an uncapped 200 µL PCR tube ( Thermo Scientific ) and incubated at 40°C . Samples were dried and weighed periodically until there was less than a 0 . 5% change in weight resulting in a minimum of 7 hr drying time . To perform consistently , around 12 hours’ drying is selected for one cycle . Dried samples were rehydrated with 10 µl of ultrafiltered deionized water and mixed thoroughly . The drying followed by rewetting constituted one drying/wetting cycle . Conductivity and pH were measured with an Oakton 700 bench top meter using a 3-point calibration curve . After the desired number of treatment cycles , samples were stored at -80°C until use . Intracerebral ( i . c . ) inoculations of Syrian hamsters ( Harlan Sprague-Dawley , Indianapolis , IN ) were conducted as described previously [30 , 60] . Silty clay loam soil HY TME ( untreated , and 1- and 10-drying/wetting-cycle treated ) and silicon dioxide powder adsorbed HY TME ( untreated , and 10-drying/wetting-cycle treated ) were selected as inocula . The incubation period was determined as the length of time in days between inoculation and the onset of clinical signs of HY TME . Protein misfolding cyclic amplification ( PMCA ) was performed as described previously [61] . Sonication was performed with a Misonix ( Farmingdale , NY ) 4000 sonicator with amplitude set to level 75 , generating an average output of 160 W during sonication treatment . Samples were diluted with 10% ( w/v ) uninfected hamster or elk brain homogenate at 1:100 for HY TME and CWD , and 1:20 for DY TME for the first round . After one round , homogenate from round 1 was diluted at 1:20 for HY TME , 1:10 for elk CWD , and 1:1 for DY TME for the subsequent rounds . Each round was performed at 37°C for either 24 hr for HY TME and DY TME consisted of 144 cycles of 5 s sonication followed by 9min 55s of incubation or 48 hr for elk CWD consisted of 288 cycles with the same sonication/incubation time as HY TME and DY TME . Before each PMCA round , an aliquot was placed at -80°C as an unsonicated control . Samples containing only 10% ( w/v ) uninfected brain homogenate were included with each PMCA round as negative controls . Western blot analysis was performed as described previously [62] . Briefly , samples were incubated and digested with 22 . 5 µg/ml ( HY TME/DY TME ) or 45 µg/ml ( elk CWD ) proteinase K ( PK ) ( Roche Diagnostics Corporation , Indianapolis , IN ) at 37°C for 30 min ( HY TME/DY TME ) or 1 hr ( CWD ) with constant agitation . The PK digestion was terminated by boiling in 1x SDS-PAGE sample buffer ( final concentration ) . The samples were size fractionated with 12 . 5% SDS-PAGE and transferred to a polyvinylidenedifluoride membrane ( NuPage; Invitrogen , Carlsbad , CA ) . The membrane was blocked with 5% w/v nonfat dry milk in 1× TTBS ( Bio-Rad Laboratories , Hercules , CA ) for 30 min . Hamster samples were immunoblotted with MAb 3F4 ( Chemicon , Temecula , CA; 1:10 , 000 ) . Elk/Tg ( CerPrP ) 1536 samples were immunoblotted with 8H4 ( 1:10 , 000 ) . The blots were developed with Supersignal West Femto maximum sensitivity substrate , according to the manufacturer’s instructions ( Pierce , Rockford , IL ) , imaged on a 4000R imaging station ( Kodak , Rochester , NY ) , and analyzed using Kodak ( New Haven , CT ) molecular imaging software , V . 5 . 0 . 1 . 27 . Densities of sample replicate ( n≥3 ) intensities were standardized to brain homogenate controls on the same gel to control for inter-gel variance . PMCA amplification is determined as the absolute difference of intensity density between unamplified and sonicator-amplified samples . For each type of soil , unbound prions and untreated samples ( Dry 0 ) were used as controls to determine the effect of drying/wetting ( Dry 1 and Dry 10 ) on PrPSc seeding efficiency . Intensities of the same control were averaged out through the entire study . Statistical analysis ( Student’s t test with Welch’s correction , two-tailed P value ) was performed using Prism 6 . 0 ( GraphPad Software , Inc . , San Diego , CA ) by separately comparing samples with each treatment to the untreated control or samples with the least treatment . Separated proteins in the polyacrylamide gel were stained with SUPRO Ruby following the protocol provided by the manufacturer . Briefly , the gel was placed in a clean plastic dish and incubated in a fixative solution containing 10% methanol and 7% acetic acid at room temperature with gentle agitation for two 15 minutes . Then the gel was incubated in undiluted SYPRO Ruby staining solution overnight without being exposed to light . Stained gel was then transferred to a clean plastic dish and washed with 10% methanol and 7% acetic solution for two times followed by one time rinse with MQ H2O . The gel was imaged in an electrophoresis gel imaging imager cabinet ( Bio-rad Universal Hood ii ) using UV epi-illumination and analyzed by a 1-D analysis software Quantity One version 4 . 6 . 7 . The lightness density of samples of interest were compared and significance was analyzed with build-in t test ( two-tailed p value with unequal variance ) in Microsoft Excel 2013 . All procedures involving animals were approved by Creighton University Institutional Animal Care and Use Committee ( protocol number 0 872 and 0881 ) and comply with the Guide for the Care and Use of Laboratory Animals .
|
Prion diseases such as chronic wasting disease and scrapie are emerging in North America at an increasing rate . Infectious prions are introduced into the environment from both living and dead animals where they can bind to soil . Little information is available on the effect of prion inactivation under conditions that would be found in the natural environment . In this study , we exposed both unbound and soil-bound prions to repeated cycles of drying and wetting to simulate ambient environmental conditions . We found evidence of prion inactivation in both unbound and soil bound prions . The influence of repeated cycles of drying and wetting are dependent on the prion strain and soil type used and , interestingly , prions bound to soil were more susceptible to inactivation . This is the first report of natural environmental processes mitigating prion infectivity . This data suggests that the total environmental prion load is a balance between input and natural clearance .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Mitigation of Prion Infectivity and Conversion Capacity by a Simulated Natural Process—Repeated Cycles of Drying and Wetting
|
Intrinsically disordered proteins ( IDPs ) are frequently associated with human diseases such as cancers , and about one-fourth of disease-associated missense mutations have been mapped into predicted disordered regions . Understanding how these mutations affect the structure-function relationship of IDPs is a formidable task that requires detailed characterization of the disordered conformational ensembles . Implicit solvent coupled with enhanced sampling has been proposed to provide a balance between accuracy and efficiency necessary for systematic and comparative assessments of the effects of mutations as well as post-translational modifications on IDP structure and interaction . Here , we utilize a recently developed replica exchange with guided annealing enhanced sampling technique to calculate well-converged atomistic conformational ensembles of the intrinsically disordered transactivation domain ( TAD ) of tumor suppressor p53 and several cancer-associated mutants in implicit solvent . The simulations are critically assessed by quantitative comparisons with several types of experimental data that provide structural information on both secondary and tertiary levels . The results show that the calculated ensembles reproduce local structural features of wild-type p53-TAD and the effects of K24N mutation quantitatively . On the tertiary level , the simulated ensembles are overly compact , even though they appear to recapitulate the overall features of transient long-range contacts qualitatively . A key finding is that , while p53-TAD and its cancer mutants sample a similar set of conformational states , cancer mutants could introduce both local and long-range structural modulations to potentially perturb the balance of p53 binding to various regulatory proteins and further alter how this balance is regulated by multisite phosphorylation of p53-TAD . The current study clearly demonstrates the promise of atomistic simulations for detailed characterization of IDP conformations , and at the same time reveals important limitations in the current implicit solvent protein force field that must be sufficiently addressed for reliable description of long-range structural features of the disordered ensembles .
Cellular signaling and regulation are frequently mediated by proteins that , in part or as a whole , lack stable structures under physiological conditions [1–3] . Such intrinsically disordered proteins ( IDPs ) are over-represented in disease pathways [4 , 5] . About ~25% of disease- associated missense mutations can be mapped into predicted disordered regions [6] ( although cancer mutations appear to prefer ordered regions [7] ) . Many disease mutations in disordered regions have been predicted to alter the residual structure level [8] , which could potentially perturb interaction networks and lead to mis-signaling and mis-regulation . Establishing the biophysical basis of how disease mutants affect the “structure”-function relationship of IDPs is a formidable task . It requires detailed characterization of the disordered conformational ensembles , which are not amenable to traditional structural determination using either X-ray crystallography or nuclear magnetic resonance ( NMR ) spectroscopy [9–11] . For disordered protein states , only ensemble-averaged properties are generally measured [12 , 13] , and single-molecule techniques are often limited by low spatial resolution and labeling complications [14–16] . Recovering the underlying structural heterogeneity using ensemble-averaged properties is fundamentally underdetermined; there is not sufficient constraint ( or information ) to uniquely define the structure ensemble based on averaged properties alone . A possible strategy to overcome this fundamental limitation is to leverage significant recent advances in physics-based protein force fields and enhanced sampling techniques to calculate de novo structural ensembles [10 , 17] . Structural data from NMR and other biophysical experiments can be then used for independent validation , but not as structural restraints during the ensemble calculation . This strategy has proven effective enough to provide useful insights on studies of several relatively small IDPs [18–23] . An important caveat is , however , de novo ensembles will inevitably contain artifacts due to persisting limitations in the current protein force fields as well as conformational sampling capability . Nonetheless , certain systematic artifacts could be suppressed by examining how the calculated ensembles depend on sequence variations , post-translational modifications , and/or solution conditions [23 , 24] . To assess the efficacy of atomistic simulations for understanding the mutant-structure-function relationship of IDPs , we exploit the intrinsically disordered transactivation domain ( TAD ) of the tumor suppressor p53 and its cancer-associated mutations as a model system of great biological and biomedical significance . p53 is the most frequently mutated protein in cancer [25 , 26] . The p53 levels are kept low in unstressed cells through continuous proteasomal degradation . Cellular stresses such as DNA damage , initiate a cascade of phosphorylation events that stabilize and activate the p53 protein [27] . Accumulation of activated p53 induces the transcription of genes involved in cell cycle arrest and apoptosis , thus suppressing cell transformation and tumor formation [28] . Most human cancers exhibit defects in the p53-signaling pathway , over 50% of which involve inactivated p53 due to various mutations [29 , 30] . Clinical studies of breast cancer have indicated that the type of p53 mutation can be linked to cancer prognosis and response to drug [31] . It is thus crucial to determine the molecular basis of p53 inactivation by various types of mutations , so as to understand the biological consequences and predict potential treatment responses and patient survival . The p53 protein contains several distinct functional domains ( Fig 1A ) . The core DBD domain binds to the regulatory regions of target genes , and the terminal domains interact with many proteins that together tightly regulate the p53 protein level , localization , oligomerization and activity [26] . The primary focus of existing structural and functional studies has been on cancer mutants in DBD [32] , which harbors over 80% of p53 cancer mutations including established cancer “hot spots” [33] . Aided by several crystal structures [34 , 35] , the molecular basis for p53 inactivation of DBD cancer mutants can be understood in terms of either disrupting DNA contacts , perturbing the structure of DNA-binding interface , or affecting the DBD stability [32] . In contrast , very little is known about the structural and functional impacts of cancer mutants in the regulatory domains and particularly TAD . This could be attributed to much lower prevalence , and thus perceived importance , of cancer mutants outside of DBD ( e . g . , ~1 per residue in TAD vs . ~6 per residue in DBD ) [33] . Nonetheless , TAD cancer mutants appear to be frequently associated with some cancers . Two out of the thee documented female genital cancers contain mutants in TAD ( E17D and K24N ) ; over 5% nasal cavity , tonsil , salivary gland and parotid gland cancers involve mutated TAD ( statistics extracted from the IRAC TP53 mutation database , version R15 [33] ) . At present , available functional knowledge of all known TAD cancer mutants ( see Fig 1A ) largely comes from a single yeast-based transcriptional activity essay study of all possible point mutations in the entire coding region of p53 gene [36] ( with a few exceptions [30 , 33] ) . Moreover , no structural or molecular interaction data is available on any TAD cancer mutants except K24N [37] . A key complication in molecular studies of p53 TAD cancer mutants is that , in contrast to DBD with a stable fold , TAD is an IDP and must be described by heterogeneous structure ensembles [38–43] . In this work , we exploit the recently developed replica exchange with guided annealing ( RE-GA ) enhanced sampling technique [44 , 45] to calculate disordered ensembles of p53-TAD at atomistic level and examine how cancer-associated mutations could modulate the disordered ensembles to potentially disturb p53’s interactions with key regulatory proteins . RE-GA extends the popular temperature replica exchange ( T-RE ) method by introducing annealing cycles , during which the temperature exchange attempt patterns are modified for a selected replica to guide its diffusion through the temperature ladder more rapidly . The GA cycles help to overcome the limitation of RE in accelerating entropically limited cooperative conformational transitions [46–48] , albeit at the expense of compromising the detailed balance for systems with large activation enthalpies [44] . For IDPs with relatively small conformational transition barriers , numerical experiments and atomistic simulations of a small 28-residue IDP have demonstrated that RE-GA introduces minimal conformational biases and could generate converged ensembles with 3–5 fold speedup compared to T-RE [44] . The convergence of RE-GA simulations will be carefully examined by comparing results from independent simulations initiated from contrasting structures . The quality of simulated ensembles will be critically assessed by direct comparison with a wide range of existing data that provide structural information on both the secondary and tertiary levels for the wild-type protein and one of its mutants [37 , 39 , 40 , 43] . Further analysis of all resulting atomistic ensembles will then be performed to obtain a preliminary understanding of how cancer-associated mutations may introduce both local and long-range structural changes in unbound p53-TAD , which could have functional consequences on how p53-TAD may differentially interact with key regulatory pathways and on how these differential interactions may be regulated through multi-site phosphorylations .
The convergence of the simulated ensembles has been evaluated by examining the self-convergence of various one-dimensional and multi-dimensional distributions , and more critically by comparing the results derived from independent control and folding runs that were initiated from contrasting initial structures . As shown in Fig 2 for the wild-type p53-TAD , the residue helicity profiles calculated using various 80-ns segments quickly reach stationary states , showing very small differences between profiles calculated using data from 40–120 ns or 120–200 ns of the simulations ( Fig 2A ) . The simulated ensembles for all five p53-TAD cancer mutants display similar convergence behaviors ( see S1 Fig ) . Importantly , the profiles calculated using the last 80-ns segments of the control and folding runs agree very well , with an overall RMSD of 0 . 014 . Similar observations can be made on comparing various distributions of 1D residue-residue distances ( e . g . , Fig 2B , red and black traces ) . The simulated ensembles also appear to converge well on level of two—dimensional distributions , which is very difficult to achieve for IDPs of the size of p53-TAD . S2 Fig illustrates that helical substate distributions largely stabilize by the end of 200-ns RE-GA simulations for both the wild-type p53-TAD and its cancer mutants and that the final distributions from the control and folding runs are largely consistent . Furthermore , as shown in Fig 3 , the structural ensembles derived from the control and folding simulations of the wild-type protein contain essentially identical sets of long-range contacts and with largely similar probabilities . The correlation coefficient of the two contact maps is 0 . 91 and the RMSD is 0 . 016 . The level of convergence observed here for local and long-range structural properties of a 61-residue IDP is noteworthy . It provides a solid basis for detecting potentially subtle structural impacts of cancer-associated mutations . The quality of the simulated ensembles has been assessed by comparing to existing experimental data that provide structural information on both the secondary and tertiary levels [37–40 , 43] . As shown in Fig 4A , the simulated helicity profile for the wild-type p53-TAD is highly consistent with NMR secondary chemical shift and NOE analysis[38] , predicting three partial helices in the same regions that show significant negative secondary chemical shifts , namely residues 18–27 , 40–44 and 48–52 . These are also the same regions where short helices have been observed when p53-TAD is bound to various targets ( see Fig 1A ) . The partial helices spanning residues 40–44 and 48–52 have been generally classified as turns I and II in previous NMR studies [38] . Nonetheless , continual sequential dNN NOEs have been detected in both regions , which support the presence of residual helices [38] . The most recent NMR analysis has estimated that the average helicity in residues 17–29 is about 11 . 2% [37] , which is quantitative agreement with the calculated value of ~10±1% in residues 18–27 from the simulations . Furthermore , as shown in Fig 4B , the theoretical RDC profiles derived from the simulated ensembles agree very well with the experimental one measured at 800 MHz [39] . For disordered protein states , RDC has been shown to be mainly sensitive to local secondary structures , particularly partial helices [39 , 49] . The agreement between calculated and measured RDC profiles thus further supports the notion that local structural properties of the simulated ensembles are most likely realistic . Long-range tertiary structural properties of the simulated ensembles have been examined based on their ability to reproduce the experimental PRE effects [42] . PRE coupled with site-directed spin-labeling techniques is one of the most powerful techniques for characterizing transient long-range contacts of disordered proteins [51–53] . Relaxation enhancement of a given proton depends sensitively on its distance from the unpaired electron of the paramagnetic spin label , with an r-6 dependence . PRE is thus uniquely suitable for detecting weakly populated transient contacts . At the same time , dominated by contributions from compact conformers , PRE is insensitive to members of the ensemble with large electron-nuclear distances . This property renders it generally unfeasible to calculate reliable structural ensembles for disordered protein states based on the PRE distances alone [54] . Nonetheless , the ability of PRE experiments to provide ensemble-averaged distance information between site-specific spin labels and all protons in the protein is extremely valuable for global validation of atomistic ensembles from de novo simulations . Fig 5 compares the theoretical PRE profiles calculated from the last 80-ns of the folding RE-GA simulation of wild-type p53-TAD with experimental results previously measured for four site-specific spin labels[43] . A key observation is that the theoretical profiles do not reach the 1 . 0 ( no broadening ) limit in any case . This suggests that the atomistic ensemble is overly compact , likely due to the known tendency of the GBSW/SA implicit force field to over-stabilize intra-peptide interactions [55 , 56] . Indeed , the end-to-end distance of the simulated ensemble ( Fig 2B ) appears substantially under-estimated compared to the single molecule FRET data [40] . Nonetheless , the calculated PRE profiles display fine features that appear to resemble the experimental ones . The simulations predict stronger PRE broadening in similar regions detected experimentally for all four spin-labeling sites located strategically to cover the whole sequence . The overall correlation coefficient of the theoretical and experimental PRE effects is about 0 . 5 , which is far from ideal but nonetheless meaningful . The implication is that , despite clear over-compaction , transient long-range contacts in the simulated ensembles are likely genuine , albeit likely with systematically elevated or skewed probabilities . We note that it is highly nontrivial for de novo atomistic simulations to generate well-converged ensembles for a 61-residue IDP like p53-TAD with non-trivial structures and achieve a high level of agreement with NMR on both secondary and long-range structural features . Implicit treatment of solvent environment is critical to reduce the computational cost , as also demonstrated quite extensively for other long IDPs [57–60] . The tendency of GBSW/SA to over stabilize collapsed structures , however , has hindered the ability of traditional T-RE simulations to generated converged ensembles for long IDPs , requiring us to adopt the RE-GA enhanced sampling here . With compromised detailed balance due to the GA cycles , the probabilities of high energy states tend to be over estimated when separated by large energy barriers [44] . Taken together , long-range structure features predicted by the current simulations should be considered qualitative at best . We note that several recently developed enhance sampling techniques may allow one to overcome the sampling limitation without compromising the detailed balance [61 , 62] . It should also be emphasized that agreement on average properties itself as discussed above does not establish the reliability of the whole ensemble , due to the under-determined nature of calculating heterogeneous structure ensembles . An essential validation will be the atomistic simulation’s ability to recapitulate the affects of mutations or post-translational modifications on the conformational properties . As will be discussed below , the latter appears to be the case for the K24N mutant . In Fig 6 , we first examine the effects of cancer-associated mutations on residue helical propensities of p53-TAD . Clearly , all cancer-associated mutants contain residual helices in the same regions as observed for the wild-type protein . However , the mutations appear to frequently modulate average helical propensities . Most effects are local . For example , the largest effects of replacing Trp53 with the helix breaking Gly residue are observed near residue 53 , where the peak residue helicity is reduced from ~8% ( black trace ) to ~3% ( purple trace ) . K24N mutation mainly reduces the helicity in residues 18–27 , from an average of ~10% to ~5% . We note that the predicted helicity reduction of K24N is in quantitative agreement with NMR secondary chemical shift analysis [37] . The effect of K24N mutation may be attributed to direct disruption of the Asp21-Lys24 salt bridge , which has been suggested to stabilize the local partial helices [38] . As shown in Fig 2B , the probability of forming contacts between residues 21 and 24 is ~50% lower for K24N mutant than the wild type ( green trace ) . On the helical substate level , while all p53-TAD sequences simulated here apparently sample a similar , if not identical , set of partial helices ( Fig 7 ) , their occupancies appear to be sensitive to mutations . We note that the convergence of helical substate distributions is more limited compared to average residue helicity profiles ( S2 Fig ) . Nonetheless , the level of redistribution of among helical sub-states due to mutation appears significant . In particular , the differences between distributions calculated from folding and control RE-GA simulations of the wild-type sequence are considerably smaller than those between the wild-type and mutant distributions ( S2 Fig ) . On the tertiary level , all sequences are extremely heterogeneous . Clustering analysis with 5 Å Cα RMSD cutoff leads to numerous small clusters for all ensembles , with very few clusters occupied over 1% ( see S3–S8 Figs ) . Using larger cutoff values reduces the total number of clusters identified but no dominant clusters would emerge . Interestingly , on average all p53-TAD constructs simulated here appear to sample similar sets of long-range residue-residue contacts , even though cancer mutants do clearly impact their prevalence in the disordered ensemble ( see Fig 8 ) . Intriguingly , several cancer mutants are predicted to lead to helicity changes in regions sequentially distal from the mutation sites . For example , besides significantly reducing the local helical propensity , W53G also leads a slight decrease in helicity within residues 18–27 ( Fig 6 , purple trace ) . The most striking case is N29K/N30D , which reduces average residue helicities in the distal regions of residues 40–44 and 48–52 by ~50% , but has minimal impact in the local region of residues 18–27 ( blue trace ) . This is a potentially important observation , and suggests that long-range coupling exists among various residual structures of p53-TAD . The predicted long-range coupling is not likely an artifact of over compaction due to limitations of GBSW/SA . Similar long-range coupling in p53-TAD dynamics has been detected in a recent florescence quenching study of p53-TAD [41] . The existence of transient long-range contacts between residual helices is also evident in PRE experiments[43] . As shown in Fig 5 , paramagnetic spin labeling at E28C leads to strong broadening around residues 43–47 and 52–54 . Conversely , labeling at A39C leads to significant broadening around residues 18–27 . Interestingly , comparing the contact probability maps ( Fig 8 ) suggests that both K24N and N29K/N30D appear to weaken long-range contacts between the N- and C-terminal segments compared to the wild type p53-TAD ( Fig 8 , circled areas ) . These segments of substantial helical propensities are responsible for p53’s specific interactions with numerous regulatory proteins ( e . g . , see Fig 1A ) . At present , little concrete biophysical data is available on how TAD cancer mutants may perturb p53’s interaction with various regulatory proteins . The only molecular data available is that K24N does not significantly affect MDM2 binding due to an apparent enthalpy-entropy compensation[37] , but its impacts on binding to CBP domains are not known . It is plausible the resulting structural changes in the disordered ensembles could have impacts on molecular interactions of p53 as well as their post-translational regulation .
The p53 protein level and activity are tightly regulated through coordinated interactions of TAD with negative regulators MDM2 and MDMX ( mouse double minute 2 and 4 ) and the general transcriptional coactivators CBP and p300 [63] ( see Fig 1B ) . Unphosphorylated p53-TAD binds to MDM2 with sub-micromolar affinity , which promotes polyubiquitination and degradation of p53 through MDM2’s E3 ubiquitin ligase activity[64 , 65] . Recent NMR and calorimetry studies showed that multisite phosphorylation of TAD reduced binding to MDM2 ( by up to 24X , or ΔΔG ~ −1 . 9 kcal/mol ) , and at the same time provided graded enhancement of binding to CBP/p300 domains ( by up to 80X , or ΔΔG ~ +2 . 6 kcal/mol ) [66–68] . These effects together dramatically shift the balance towards favoring binding to CBP/p300 , up to 1000-fold . The graded dependence on the extent of p53 phosphorylation provides a mechanism for gradually increasing p53 response under prolonged genotoxic stress[69] . Nonetheless , precisely how phosphorylation regulates the binding affinities is not entirely clear . Phosphorylation may simply provide a new interaction site and/or disrupt the binding interface . However , available structures of complexes involving p53-TAD[70–73] show that TAD interacts with other proteins mainly via two short helices ( see Fig 1A ) . The simple interaction or interface interruption mechanism thus cannot explain the effects of phosphorylation at several sites outside of the helical segments . Instead , the unbound state of p53-TAD must also be considered . Specifically , the disordered ensemble of free TAD is highly susceptible to post-translational modifications , which could alter the level of residual structures and modulate the entropy cost of folding upon specific binding to regulate the binding affinity . Such a mechanism has been demonstrated in our previous study of the CREB/CBP interaction[19] . The molecular mechanism of p53 activation by multisite phosphorylation is highly relevant for understanding how TAD cancer mutants may alter the spectrum of target gene transactivation[74] and contribute to the gradient of p53 tumor suppression function in cancers[75] . In particular , the current simulations strongly support that TAD cancer mutants can significantly modulate the unbound conformational ensembles , which could in turn disturb the balance between binding to MDM2 and CBP and further alter how the balance is regulated by multisite phosphorylation of TAD . Establishing the functional implications of the predicted cancer mutant modulation of the disordered ensembles will require additional experimental characterization of TAD cancer mutant structural properties as well as new biochemical and biophysical measurements of p53 binding thermodynamics with key regulatory proteins . The success of the current simulations demonstrates the feasibility and promise of combining advanced sampling techniques and modern atomistic force fields , particularly with implicit solvent , for effective IDP simulations . Coupled with appropriate structural and biophysical experiments , de novo atomistic simulations could provide a general framework for comparatively assessing the effects of disease-related mutations as well as post-translational modifications on IDP structure and interaction . At the same time , important limitations remain in implicit solvent protein force fields , and the simulated ensembles are overly compact . This has proven to be a key artifact that not only severely hinders our ability to generate highly converged ensembles but also greatly compromises reliable interpretation of predicted structural impacts of mutations . The current study thus also underpins the importance of continual development and optimization of implicit solvent protein force fields .
Fully extended and helical conformations of the wild-type p53-TAD ( residues 1–61: MEEPQ SDPSV EPPLS QETFS DLWKL LPENN VLSPLP SQAM DDLM LSPDDI EQWFT EDPGP D ) were first generated using CHARMM [76 , 77] . Both termini were neutralized . These initial conformations were then used to initiate two independent RE-GA simulations ( referred to as folding and control runs , respectively ) in the GBSW/SA implicit solvent [78–80] . The GBSW/SA force field is based on the CHARMM22/CMAP protein force field [81–84] , and has been previously optimized for simulation of conformational equilibria of small peptides . Despite several existing limitations [55 , 56] , it has been reasonably successful in simulating the disordered ensembles of several IDPs [18–20] and unstable protein states [85–87] . The SHAKE algorithm [88] was applied to fix lengths of all hydrogen-involving bonds , and the dynamics time step was 2 fs . The nonbonded interactions were cut off at 16 Å , and the salt concentration was set to 0 . 1 M in GBSW . All RE-GA simulations were performed using the Multi-scale Modeling Tools in Structural Biology ( MMTSB ) Toolset [89] together with CHARMM . Each RE-GA run involved 16 replicas distributed exponentially between 300 and 500 K . Temperature exchanges were attempted every 2 ps . The replica occupying the lowest temperature was selected to undergo GA every 2000 RE cycles after the completion of the previous GA cycle [44] . The total length of all RE-GA simulations was 200 ns per replica , which proved sufficient for achieving excellent convergence in the calculated ensembles ( see Results ) . The exchange acceptance ratios were about 25% . Additional 200-ns RE-GA simulations were initiated from fully extended conformations for five selected cancer-associated mutants of p53-TAD . E17D and K24N are frequently associated with female genital cancers [33]; D49Y and W53G are predicted to cause the largest changes in the disorder tendency based on metaPrDOS sequence analysis [90] and are associated with brain and bladder cancers , respectively [91 , 92]; and N29K/N30D is only complex cancer mutant known[33] and is associated with breast cancers [93] . Structural ensembles were constructed by collecting conformations sampled at 300 K during the RE-GA simulations . All subsequent structural and clustering analysis was performed using a combination of CHARMM , the MMTSB toolset and in-house scripts . Molecular visualization was generated using VMD [94] . For clustering analysis , the simulated ensembles were first under-sampled by only including snapshots sampled every 20 ps during the last 80 ns of each RE-GA simulations . The resulting 4000-member ensembles were clustered using the fixed radius clustering algorithm as implemented in the MMTSB/enscluster . pl tool ( with—kclust option ) , with a cutoff radius of 5 Å Cα root-mean-square distance ( RMSD ) . Theoretical residual dipolar coupling ( RDC ) values were computed from the simulated ensembles using the PALES software [95] , and the final ensemble-averaged RDC profiles were uniformly scaled to best reproduce the experimental data [39] . The theoretical paramagnetic relaxation enhancement ( PRE ) broadenings of several previously characterized sites of spin-label attachment ( D7C , E28C , A39C and D61C ) [37] were calculated for the wild-type p53-TAD . The theoretical ratios of 1H-15N HSQC peak intensities in the paramagnetic and diamagnetic samples were calculated as peak intensities in the paramagnetic sample , Iox and the diamagnetic sample Ired were calculated theoretically using the equation IoxIred=R2 exp ( -R2spt ) R2+R2sp with R2sp=Kr6 ( 4τC+3τC1+ωH2τC2 ) [96] . Here r is the ensemble-averaged residue-spin label distance , and K = 1 . 23×10-32 cm6s-2 for the interaction between a single electron and proton . The simulations did not include actual MTSL spin label used in NMR experiments [43] . Therefore , Cα-Cα distances were calculated to approximate the actual electron-proton separations . Consistent with the experimental work [43] , Larmor frequency ωH = 600 MHz , the average correlation time τC for the electron-nuclear dipole-dipole interaction is set to 3 . 3 ns , the average R2 relaxation time in absence of the paramagnetic center is set to 16 s-1 , and the duration of the INEPT delay is set to t = 9 . 8 ms .
|
Tumor suppressor p53 is the most frequently mutated protein in human cancers . Clinical studies have suggested that the type of p53 mutation can be linked to cancer prognosis , response to drug treatment , and patient survival . It is thus crucial to understand the molecular basis of p53 inactivation by various types of mutations , so as to understand the biological outcomes and assess potential cancer intervention strategies . Here , we utilize a recently developed replica exchange with guided annealing enhanced sampling technique to calculate well-converged atomistic conformational ensembles of the intrinsically disordered transactivation domain ( TAD ) of tumor suppressor p53 and several cancer-associated mutants in an implicit solvent protein force field . The calculated ensembles are in quantitative agreement with several types of existing NMR data on the wild-type protein and the K24N mutant . The results suggest that , while all sequences sample a similar set of conformational substates , cancer mutants could introduce both local and long-range structural modulations and in turn perturb the balance of p53 binding to various regulatory proteins and further alter how this balance is regulated by multisite phosphorylation of p53-TAD . The study also reveals important limitations in implicit solvent for simulations of disordered proteins like p53-TAD .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
|
Modulation of the Disordered Conformational Ensembles of the p53 Transactivation Domain by Cancer-Associated Mutations
|
Exposure to the environmental toxin β-methylamino-L-alanine ( BMAA ) is linked to amyotrophic lateral sclerosis ( ALS ) , but its disease-promoting mechanism remains unknown . We propose that incorporation of BMAA into the ALS-linked protein Cu , Zn superoxide dismutase ( SOD1 ) upon translation promotes protein misfolding and aggregation , which has been linked to ALS onset and progression . Using molecular simulation and predictive energetic computation , we demonstrate that substituting any serine with BMAA in SOD1 results in structural destabilization and aberrant dynamics , promoting neurotoxic SOD1 aggregation . We propose that translational incorporation of BMAA into SOD1 is directly responsible for its toxicity in neurodegeneration , and BMAA modification of SOD1 may serve as a biomarker of ALS .
Amyotrophic lateral sclerosis ( ALS ) is a motor neurodegenerative disease that affects 2–9 individuals per 100 , 000 every year [1] . More than 150 mutations to Cu , Zn superoxide dismutase ( SOD1 ) have been associated with ALS . Misfolded and aggregated SOD1 has been found in motor neurons in both sporadic and familial ALS [2] . In recent studies , a non-native trimeric oligomer of SOD1 has been shown to be toxic in the hybridized motor neuron cell line NSC-34 , suggesting a causative role of misfolded SOD1 aggregates in ALS etiology [3] . The phenomenon of SOD1 misfolding is puzzling due to the protein’s remarkable stability ( ΔΔG >20 kcal/mol ) [4]; the mild destabilization ( <5 kcal/mol ) caused by ALS-linked mutations [5] does not significantly reduce the stability of SOD1 from that of the average human protein ( ~5–15 kcal/mol [6 , 7] ) , and so does not explain SOD1 misfolding [8 , 9] . Previous studies have demonstrated that post-translational modifications of SOD1 can contribute to destabilization [10] , and that glutathionylation of Cys111 promotes SOD1 dimer dissociation , the required initial step for SOD1 aggregation [11] , by ~1 , 000 fold [12 , 13] . Environmental toxins that modify proteins have also been proposed to play a role in ALS etiology . The indigenous Chamorro population on Guam have an ALS incidence 100 times larger than the worldwide average , which has been linked to an enrichment of the toxin β-methylamino-L-alanine ( BMAA ) in their diet [14] . The quest for the mechanism of BMAA toxicity resulted in the hypothesis that this amino acid is misincorporated into proteins [15] , resulting in formation of inclusion bodies in neurons [16] . Studies have demonstrated synergistic toxicity of ALS-linked mutant SOD1 and BMAA [17] , yet no reports of misincorporation have been presented . A large-scale proteomic study has identified multiple proteins that featured misincorporated BMAA [18] . However , the reported misincorporation rates were low . Despite the low misincorporation rates , Ackerman and colleagues have argued that even a rate of 1 misincorporation per 10 , 000 codons can lead to neurodegeneration in mice [19] . Hence , identification of BMAA misincorporation into SOD1 may have been overlooked due to sensitivity issues , and never reported . We propose that misincorporation of BMAA into SOD1 destabilizes the protein , increases aggregation propensity , and thus promotes ALS onset and progression . We hypothesize that BMAA can directly modify SOD1 by incorporation in place of serine during translation . As a proof of principle , we perform a computational analysis predicting the effects on thermodynamic stability of substituting BMAA in place of each of the ten serines in SOD1 . We find remarkable destabilization of SOD1 due to BMAA misincorporation at all sites , strongly suggesting a direct role of this toxin on the etiology of ALS . We perform molecular dynamics simulations of modified SOD1S107B to evaluate the structural impact of such substitution , and find significant dynamic changes to residues participating in metal-binding and the intra-monomer disulfide bond , key structural determinants of SOD1 stability . These findings suggest a mechanism for the toxicity of BMAA in ALS , and provide support for the candidacy of BMAA as a long-sought biomarker for ALS .
We evaluate the effects of replacing serine residues with BMAA in the SOD1 dimer . Because misincorporation is a rare event , more than one instance in the same molecule would be unlikely , and thus we study the scenario of BMAA misincoporation into only one monomer of the SOD1 heterodimer . We computationally substitute each individual serine residue in SOD1 ( PDB ID: 1SPD ) to BMAA , and estimate the resulting changes in free energy ( ΔΔG ) of the structure . To control for the effect of computational mutation , we also perform the same calculation while converting the given residue to lysine . Lysine , similar to BMAA , is also an unbranched , positively-charged amino acid . We find that while mutations of each serine to either BMAA or lysine generally destabilize the SOD1 dimer ( Table 1 ) , mutations to BMAA result in significant destabilization , while mutations to lysine result in minor ( <2 kcal/mol ) or negligible ( <1 kcal/mol ) destabilization , and in some cases ΔΔG is within error of zero . We conclude from these results that substitution of BMAA for serine in the SOD1 structure results in an unfavorable structural shift resulting in thermodynamic destabilization , likely due to steric effects from the larger BMAA molecule . To obtain the thermodynamic melting curve of BMAA-SOD1 , we perform replica exchange DMD simulations at a wide range of temperatures . As a demonstration of potential effects of BMAA , we choose substitution of S107 , as the smallest predicted ΔΔG ( Table 1 ) upon misincorporation of BMAA . Misincorporation of BMAA at a site with a larger predicted ΔΔG would be likely to have larger thermodynamic effects . We find that the incorporation of BMAA into the SOD1 structure in place of serine-107 shifts the melting temperature of the protein by only ~2°C ( Fig 1A ) . However , we observe evidence of lower temperature localized unfolding events present in BMAA-SOD1 that are absent from the unfolding of WT-SOD1 , which displays one dominant peak in CV representing coupled dimer dissociation and monomer unfolding [13] . Supporting this hypothesis , we find that BMAA modification increases the potential free energy of the low-energy “ground state” of the SOD1 dimer , decreasing the stability of the native state ( Fig 1B ) . This destabilization makes BMAA-SOD1 more likely to undergo localized unfolding events that can expose toxic epitopes , as well as lead to the protein aggregation characteristic of ALS . This destabilization of the SOD1 dimer by BMAA substitution provides a mechanism for the linkage of BMAA poisoning to ALS etiology . To test our conclusion that mutation of serine to BMAA results in a significant structural change in SOD1 , we perform discrete molecular dynamics ( DMD ) simulations of SOD1 with BMAA incorporated into one monomer of the structure in place of Ser107 , the site at which BMAA misincorporation was predicted to have the smallest thermodynamic effect . Misincorporation of BMAA at a site with a larger predicted ΔΔG ( Table 1 ) would be likely to have larger structural changes . Upon building and equilibrating our model of BMAA-SOD1 , we find rearrangement of the beta-barrel of the modified monomer , and resulting lengthening and twisting of the beta-strands that form the SOD1 dimer interface ( Fig 1C ) , with a total root mean square structural deviation of 3 . 24 Å . Although metal ions are necessarily constrained to their ligands in our simulations , we note that the distortion of the SOD1 structure extends to shifts in the orientation of metal-binding residues , especially those coordinating Zn ( Fig 1D ) , which would potentially affect the binding affinity of Cu and Zn in vitro and in vivo . Binding of metal ions , especially Zn , contributes significantly to the stability of SOD1 , and destabilization and loss of bound metal ions is the second step in SOD1 aggregation [11] , and metal-binding residues feature several known ALS-linked mutations . To further investigate the potential effects of incorporation of BMAA into the SOD1 structure , we analyze the dynamics of the BMAA-modified protein in low-temperature steady-state simulations and compare with wild-type protein . Changes in root mean square fluctuation ( RMSF ) over the length of the protein ( Fig 2 , top ) upon BMAA modification reveal increased flexibility in the metal-binding loop ( residues 49–84 ) and the residues directly surrounding the BMAA modification , as well as flexibility differences caused by slight shifts in the residues included in β-strands 1 , 2 , and 3 due to the rearrangement in β-barrels discussed above . While changes in RMSF indicate differences in local stability , correlated dynamics are a more informative measure of the effect of protein modification on overall structure , stability , and function because they reveal dynamic coupling between distal regions of the protein [20] . Changes in dynamic coupling across SOD1 due to BMAA misincorporation would change not only local stability , but also how local instabilities are propagated to other regions of the protein , potentially resulting in additional changes to structurally important features . In calculating the correlated motions of residue pairs [20] , we find profound differences in the motions of residues corresponding to key structural features of SOD1 known to promote integrity of the properly folded structure ( Fig 2 ) : namely , both cysteines of the intra-monomer Cys57-Cys146; the Cu-binding histidines 46 , 48 , and 120; the Cu-Zn bridging ligand His63; the Zn-coordinating residues His71 , His80 , and Asp83; and the structurally important residue Asp124 , which forms a crucial connection between Cu- and Zn- binding residues and whose mutation has been linked to ALS [21] . We also observe significant disturbances to large portions of both the electrostatic loop and the metal-binding loop , which contribute to enzymatic function , maintain structural integrity , coordinate the binding of the metal ions , and prevent protein aggregation [22] . Together , these findings strongly support the conclusion that the incorporation of BMAA into SOD1 causes both static and dynamic structural disturbances that result in local destabilization of the region surrounding the modification , including the nearby electrostatic loop , and propagation of those instabilities to important structural features of the protein , leading to increased propensity for misfolding and aggregation . This work supports an SOD1-linked mechanism for the toxicity of BMAA in environmentally caused cases of ALS .
SOD1 dimer dissociation has been shown to be the first step in the misfolding and aggregation of SOD1 [11] . Proctor et al . [3] recently demonstrated that the association of misfolded SOD1 monomers into a non-native trimeric oligomer results in cytotoxicity in hybridized motor neurons . The remarkable thermodynamic stability of unmodified wild type SOD1 protects against this first necessary step of dimer dissociation [5] , thus also protecting against the formation of toxic oligomers . However , the addition of exogenous factors to the SOD1 structure , such as post-translational modifications , has been shown to have a profound destabilizing effect on dimer stability [10 , 12 , 23]; oxidative glutathionylation is a particularly severe example of such a modification [12 , 24] . Given the high fraction ( 90% ) of sporadic ALS cases as compared to those with a known genetic link , we have long hypothesized that other post-translational modifications may similarly impact SOD1 stability . BMAA is a good candidate because , while not overly abundant , this cyanobacteria-produced neurotoxin has been linked to significantly increased occurrence of sporadic ALS in populations with frequent dietary consumption of food sources containing high levels of BMAA [14 , 16] . In this work , we present the hypothesis , based on others’ experimental and epidemiological observations [15 , 18] , that BMAA can be incorporated into SOD1 , and demonstrate using computational structural analysis and simulation that incorporation of BMAA would promote SOD1 dissociation , loss of metals , and misfolding . Misfolded SOD1 then aggregates to form oligomers that , through as yet unknown mechanisms result in motor neuron death , thereby contributing to the neurotoxicity of BMAA and its linkage to sporadic ALS in areas of environmental contamination ( Fig 3 ) . We speculate that BMAA incorporation into SOD1 may be rare , explaining why this modification has not yet been reported . However , even rare events may promote an avalanche of misfolding events; the initiating destabilization by BMAA incorporation may serve as a nucleating event for the misfolding and aggregation of SOD1 through the templating mechanism [25–29] . Our analysis suggests the need for a comprehensive study of SOD1 modification patterns in ALS patients in order to uncover mechanistic patterns of disease onset and progression , and aid in understanding of potential lifestyle and preventative interventions for sporadic ALS .
We determine the changes in free energy ( ΔΔG ) for mutations of each serine residue in the SOD1 dimer ( PDB ID: 1SPD ) to β-methylamino-L-alanine ( BMAA ) or lysine using Eris [30 , 31] . Reported ΔΔG values represent the mean ± standard deviation of 20 independent rounds of Eris calculation . Each round of Eris calculation produces an expected value of the ΔΔG of mutation from 20 independent simulations for both wild-type and mutant protein with each simulation consisting of 20 steps of Monte Carlo optimization . The BMAA rotamer library was generated using the Rosetta MakeRotLib protocol [32] . We used the Gaussian 09 program ( Gaussian , Inc . ) to optimize the initial structure of BMAA at the HF 6-31G ( d ) level of theory with a polarized continuum model of the aqueous solvent , which appropriately shields the positive charge on the BMAA side chain . We generate a backbone-dependent rotamer library from the initial structure using 10° increments for both φ and ψ angles for a total of 1296 ( = 36 × 36 ) φ/ψ bins , within which each of the two χ angles of BMAA were sampled at 30° increments . The MakeRotLib protocol was used to obtain mean angles and probabilities for all combinations of the three staggered conformations for the two χ angles in each φ/ψ bin . Lysine parameters for Ramachandran probabilities , χ angle standard deviations , and the reference energy were used for both BMAA and lysine , as both feature unbranched , positively-charged side chains . The residue type parameter file for BMAA was built using pre-existing atom types in the CHARMM-based Medusa force field [33] . DMD implements step function potentials to describe inter-atomic interactions , as opposed to the continuous potentials used in traditional molecular dynamics ( MD ) [34–36] . We utilize an all-atom protein model that explicitly represents all heavy atoms and polar hydrogen atoms . Bonded interactions are represented using infinite square-well constraints for bond lengths , bond angles , and dihedral angles . Non-bonded interactions are adapted from the continuous CHARMM-based Medusa force field [33] , van der Waals interactions are modeled using the Lennard-Jones potential , and solvation interactions are modeled using Lazaridis-Karplus solvation [37] , all discretized by multi-step square-well functions for use in DMD . We model hydrogen bonding interactions using the reaction algorithm [38] . The DMD simulation engine ( πDMD , v1 . 0 ) with Medusa all-atom force field is available from Molecules In Action , LLC ( free to academic users , moleculesinaction . com ) . Using the known X-ray crystallographic structure of wild type SOD1 ( PDB ID 1SPD ) as a reference structure , we deleted serine 107 from one monomer and replaced it with BMAA , which was joined in the peptide chain of SOD1 using peptide bond constraints and equilibrated using the discretized Medusa force field [33] in DMD with an iterative relaxation and equilibration protocol as previously described [13] . We use the replica exchange method to construct a thermodynamic profile of BMAA-SOD1 unfolding [39] . Independent replicas of the simulation system of interest are run in parallel at 16 different temperatures: 0 . 48 ( ∼242 K ) , 0 . 495 ( ∼ 249 K ) , 0 . 51 ( ∼ 257 K ) , 0 . 525 ( ∼ 264 K ) , 0 . 54 ( ∼ 272 K ) , 0 . 555 ( ∼ 280 K ) , 0 . 57 ( ∼ 287 K ) , 0 . 585 ( ∼ 295 K ) , 0 . 60 ( ∼ 302 K ) , 0 . 615 ( ∼ 310 K ) , 0 . 63 ( ∼ 317 K ) , 0 . 645 ( ∼ 325 K ) , 0 . 65 ( ∼ 327 K ) , 0 . 67 ( ∼ 337 K ) , 0 . 69 ( ∼ 347 K ) and 0 . 71 ( ∼357 K ) kcal ( mol kB ) –1 . Every 50 ps , replicas neighboring in temperature attempt to exchange temperature values according to the Metropolis criterion . The replica exchange method increases sampling efficiency by allowing energetic barriers to be overcome with exposure to higher temperatures . We note that temperatures used in MD simulations do not directly equate to physical temperatures , but are useful to evaluate relative differences between systems . Replica trajectories were combined for the analysis of folding thermodynamics using the MMTSB tool [40] for weighted histogram analysis method ( WHAM ) [41] . WHAM computes the density of states by combining energy histograms from simulation trajectories with overlapping energies and calculates the folding specific heat at constant volume at a function of temperature .
|
The environmental toxin β-methylamino-L-alanine ( BMAA ) has been linked to cases of amyotrophic lateral sclerosis ( ALS ) , but the role of this compound in disease is unknown . We propose that BMAA becomes incorporated into the ALS-linked protein Cu , Zn superoxide dismutase ( SOD1 ) , destabilizing it and promoting formation of the protein aggregates characteristic of ALS . Using computational techniques focused on the structure of BMAA-incorporated SOD1 , we demonstrate that the presence of BMAA changes SOD1 structure and dynamics to promote aggregation . We propose that BMAA incorporation in SOD1 in the mechanism of the compound’s link to ALS , and that BMAA modification may serve as a biomarker for environmentally-linked cases of ALS .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"chemical",
"compounds",
"pathology",
"and",
"laboratory",
"medicine",
"monomers",
"enzymes",
"neurodegenerative",
"diseases",
"amyotrophic",
"lateral",
"sclerosis",
"enzymology",
"dismutases",
"organic",
"compounds",
"toxicology",
"toxicity",
"serine",
"materials",
"science",
"basic",
"amino",
"acids",
"amino",
"acids",
"oligomers",
"thermodynamics",
"motor",
"neuron",
"diseases",
"polymer",
"chemistry",
"proteins",
"chemistry",
"physics",
"biochemistry",
"superoxide",
"dismutase",
"organic",
"chemistry",
"neurology",
"hydroxyl",
"amino",
"acids",
"biology",
"and",
"life",
"sciences",
"lysine",
"physical",
"sciences",
"dimers",
"materials"
] |
2019
|
β-Methylamino-L-alanine substitution of serine in SOD1 suggests a direct role in ALS etiology
|
Dengue virus ( DENV ) is the causative agent of dengue fever and dengue hemorrhagic shock syndrome . Dengue vaccine development is challenging because of the need to induce protection against four antigenically distinct DENV serotypes . Recent studies indicate that tetravalent DENV vaccines must induce balanced , serotype-specific neutralizing antibodies to achieve durable protective immunity against all 4 serotypes . With the leading live attenuated tetravalent DENV vaccines , it has been difficult to achieve balanced and type-specific responses to each serotype , most likely because of unbalanced replication of vaccine viral strains . Here we evaluate a tetravalent DENV protein subunit vaccine , based on recombinant envelope protein ( rE ) adsorbed to the surface of poly ( lactic-co-glycolic acid ) ( PLGA ) nanoparticles for immunogenicity in mice . In monovalent and tetravalent formulations , we show that particulate rE induced higher neutralizing antibody titers compared to the soluble rE antigen alone . Importantly , we show the trend that tetravalent rE adsorbed to nanoparticles stimulated a more balanced serotype specific antibody response to each DENV serotype compared to soluble antigens . Our results demonstrate that tetravalent DENV subunit vaccines displayed on nanoparticles have the potential to overcome unbalanced immunity observed for leading live-attenuated vaccine candidates .
The four dengue virus ( DENV ) serotypes are the causative agent of dengue fever and dengue hemorrhagic fever . DENVs are transmitted by Aedes sp . mosquitoes and both virus and vector are widely distributed throughout all tropical and subtropical regions , resulting in an estimated 300 million new infections per year , and approximately 1 million cases of severe disease with a case fatality 2–5% [1] . DENVs are endemic in over 125 countries and about 40% of the world’s population is at risk of getting infected by one of the 4 DENV serotypes . Primary infections induce robust and long term protective immunity against the serotype of infection , but individuals remain susceptible to one of the other serotypes . People experiencing secondary heterotypic infections are at greater risk of developing severe disease . Under some conditions , DENV serotype cross-reactive and poorly neutralizing antibodies induced after the primary infection , appear to enhance the second infection via the formation of virus-antibody complexes that promote infection of Fc-receptor bearing human myeloid cells [2 , 3] . It has been challenging to control the main mosquito vector of DENV . There are no effective antiviral or other therapies to treat DENV infections [4] . Based on success with other flaviviruses such as yellow fever and Japanese encephalitis viruses , vaccination is a promising strategy for dengue prevention and control . As effective immunity to just one serotype may place people at risk of severe disease upon infection with a different serotype , leading vaccine candidates are based on tetravalent live-attenuated virus formulations . In December 2015 , the first DENV tetravalent vaccine , Dengvaxia developed by Sanofi Pasture , was licensed by several countries . However , long-term data from Dengvaxia clinical trials indicate that the vaccine is only effective in people who have already been primed by natural DENV infections before vaccination . Naïve individuals who have received the vaccine appear to face a greater risk of developing severe disease upon exposure to wild type DENVs and the vaccine is now recommended for use only in people with pre-existing immunity to DENVs [5–10] . As an alternative to inactivated or live attenuated whole virus formulations , several groups have focused on using recombinant DENV envelope ( E ) protein ( rE ) as a vaccine antigen [11–15] . Even though single soluble subunits are generally not immunogenic in primates [16] , studies have shown that the immunogenicity of rE subunits can be augmented when combined with potent adjuvants or carriers [15 , 17 , 18] . We have previously demonstrated that adsorbing DENV2 rE to the surface of 80x320 nm particle replication in non-wetting templates ( PRINT ) produced poly ( lactic-co-glycolic acid ) ( PLGA ) -nanoparticles outperformed soluble rE subunits in terms of DENV2 specific IgG titers and neutralizing antibody titers [15] . The use of nanocarriers composed of biodegradable polymers not only creates an antigen depot and enhances antigen immunogenicity by effectively targeting antigen-presenting cells , but it provides a platform with the potential to mimic structural and antigenic features of the pathogen [19–22] . Here we demonstrate that a tetravalent DENV E protein formulation of PLGA nanoparticles induced higher levels of serotype specific IgG and neutralizing antibody titers than soluble tetravalent rE formulations in mice .
Vero cells ( American Type Culture Collection ( ATCC ) ) were maintained as monolayer cultures in DMEM medium ( Gibco ) supplemented with 1% non-essential amino acids , 5% fetal bovine serum ( FBS ) , 100 U/ml penicillin and 100 μg/ml streptomycin at 37°C with 5% CO2 . EXPI293 cells ( Gibco ) were maintained in suspension culture in EXPI293 Expression Medium ( Life Technologies ) and were passaged 1:10 when cell densities reached 3 . 5×106 cells/ml . To determine ( neutralizing ) antibody titers , DENV1 WestPac-74 , DENV2 S-16803 , DENV3 CH53489 and DENV4 TVP-376 were used . The recombinant E ( rE ) proteins of DENV1 ( aa 1–397 ) , DENV2 ( aa 1–395 ) , DENV3 ( aa 1–395 ) and DENV4 ( aa 1–397 ) were expressed by the EXPI293 transient expression system ( ThermoFisher ) following supplied protocols . The previously described expression construct was used [15] where an N-terminal IL2 secretion peptide leads the homotypic prM-rE cassette for each serotype . All rE proteins were equipped with a C-terminal 6×His-tag ( SSGGSHHHHHH ) . Protein expression was driven by a CMV early enhancer β-actin promoter . Recombinant proteins were purified as previously described [15] . In short , expression supernatants were concentrated and buffer exchanged by tangential flow filtration and proteins were purified by Ni2+-affinity chromatography . Elution fractions containing the rE-proteins were pooled and subjected to size-exclusion chromatography . Finally , the purified and concentrated proteins were flash frozen and stored at -80°C until further use . Purified rE proteins were subjected to SDS-PAGE and analyzed by Western Blot ( WB ) and Coomassie Brilliant Blue ( CBB ) staining . 500 ng of DENV1 rE and 1μg of DENV2 , 3 and 4 rE was added to a denaturing gel loading buffer containing SDS . Separated protein fraction were transferred to a nitrocellulose membrane and blocked overnight at 4°C with TBS+3% Skim Milk + 0 . 05% Tween-20 . Next , the membranes were treated with 0 . 5 μg/ml 1M7 human mAb in blocking buffer for 1 hr at 37°C . After washing , the membranes were subjected to 1:1000 diluted AP-conjugated anti-human IgG for 1 hr at 37°C and membranes were subsequently washed . Finally , membranes were developed using NBT/BCIP substrate ( ThermoScientific ) and the reaction was terminated in MilliQ water . Ni2+-coated ELISA plates ( Pierce ) were used to capture 2 ng/μl ( in TBS ) rE proteins ( DENV1-4 ) for 1 hr at 37°C . The plates were blocked using TBS + 3% skim milk + 0 . 05% Tween-20 for 1 hr at 37°C and subsequently washed 3 times with TBS + 0 . 05% Tween-20 . Next , plates were incubated for 1 hr at 37°C with 2 ng/μl ( in blocking buffer ) of mouse and human derived mAbs: 4G2 ( mouse , cross-reactive ) , 3H5 ( mouse , DENV2 specific ) , 12C1 . 5 ( mouse , cross-reactive ) , 8A1 ( mouse , DENV3 specific ) , 1M7 ( human , cross-reactive ) , 1F4 ( human , DENV1 specific ) , 2D22 ( human , DENV2 specific ) , 5J7 ( human , DENV3 specific ) , 5H2 ( chimpanzee , DENV4 specific ) and DV4 141 ( human , DENV4 specific ) ( Table 1 ) . Following incubation , the plates were washed and accordingly treated with AP-conjugated anti-mouse IgG ( Sigma , 1:1000 ) or AP-conjugated anti-human IgG ( Sigma , 1:2500 ) for 45 mins at 37°C . Finally , the plates were washed , developed with AP-substrate ( Sigma ) and absorbance was measures at 405 nm . PRINT technology was used to manufacture 80×320 nm poly ( lactic co-glycolic acid ) ( PLGA , 50:50 , 35 kDa , Lakeshore Biomaterials ) particles as previously described [34 , 35] . In short , PLGA and DC-cholesterol ( Avanti Polar Lipids ) were dissolved in chloroform ( 9:1 w/w ratio ) and casted into a thin film on a PET-sheet ( KRS Plastics ) . The film was oriented in order to contact the molds and carefully heated . Next , the film was split and the mold content was transferred to a second PET-sheet by passing through a laminator . Then water with 0 . 1% polyvinyl alcohol ( PVOH ) was added to the PET-sheet to release the nanoparticles ( NPs ) . The harvested particles were sterilized and concentrated by sterile filtration and tangential flow filtration . To adsorb the rE proteins to NP surfaces , rE was incubated with PLGA NPs in a 1% rE/NP ( w/w% ) ratio for 15 mins at room temperature in 0 . 1% PVOH in water with 9 . 25% sucrose to establish 100% adsorption efficiency for all serotypes . Female Balb/c mice were purchased from Jackson Laboratory and used at 6–12 weeks of age . For the monovalent formulations of every serotype , each mouse was immunized subcutaneously in the flank with 5 μg soluble rE ( n = 5 ) , 5 μg rE+500 μg Alum ( n = 5 ) , PBS ( Vehicle n = 3 ) or 5 μg adsorbed to 500 μg PLGA-NPs ( n = 5 ) . DENV2 immunization were previously described , but were included to generate complete overview [15] . The tetravalent formulation were divided into 2 groups . The tetra rE group ( TR ) combined 5 μg soluble rE of each serotype ( n = 5 ) . The NP-tetra rE group ( NTR ) is composed of 500 μg NPs combined with a mix of the four rE serotypes ( NP+rEDENV1-4 ( 5 μg each , n = 5 ) ) . The tetra NP-rE group ( TNR ) combines four individually adsorbed NP-rE formulations of each DENV serotype ( NP-rEDENV1 + NP-rEDENV2 + NP-rEDENV3 + NP-rEDENV4 ( 500 μg NPs + 5 μg rE ) ) . All groups in both the monovalent and tetravalent studies were immunized with the same antigen dose at day 0 , 21 and 63 and serum samples were collected at indicated time points . ELISA plates were coated overnight with 2 ng/μl 1M7 in 50mM carbonate/bicarbonate buffer at 4°C . The following day , the plates were blocked with TBS + 3% skim milk + 0 . 05% Tween-20 for 1 hr at 37°C . Next , the plates were washed in TBS + 0 . 05% Tween-20 and incubated with DENV2 or 2 ng/μl rE in blocking buffer for 1 hr at 37°C . After washing , the immunized mice sera of week 3 , 4 , 8 , 10 and 16 were diluted 1:50 in blocking buffer and week 16 sera was also serially diluted in blocking buffer and loaded onto the plate for 1 hr at 37°C . Next , the plates were washed and incubated with AP-conjugated anti-mouse IgG ( Sigma , 1:1000 in blocking buffer ) , IgG1 ( Abcam , 1:2500 ) or IgG2a ( Abcam , 1:2500 ) for 45 mins at 37°C . The plates were developed after washing with AP-substrate ( Sigma ) and absorbance was measured at 405 nm . The end point dilution ( EPD ) where the immunized mice sera reached background levels was determined using GraphPad Prism software . The previously described Vero-cell based flow cytometry neutralization assay was used to measure DENV serotype specific neutralizing antibodies [15 , 36] . In brief , Vero cells ( 25000/well ) were seeded and incubated overnight at 37°C . The next day , immunized mice sera was serially diluted in OptiMEM ( Gibco ) supplemented with 2% FBS and incubated with the appropriate amount of virus to infect ~15% of the cells ( amount previously determined ) for 45 mins at 37°C . Next , the cells were washed with OptiMEM and overlaid with the virus-serum combination for 2 hr at 37°C . Following incubation , the cells are washed with growth medium and incubated overnight in 200 μl growth medium at 37°C . Next , the cells are washed with PBS and detached from the plates by trypsin ( Gibco ) . Detached cells are transferred to a round-bottom plate and fixed with 4% paraformaldehyde for 10 mins at room temperature . The cells are washed in permeabilization buffer and blocked 1% normal mouse serum in perm buffer for 30 mins at room temperature . Next , the cells were incubated with Alexa-fluor 488 conjugated anti-prM mAb 2H2 for 1 hr at 37°C . After washing in perm buffer , the cells were resuspended in 200 μl FACS buffer . The percentage of infected cells was determined by flow cytometry using the Guava Flow Cytometer ( EMD Millipore ) and the neutralizing capacity was determined by GraphPad Prism and expressed in neut50 values ( the dilution where 50% of the virus was neutralized ) . Serum from 5 immunized mice was pooled for each tetravalent group and the serotype specific and cross-reactive IgG populations were depleted using immobilized homo- and heterotypic rE proteins . First , 1 mg of HisPur Ni-NTA magnetic beads ( ThermoFisher ) was washed and equilibrated using PBS + 10mM imidazole . Equilibrated beads were incubated with 40 μg of rE or MBP-His proteins ( control depletions ) for 30 mins at 37°C on a rotator . The beads were placed in a magnetic stand and were washed 3 times with washing buffer and finally divided over 2 tubes for two rounds of depletion . Next , 400 μl of 1:10 diluted ( PBS ) pooled sera was incubated with the chelated rE-beads for 1 hr at 37°C . The beads were placed in a magnetic stand and the sera was transferred for the second round of depletion for 1 hr at 37°C . The depleted serum was separated from the beads and stored at 4°C for subsequent IgG ELISAs and neutralization assays . The percentage of type specific neutralizing antibodies in Table 2 was determined by dividing the type specific neut50 ( CR depl . ) by the control neut50 ( Ctrl depl . ) . All experiments involving mice were performed according to the animal use protocol ( IACUC ID:17–047 ) approved by the University of North Carolina Animal Care and Use Committee . The animal care and use related to this work complied with federal regulations: the Public Health Service Policy on Humane Care and Use of Laboratory Animals , Animal Welfare Act , and followed the Guide for the Care and Use of Laboratory Animals .
The full prM sequence and the ectodomain of E of each DENV serotype was cloned downstream of an IL2 secretion signal sequence and expressed in EXPI293 cells ( Fig 1A ) . All rE proteins contained a C-terminal His-tag for purification and immobilization purposes . Analysis of the purified rE samples by gel electrophoresis and Western Blotting established the proteins were pure and of the predicted molecular mass of ~ 48kDa ( Fig 1B ) . In addition to monomers , rE dimers of ~ 100kDa were routinely detected . While the exact confirmation of this dimer is unknown , DENV rE proteins have been shown to form a concentration and temperature dependent dimer-monomer equilibrium [37 , 38] . The DENV rE proteins were captured on Ni2+-coated ELISA plates using the C-terminal His-tag and tested for binding to a panel of serotype specific or cross-reactive mAbs ( Fig 1C ) . The DENV cross-reactive mAbs 1M7 and 4G2 efficiently bound to all serotypes of rE . The DENV type-specific mAbs 3H5 ( DENV2 ) , 8A1 ( DENV3 ) , DV4 141 ( DENV4 ) that recognize E protein epitopes on the monomer bound to each rE protein . DENV type-specific human mAbs 1F4 ( DENV1 ) , 2D22 ( DENV2 ) and 5J7 ( DENV3 ) which bind to quaternary structure epitopes displayed on the E homodimer or higher order structures showed marginal binding to each cognate antigen indicating that the purified antigens were mainly present as monomers [38] . Mice were subcutaneously immunized with 5 μg of soluble rE alone , 5 μg of rE adsorbed to 80×320 PLGA NPs ( Fig 2A ) and 5 μg of rE with Alum ( 500 μg alum ) on day 0 and then boosted on week 3 and week 9 with the same vaccine formulation used to prime the animals . DENV specific IgG levels were evaluated in sera collected on week 3 , 4 , 10 and 16 , and neutralizing antibody titers were determined for week 16 serum samples ( Fig 2B ) . The DENV1 , 2 and 4 rE antigens adsorbed to nanoparticles stimulated higher mid-point dilution levels of specific antibody compared to soluble antigens ( Fig 3A ) . The antigens on nanoparticles and the soluble antigens with Alum adjuvant induced similar levels of DENV-specific antibodies ( Fig 3A ) . The DENV3 rE appeared to be inherently immunogenic , since DENV3 rE alone induced high levels of neutralizing antibodies that were not improved by particulation or the addition of the adjuvant . As described previously , at week 16 the DENV2 rE on nanoparticles induced higher levels of neutralizing antibodies than the soluble antigen alone ( Fig 3B ) [15] . Mice that were immunized with DENV1 or DENV4 rE on nanoparticles also had higher levels of neutralizing antibodies compared to soluble antigen alone or with the alum adjuvant ( Fig 3B ) . These results establish that the immunogenicity of monovalent DENV1 , 2 and 4 rE antigens is improved by adsorption to 80 x 320 nm PLGA nanoparticles when compared to the soluble antigen alone . Next , we assessed the immunogenicity of tetravalent formulations of rE from DENV1-4 in the presence or absence of nanoparticles . Mice were immunized with a mixture of the 4 soluble rE antigens ( 5 μg per antigen ) alone ( TR group ) , or with 5 μg of each antigen separately adsorbed to 500 μg of PLGA NPs and then combined into a tetravalent mix ( TNR group ) ( Fig 4 ) . Mice were immunized and boosted at week 3 and 9 . Serum samples were collected at week 3 , 4 , 8 , 10 and 16 to measure total DENV-specific IgG and functionally neutralizing antibody ( week 16 only ) ( Fig 2A ) . Both tetravalent formulations induced DENV-specific IgG against all four serotypes and antibody titers remained elevated through week 16 ( 7 weeks post 2nd boost ) ( Fig 5A ) . At week 16 , the overall level of DENV2 , 3 and 4 binding antibodies were higher in the TNR group compared to the soluble TR group ( Fig 5B ) . Soluble antigens predominantly induced an IgG1 response , where the particulated antigen stimulate a more balanced IgG1/IgG2a response ( Fig 5B ) . The soluble and particulate formulation induced high levels of neutralizing antibodies with no significant differences between groups ( Fig 5C ) . Overall DENV1 neut50 titers were lower compared to the other serotypes due to large variation within groups especially in the TR group . Recent data from a live-attenuated DENV vaccine clinical trials indicate that the quality rather than the total quantity of DENV neutralizing antibodies is a better predictor of vaccine efficacy [39] . In particular , the level of DENV type-specific neutralizing antibody may be a better predictor of protective immunity than the total level of vaccine induced neutralizing antibody ( type-specific + cross-reactive ) [39] . We performed studies to evaluate the type-specificity of binding and neutralizing antibodies induced by soluble and nanoparticle delivered tetravalent rE vaccines . Given the limited quantities of week 16 immune sera remaining after the primary analysis , we had to pool the sera from the 5 mice in each group for these studies . To determine the fraction of serotype specific IgG and neutralizing antibodies stimulated by each formulation , the pooled immune sera from each group was depleted to remove total or just cross-reactive antibodies to each serotype and tested for binding to rE from each serotype ( Fig 6 ) and for functional neutralization of each serotype ( Fig 7 , Table 2 ) . When DENV cross-reactive antibodies were depleted from immune sera collected from mice that received the soluble TR formulation , the animals had type-specific antibodies directed to epitopes on DENV2 , 3 and 4 but not DENV1 ( Fig 6A green bars ) . In the animals that received rE antigens adsorbed to nanoparticles , we observed type-specific antibodies to all 4 serotypes ( Fig 6B green bars ) . Depletions with the homologous rE of each serotype efficiently removed all neutralizing antibodies from mice immunized with each tetravalent formulation ( Fig 7 , red lines ) . In the TR group ( soluble tetravalent antigen ) , the animals had high levels of type-specific neutralizing antibodies to DENV2 , 3 and 4 but the DENV1 neutralizing antibody response was mainly derived from cross-reactive antibodies ( Fig 7A , green line; Table 1 ) . In contrast , in the TNR group the animals developed robust type-specific neutralizing antibody responses to all 4 serotypes ( Fig 7B , green line; Table 2 ) . The magnitude of the type-specific neutralizing antibody response was greater for both DENV4 and DENV1 in the TNR group compared to the TR group ( Table 2 ) . The DENV2 and 3 responses had similar levels of type-specific neutralizing antibodies in both the TR and TNR groups . Our results indicate that nanoparticle delivery of a tetravalent rE antigen mix stimulates a more balanced type-specific neutralizing antibodies compared to a soluble rE antigen mix .
A safe and efficacious vaccine that protects against all 4 DENV serotypes is urgently needed , but so far , results with leading live-attenuated tetravalent vaccine candidates are mixed . While the leading candidate developed by Sanofi Pasture is efficacious in people with pre-existing immunity to DENVs prior to vaccination , the vaccine is contraindicated in naïve individuals because of poor efficacy and safety [40] . The unbalanced replication of vaccine virus strains is the most likely explanation for the poor performance in naïve individuals [41] . Multi-component protein subunit vaccines are a promising alternative strategy to induce balanced immunity to all 4 serotypes but soluble antigens are poor immunogens compared to virions . Here we demonstrate that nanoparticles displaying rE from each serotype are a promising alternative to live virus vaccines . In previous studies we have shown that adsorbing DENV2 rE to PLGA nanoparticles of 80 × 320 nm increased specific IgG and neutralizing antibody titers compared to the soluble rE protein alone [15] . In this study , we first evaluated nanoparticle delivery of the other three DENV serotypes in monovalent formulations . For DENV1 , 2 and 4 we saw enhanced levels of serotype specific ( TS ) IgG and neutralizing antibodies when rE proteins were delivered using PLGA nanoparticles compared to soluble antigen . DENV3 rE appeared to be highly immunogenic as a soluble protein , so a particulation effect was not observed . Since all mice were inoculated with 5 μg of antigen , adsorbed to particles or as a soluble antigen , a lower DENV3 rE dose might reveal increased responses in the particle groups . This would require further dose optimization . DENV1-4 rE proteins were evaluated as tetravalent soluble or particulate vaccines in mice . While both the soluble and particulate vaccines induced similar levels of neutralizing antibody , possibly due to an increase of cross-reactive antibody levels , we observed differences in IgG isotypes being induced . Where soluble antigens promote a predominant Th2 response by the induction of IgG1 , particulate antigens stimulated a more balanced IgG1/IgG2a and thus Th1/Th2 response . In addition , we observed qualitative differences in the properties of neutralizing antibodies induced by each vaccine formulation . Though limited by the quantity of sera , serum depletion studies revealed a trend that the proportion of DENV serotype specific neutralizing antibodies increased when rE was adsorbed to PLGA surfaces compared to the soluble vaccine . This effect was especially clear for DENV1 , and DENV4 , where the serotype specific neutralizing antibody response against DENV1 increased from 6 . 1% to 22 . 8% and for DENV4 from 43 . 1% to almost 98% . Due large quantity of immune sera required for antibody depletion studies , we had to pool immune sera from all the animals within each group for these experiments , which precluded statistical analysis of the differences between groups . While neutralizing antibodies have been long considered to be a surrogate of protective immunity in flavivirus vaccine development , the Sanofi clinical trial with Dengvaxia has clearly established that people with high levels of neutralizing antibodies can experience DENV infections [39 , 42] . The Sanofi experience and other studies point to both the level and quality of neutralizing antibodies as critical determinants of protective immunity [43 , 44] . In particular , in naïve individuals who are vaccinated , serotype-specific neutralizing antibodies appears to be critical for protection [39] [44] . Even though DENV rE subunit antigens have not been particularly promising as vaccine antigens [16] , here we show that both the level and quality of neutralizing antibodies can be enhanced by using nanoparticles to deliver DENV monovalent and tetravalent rE proteins . The efficacy of subunit based dengue vaccines is expected be increased by the expression of quaternary epitopes recognized by strongly neutralizing antibodies . Although rE subunits are lacking these E-dimer dependent epitopes , we have previously developed methods to assemble E-dimers out of soluble monomers [37] . Future studies will focus on displaying quaternary epitopes carrying E-protein subunits on nanoparticles surfaces . In addition , our nanoparticles vaccine platform requires further evaluation of adjuvants to enhance the immunogenicity of particulated rE antigens . More broadly , a further developed platform described here for DENV vaccines can be modified to develop vaccines for other flaviviruses such as West Nile , yellow fever virus or Zika viruses .
|
Dengue virus ( DENV ) is the causative agent of dengue fever and dengue hemorrhagic fever . Yearly , over 350 million individuals in over 120 countries are infected . To establish protection through vaccination , one must induce simultaneous immunity against four antigenically distinct DENV serotypes . However , this is challenging because it has been shown that vaccination can enhance disease due to specific immunity to the virus . As an alternative to existing vaccine platforms , we are exploring the potential of a protein subunit vaccine using only the DENV envelope protein ( E ) as the vaccine antigen . To increase the immunogenic potency of E , we attach it to nanoparticle carriers . For each individual DENV serotype , we show that we can enhance immune responses in monovalent as well as tetravalent formulations when E is attached to nanoparticles . Additionally , in tetravalent nanoparticle formulations , vaccine quality is increased by the generation of a more balanced serotype specific immune antibody response to each DENV serotype . The nanoparticle vaccine platform described here for DENV vaccines serves as a promising and safe alternative to more conventional vaccine platforms and can be modified to develop vaccines for other viral pathogens such as West Nile , yellow fever virus or Zika virus .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
"dengue",
"virus",
"viral",
"vaccines",
"medicine",
"and",
"health",
"sciences",
"immune",
"physiology",
"pathology",
"and",
"laboratory",
"medicine",
"engineering",
"and",
"technology",
"pathogens",
"immunology",
"microbiology",
"vaccines",
"viruses",
"preventive",
"medicine",
"rna",
"viruses",
"nanoparticles",
"nanotechnology",
"infectious",
"disease",
"control",
"antibodies",
"vaccination",
"and",
"immunization",
"public",
"and",
"occupational",
"health",
"immune",
"system",
"proteins",
"infectious",
"diseases",
"proteins",
"medical",
"microbiology",
"antigens",
"microbial",
"pathogens",
"biochemistry",
"flaviviruses",
"immunity",
"virology",
"physiology",
"viral",
"pathogens",
"biology",
"and",
"life",
"sciences",
"vaccine",
"development",
"organisms"
] |
2018
|
Nanoparticle delivery of a tetravalent E protein subunit vaccine induces balanced, type-specific neutralizing antibodies to each dengue virus serotype
|
Crimean Congo hemorrhagic fever virus ( CCHFV ) is a negative-strand RNA virus of the family Bunyaviridae ( genus: Nairovirus ) . In humans , CCHFV causes fever , hemorrhage , severe thrombocytopenia , and high fatality . A major impediment in precisely determining the basis of CCHFV’s high pathogenicity has been the lack of methodology to produce recombinant CCHFV . We developed a reverse genetics system based on transfecting plasmids into BSR-T7/5 and Huh7 cells . In our system , bacteriophage T7 RNA polymerase produced complementary RNA copies of the viral S , M , and L segments that were encapsidated with the support , in trans , of CCHFV nucleoprotein and L polymerase . The system was optimized to systematically recover high yields of infectious CCHFV . Additionally , we tested the ability of the system to produce specifically designed CCHFV mutants . The M segment encodes a polyprotein that is processed by host proprotein convertases ( PCs ) , including the site-1 protease ( S1P ) and furin-like PCs . S1P and furin cleavages are necessary for producing the non-structural glycoprotein GP38 , while S1P cleavage yields structural Gn . We studied the role of furin cleavage by rescuing a recombinant CCHFV encoding a virus glycoprotein precursor lacking a functional furin cleavage motif ( RSKR mutated to ASKA ) . The ASKA mutation blocked glycoprotein precursor’s maturation to GP38 , and Gn precursor’s maturation to Gn was slightly diminished . Furin cleavage was not essential for replication , as blocking furin cleavage resulted only in transient reduction of CCHFV titers , suggesting that either GP38 and/or decreased Gn maturation accounted for the reduced virion production . Our data demonstrate that nairoviruses can be produced by reverse genetics , and the utility of our system uncovered a function for furin cleavage . This viral rescue system could be further used to study the CCHFV replication cycle and facilitate the development of efficacious vaccines to counter this biological and public health threat .
Crimean Congo hemorrhagic fever virus ( CCHFV ) is a severe human pathogen transmitted through tick bites or contact with infected animals or patients . In ~5–40% of the cases , patients suffer from profound hemorrhage leading to shock and death . CCHFV was first isolated in the Democratic Republic of the Congo in 1956 , and later classified into the family Bunyaviridae , genus Nairovirus [1] . The family also includes the genera Orthobunyavirus , Hantavirus , Phlebovirus , and Tospovirus . Today , CCHFV is recognized as endemic in several countries of Africa , Europe , Asia , and the Middle East , where the main vector , hard ticks of the genus Hyalomma , are found [1] . Recent emergence and re-emergence of Crimean Congo hemorrhagic fever ( CCHF ) in Turkey , southern Russia , Balkans , Democratic Republic of the Congo , Sudan , Uganda , Pakistan , and India have spurred concerns over the spread of this disease to other countries where ticks vectors are present [2 , 3] . The initial phase of CCHF consists of an acute febrile prodrome indistinguishable from that caused by several other viral infections . However , in less than a week , CCHF often progresses into a life-threating illness characterized by leucopenia , high levels of pro-inflammatory cytokines , coagulopathy , liver necrosis , and hemorrhage [4] . These pathological findings likely reflect a pro-inflammatory immune response mounted against viral infection , and the resulting tissue damage . The high virulence , absence of effective treatments , and documented person-to-person transmission of CCHFV have justified its inclusion on the list of agents requiring biosafety level 4 ( BSL-4 ) , the highest level of containment , to perform experimental work [5] . The genome of nairoviruses is distributed on 3 negative-sense RNA segments designated small ( S ) , medium ( M ) , and large ( L ) . The S segment encodes the nucleoprotein ( N ) that encapsidates the viral genome to form ribonucleoprotein particles ( RNPs ) . The M segment encodes a ~1700 amino acid precursor that is processed into structural glycoproteins ( Gn and Gc ) that decorate the surface of virions , non-structural M protein ( NSm ) [6] , and secreted non-structural glycoproteins ( NSGs ) GP85 , GP160 , and GP38 [7 , 8] . L segment mRNA is translated into an unusually large ( ~450 kDa ) RNA-dependent RNA polymerase ( L-RdRp ) that replicates and transcribes viral RNA [9] . Nairovirus glycoprotein biosynthesis is more complex than that of other bunyaviruses in terms of size , functional domains , and the number of mature proteins produced [7 , 8] . Synthesis of the CCHFV M segment polyprotein is targeted to the endoplasmic reticulum , where the signal peptidase complex rapidly cleaves it into NSm , the membrane bound Gn precursor ( PreGn ) , and the Gc precursor ( PreGc ) [7] . PreGn and PreGc are further modified by addition of N-linked sugars , and cleaved into Gn and Gc by highly specific host endoproteases in an early compartment of the secretory pathway . PreGn is processed by the site-1 protease ( S1P , also known as subtilisin kexin isozyme-1 [SKI-1] ) , a member of the proprotein convertase ( PC ) family . PreGc is matured by an unidentified host convertase [10] . S1P activity leads to the selective incorporation of Gn and Gc into nascent virions [11] , and to the secretion of NSGs containing 2 domains specific to nairoviruses: a heavily O-glycosylated and variable mucin-like domain ( MLD ) , and a GP38 domain [8] . Three NSGs are observed in the extracellular milieu of CCHFV-infected cells: GP160 and GP85 , which contain both MLD and GP38 domains; and GP38 , a cleavage product of GP160/GP85 lacking the MLD domain [8] . It is currently unclear what accounts for the differential migration of GP160 and GP85 on SDS-PAGE , as neither tertiary structures nor glycosylation appear to contribute . GP85 probably contains a shorter polypeptide chain while still sharing common epitopes with GP160 in both MLD and GP38 domains [8 , 12] . The last processing step leading to GP38 secretion occurs at a canonical furin-like PC motif ( RSKR↓ ) in a late compartment of the secretory pathway [8] , most likely in the trans-Golgi network ( TGN ) where furin concentrates [13] . Cleavage of NSGs by furin and/or PCs with related specificity leads to the secretion of GP38 . Recovery of recombinant viruses from cloned complementary DNA allows the design of viruses with precisely defined genomic sequences . This permits researchers to elucidate viral protein function in the context of infection . Viruses with tailored genomes are useful for designing viral variants and live attenuated vaccines harboring engineered mutations and/or gene deletions . This powerful technology has not been available for nairoviruses , a deficiency that has seriously limited our understanding of CCHFV biology and pathogenesis . Here , we describe a method utilizing codon optimization of CCHFV’s L-RdRp to support the recovery of infectious CCHFV entirely from cDNA transfection . Using this system , we investigate the possible role of NSG cleavage in CCHFV life cycle .
We previously reported the reconstitution of transcriptionally active CCHFV RNPs by co-transfecting 2 plasmids producing the L-RdRp and N proteins together with a surrogate RNA minigenome synthesized by bacteriophage T7 RNA polymerase ( T7 Pol ) [9] . These minigenomes are composed of the viral promoter sequences ( 3′ and 5′ non-coding regions ) of the S , M , or L , and a luciferase gene in place of the viral open reading frame ( ORF ) . The co-transfection of a minigenome together with the L-RdRp and N proteins yielded luciferase activity [9] , demonstrating the feasibility of reconstituting CCHFV RNPs . In order to recover infectious CCHFV from cloned cDNA , the complete S , M , and L segments were cloned between a T7 promoter to drive the transcription of CCHFV complementary genomic RNA copies , and a hepatitis D ribozyme to obtain authentic 3′ termini ( S1A Fig ) . Our first attempts consisted of transfecting 3 plasmids producing complementary ( positive sense ) copies of the CCHFV S , M , and L segments into cells stably expressing T7 ( BSR-T7/5; S1 Fig ) . We chose to use positive-sense RNA segments over negative-sense , as uncapped T7 transcripts could be translated into enough viral proteins to launch CCHFV replication , as previously demonstrated for other bunyaviruses and arenaviruses [14–16] . In addition , most rescue systems for negative-strand RNA viruses rely on producing positive-sense transcripts to avoid the formation of double-stranded RNA between the negative-sense T7 transcripts and positive-sense support plasmids that are often required [17] . Transfection of only pT7-S , pT7-M , and pT7-L , failed to rescue CCHFV , possibly because the CCHFV L segment is extremely long compared to the L of arenaviruses and bunyaviruses and could make reconstituting RNPs less efficient . To improve reconstitution of RNPs containing full-length L , we supplemented the transfection reactions with expression vectors pC-L and pC-N ( S1B Fig , pC-L + pC-N ) , producing the L-RdRp and N from a strong RNA polymerase II promoter that has been previously used to successfully replicate minigenomes of all CCHFV segments [9] . Despite multiple attempts and protocol modifications , no CCHFV was recovered using this set of plasmids . Western blot analysis of the L-protein from pC-L transfections revealed that only a small amount of the full-length L-RdRp had been produced [9] . We hypothesized that low abundance of full-length L-RdRp might be preventing the replication of full-length CCHFV RNA transcripts . Since bunyavirus transcription naturally occurs in the cytoplasm , we suspected that the L transcripts produced in the nucleus might yield truncated L-RdRp products due to aberrant splicing and/or premature termination of transcription by PolII . In an attempt to increase the amount of ~450 kDa L-RdRp product and improve viral RNA synthesis , we produced a synthetic V5-tagged L-RdRp codon optimized for expression in human cells ( pC-L opti ) . The algorithms used to generate this L sequence maximize the usage of preferred human codons and remove potential cryptic splicing sites and secondary structures [18] that might interfere with transcription and translation of full-length L-RdRp . We first tested the effects of L gene codon optimization on L-RdRp activity using a luciferase minigenome system . Codon optimization led to ~7-fold increase in L-RdRp activity and more robust production of the full-length L-protein , confirming that codon optimization greatly improves L-RdRp activity and expression ( Fig 1A and 1B ) . Therefore , we used pC-L opti instead of wild-type ( WT ) pC-L in subsequent attempts to rescue CCHFV . Since CCHFV does not cause obvious cytopathic effect in permissive BSR-T7/5 cells , a PreGn mAb ( 7F5 ) was used to rapidly detect possible rescue of CCHFV directly in the transfected cells . This was possible because foci of > 10 cells with discernable Golgi-like perinuclear staining could only be observed when CCHFV was present as opposed to transfection of pT7-S , -M , and-L alone . Four days after transfection of BSR-T7/5 cells with pC-L opti , immunoreactive foci of infection were detected , suggesting the recovery of CCHFV from cDNA ( Fig 1C ) . Supernatants from transfected BSR-T7/5 cells were then transferred to highly susceptible SW13 cells , which show apoptosis-induced cytopathic effect upon CCHFV infection [20] . Three days later , cytopathic effect were evident in these cells , and CCHFV antigens were detected throughout the cell monolayer . Growth kinetics of recombinant CCHFV in RIG-I signaling deficient ( BSR-T7/5 ) [21] and competent ( A549 ) cells matched those of the parental virus isolate ( Fig 2 ) . These data confirmed that infectious nairoviruses can be obtained entirely from cloned DNA in our system . After confirming CCHFV rescue , we optimized the system by varying the ratios of plasmids used in order to maximize rescue efficiency . We first tested the effect of adding exogenous T7 Pol . Without expressing plasmid-derived T7 , we rescued CCHFV in 2 out 3 experiments ( Fig 3A ) . In comparison , adding pC-T7 plasmid to the plasmid mix yielded rescue in 6/6 experiments and similar CCHFV titers over the 5-day experimental period ( Fig 3B , ratio 2:1 ) . We concluded that additional T7 is not required but overall not detrimental to rescue efficiency . Stable expression of T7 in BSR-T7/5 can vary with passaging and requires a strict antibiotic selection regimen [14] , so being able to use exogenous T7 Pol can be useful . Since adding pC-T7 was not detrimental to rescue efficiency , we continued to add pC-T7 in subsequent rescue experiments to mitigate any potential variation in the stable expression of T7 Pol from BSR-T7/5 cells . The ability to add T7 also allowed us to determine if CCHFV could be efficiently rescued in cell lines that do not stably express T7 . The human hepatocyte cell line Huh7 was transfected with pC-T7 , pC-N , pC-L opti and pT7-S , -M , and-L; virus was detected , and viral titers were measured by tissue culture infective dose ( TCID50 ) determination 2–5 days post transfection . CCHFV was rescued from Huh7 cells in 3/3 experiments , and infectious virus titers peaked at 4 days post transfection ( S2 Fig ) with no significant differences from levels seen in BSR-T7/5 cells when using a 2:1 pC-N:pC-L opti plasmids ratio ( Fig 3B , ratio 2:1 ) . We next determined if increasing support plasmid ratios affected rescue efficiency . Recombinant CCHFV was detected as early as 2 days post transfection when pC-N and pC-L opti were used in a 2:1 ratio , and ~10-fold higher viral titers were obtained 3 days post transfection than when using a 19:1 ratio of these plasmids ( Fig 3B ) . However , rescue efficiency remained 100% with both ratios tested , and viral titers peaked 4–5 days post transfection independently of the ratios of support plasmids used . Finally , we varied the ratios of plasmids used to express the full-length complement RNA of the 3 CCHFV segments ( pT7-S , pT7-M , and pT7-L ) while keeping the 2:1 ratio of support plasmids ( Fig 4 ) . At 4 days post infection , the same viral titers of ~1 × 105 TCID50/mL were obtained at all ratios tested , except for the 2 . 5:1:1 ( S:M:L ) ratio , which did not yield measurable CCHFV titers . Within the family Bunyaviridae , only nairoviruses produce NSGs or viral glycoprotein precursors subjected to PC cleavage [8 , 10 , 22] . In most cases , the cleavage of structural viral glycoproteins promotes viral replication by allowing the entry of the virus into host cells . Interestingly , Ebola virus , a filovirus that causes highly fatal hemorrhagic fever , synthesizes NSGs and a structural glycoprotein precursor containing MLD and furin-like PC cleavage motifs ( RTRR↓ ) [23 , 24] . Unlike most viral glycoproteins cleaved by furin-like PCs , Ebola virus glycoprotein cleavage appeared to have little or no effect on viral replication , as infectious virus was still produced in mutants with blocked processing of the glycoprotein precursor [25 , 26] . We therefore endeavored to determine whether cleavage of CCHFV glycoprotein at the RSKR247 site was critical for CCHFV propagation ( Fig 5 ) . The importance of furin-like PCs in the propagation of CCHFV and control Rift Valley fever virus ( RVFV ) was evaluated by infecting cells deficient in furin . The parental Chinese hamster ovary ( CHO ) -derived cell lines ( Par6 ) , furin-deficient ( FD11 ) [27] , and furin-reconstituted FD11 ( FD11-Fur ) were infected with CCHFV or RVFV at multiplicity of infection ( MOI ) of 1 or 0 . 1 , respectively , and percentages of infected cells were determined 24 and 48 h post infection ( Fig 6A ) . After 24 h , 37% of Par6 cells and 36% of FD11-Fur cells were infected with CCHFV , compared to 4% of FD11 cells not expressing furin . After 48 h , Par6 and FD11-furin cell monolayers were totally infected , while only 18% of the FD11 cells where infected ( Fig 6B ) . In comparison , the replication of RVFV , which does not contain known furin-like PC cleavage motifs in its glycoprotein sequence , was similar in all cell lines , supporting the hypothesis that glycoprotein cleavage at a putative RSKR motif enhances CCHFV replication . Alternatively , furin might indirectly regulate CCHFV spread by processing host proteins implicated in its life cycle . To clarify whether furin or general NSG cleavage is required for CCHFV replication , we first sought to recover a CCHFV with a glycoprotein resistant to furin-like PC endoproteolysis . The substrates of furin-like PCs are normally cleaved at the general ( R/K ) -2nX-R↓ motif , where n = 0–3 amino acids [28] . The canonical motif ( RSKR↓ ) is located at the junction of the MLD and GP38 domains of CCHFV ( Fig 5A ) . To block processing at this site , we mutated the ORF encoded by the pT7-M plasmid , so that Arg residues at positions 1 and 4 were changed to Ala ( RSKR to ASKA ) . Given the specificity of furin-like PCs , these mutations should block the conversion and production of GP38 . The pT7-M-ASKA plasmid was transfected together with plasmids needed to produce recombinant CCHFV , and a viable recombinant virus ( CCHFV-ASKA ) was recovered . Like WT CCHFV , the rescued CCHFV-ASKA mutant produced extensive cytopathic effect in SW13 cells . In cells infected with CCHFV-ASKA , PreGn colocalized with a Golgi marker , similarly to WT CCHFV ( S3 Fig ) . Sequencing the M segment from this mutant virus confirmed mutations only at the cleavage site mutations . To confirm that the ASKA mutation blocked NSG processing , viral proteins extracted from lysates of infected cells were analyzed using antibodies directed against PreGn/Gn , PreGc/Gc , and N . Densitometry analysis of the precursor and mature Gn and Gc indicated no change in Gc:PreGc ratios , while the Gn:PreGn ratio was slightly reduced in ASKA mutants ( Fig 7A ) . The anti-GP38 antibody also detected PreGn but GP160/85 and GP38 did not accumulate in cells to detectable levels ( S4 Fig ) . To detect the secreted NSGs , supernatants of infected cells were immunoprecipitated using a mAb directed against the GP38 domain . Polyacrylamide gel electrophoresis revealed GP160/GP85 and GP38 presence upon infection with WT CCHFV ( Fig 7B ) . In comparison , CCHFV-ASKA infection produced abundant GP160/GP85 , but only trace amounts of GP38 . Thus , mutating RSKR to ASKA selectively impaired maturation of the glycoprotein precursor to GP38 , and slightly reduced PreGn conversion to Gn . Since CCHFV-ASKA mutations effectively blocked the production of GP38 , we addressed the role of GP38 in the production of infectious CCHFV . FD11 and FD11-Fur cells were infected with CCHFV-WT or CCHFV-ASKA , and viral loads and associated viral RNA levels were measured for 5 days after infection by , respectively , TCID50 determination and qRT-PCR ( Fig 8 ) . Furin-reconstituted cells ( FD11-Fur ) infected with CCHFV-WT yielded ~10-fold more infectious virus and viral RNA than the same cells infected with CCHFV-ASKA , but only 1 day post infection . In comparison , no differences were noted in furin-deficient cells ( FD11; Fig 8 ) .
Prior to codon optimization of the L-gene in the support plasmid , all attempts to generate CCHFV failed . Notably , the same plasmid vector stocks expressing complementary copies of the 3 viral segments ( pT7-S , pT7-M , and pT7-L ) were used in all of our unsuccessful and successful attempts , suggesting that inefficient synthesis of full-length L-RdRp might explain our initial inability to rescue recombinant CCHFV ( Fig 1 ) . The increase in L-RdRp expression upon introducing the optimized L construct was seen in both L-RdRp yield and activity ( Fig 1A ) . The recoded , optimized L ( pC-L opti ) efficiently launched viral replication and allowed us to obtain infectious CCHFV with a tailored genome . CCHFV infection persists in ticks and its replication cycle is exclusively cytoplasmic . It is plausible that evolutionary pressure exerted by the tick host cells and adaption for cytoplasmic transcription could explain the difficulty in producing large amounts of L-RdRp from the nucleus of mammalian cells using CCHFV’s natural codons ( Fig 1B ) . Our failure to recover CCHFV when using WT pC-L in previous attempts suggested that in silico recoding may be important for systematically recovering recombinant CCHFV . Synthetic genes can also be introduced into the genome of viruses using reverse genetics . Introduction of rare codon pairs in poliovirus and influenza virus genes leads to reduced viral protein production , strong attenuation in vivo , and prototype vaccines that can confer complete protection against homologous virus challenges [29 , 30] . As efficacious CCHF vaccines are critically needed , a similar codon deoptimization approach could now be attempted in order to obtain a live , attenuated CCHFV vaccine candidate . Robust recue systems are critical for obtaining mutant viruses , especially those with severe growth defects . Herein , we describe optimization of the rescue protocol that yielded recombinant CCHFV in virtually every transfection attempted . Although not required for rescue in cells stably expressing T7 Pol , exogenously added pC-T7 did not negatively impact rescue efficiency in BSR-T7/5 cells . Adding pC-T7 further allowed us to rescue CCHFV in Huh7 cells , a human hepatocyte cell line not stably expressing T7 Pol . This demonstrates that our rescue system is robust and could be easily transposed to a cell line of human origin without cell specific optimization . The option of using other cell lines might be particularly important in the eventuality of mutations preventing CCHFV recovery in BSR-T7/5 due to low or absent expression of cell- or species-specific host factors . Optimal rescue conditions should yield the highest viral titers at early times post-transfection . By varying support plasmid ratios ( pC-N:pC-L opti ) , we observed that lower ratios increased CCHFV yield by ~10-fold 3 days post transfection , showing that reducing the expression of codon-optimized L-RdRp is detrimental to virus rescue . Nevertheless , even lower amounts of codon-optimized L-RdRp eventually yielded high titers of CCHFV at later time points . In contrast , increasing the ratio of pT7-S to pT7-M and-L dramatically reduced virus rescue efficiency , as noted by the absence of detectable CCHFV at 4 days post transfection when using an S:M:L ratio of 2 . 5:1:1 ( Fig 4 ) . This finding suggests that overrepresentation of the plasmid producing the S segment complementary genome should be avoided . The smaller size of pT7-S could explain these phenomena , as T7 Pol . may preferentially synthesize smaller segments more efficiently , thereby requiring less pT7-S input than pT7-M and pT7-L . Limited endoproteolysis is a common mechanism exploited by viruses to modulate the activity of their glycoprotein precursors , and CCHFV uses PCs with non-overlapping specificity to generate GP38 and Gn . Previous biochemical analyses of the M-polyprotein have revealed the role of host proteases in the complex biosynthesis of at least 8 different proteins from a single protein [6 , 8] . Our past efforts highlighted the critical function of S1P in assembling infectious CCHFV [11] , but the contribution of furin-like PCs in viral replication remained unexplored . Using the CCHFV reverse genetics system , we addressed the function of this cleavage on CCHFV replication without the need to rely on furin-like PC inhibitors . This is particularly important , as inhibition of furin-like PCs could also modulate the activity of other cellular receptors , ligands , growth factors , and enzymes that could indirectly affect CCHFV replication independently of glycoprotein processing . The conservation of basic amino acids in the RSKR motif among all CCHFV strains from diverse endemic areas ( S5 Fig ) suggests selective pressure for furin-like PC cleavage at this particular site . Structural analysis of RVFV Gc revealed that the overall architecture of bunyavirus Gc resembles that of class II fusion proteins of West Nile virus and Sindbis virus , whose companion proteins are cleaved by furin-like PCs . However , neither RVFV Gc nor the companion protein Gn is obtained by PC activity . Therefore , it became critical to determine whether cleavage of CCHFV glycoprotein , both by furin-like PCs at the putative RSKR motif and by SKI-I/S1P at the RRLL motif , is necessary for efficient spread and production of CCHFV , or whether furin-like PC cleavage simply fulfills accessory functions dispensable for viral replication . To address the function of CCHFV glycoprotein cleavage at the RSKR motif in virus propagation , we obtained a recombinant CCHFV variant , CCHFV-ASKA , which produced glycoprotein precursors resistant to furin-like PC activity . The simple recovery of ASKA mutant virus demonstrated that furin-like PC cleavage is not an essential step in CCHFV replication in cell culture , and enabled us to study the function of cleavage at this site without being obligated to manipulate cellular functions with inhibitors , siRNA , or gene overexpression . Consistent with the preference of furin-like PCs for Arg residues at P1 and P4 , the CCHFV-ASKA glycoprotein profile confirmed that mutating P1 and P4 selectively blocked glycoprotein processing to GP38 . To a less significant extend , mutations reduced the Gn:PreGn ratio , but the Gc:PreGc ratio was unaffected . The fact that mature Gn was still produced is compatible with the rapid production of PreGn by S1P early in the secretory pathway ( endoplasmic reticulum/early Golgi ) ; furin , on the other hand , is localized in the trans-Golgi network , and the cleavage yielding GP38 . Consequently , we propose that S1P PreGn cleavage into Gn and MLD-containing NSGs ( GP160/85 ) precedes furin-like PC cleavage , and that the distinct localization of S1P and furin might dictate the proper sequence of cleavage events that yields the complete set of structural and non-structural glycoproteins . However , our data also suggest that furin processing could indirectly increase PreGn to Gn conversion performed by S1P . It remains to be shown if furin does indeed cleave a pool of PreGn prior to S1P and if prior cleavage at RSKR site would enhance Gn production . The relative ease of propagating the CCHFV-ASKA mutant appears in stark contrast with the total loss of CCHFV infectivity when S1P is not expressed [11] . Further experiments were performed to address the function of furin-like PCs in the replication of CCHFV , using RVFV as a PC-independent control . Although our data do not exclude the potential role of other furin substrates in CCHFV’s life cycle , our findings certainly stress a specific role of furin cleavage at the RSKR site . This conclusion is supported by higher WT CCHFV production than CCHFV-ASKA production in cells expressing furin ( Fig 8 ) . If furin cleavage at RSKR did not contribute to CCHFV’s replication , then this difference would also be expected in furin-deficient cells . Therefore , we conclude that cleavage at the putative furin-like PC motif is not essential for CCHFV production , but furin cleavage at RSKR enhances virion production . Despite evolutionary conservation of the RSKR motif , our in vitro replication experiments confirm that this cleavage event is dispensable for the production or cell-to-cell spread of CCHFV . The biological relevance of CCHFV NSGs remains unknown , and no specific ligands have been determined for any of them . Intriguingly , the decrease in CCHFV-ASKA production was transient , and CCHFV-ASKA levels matched those of WT-CCHFV 2 days and later post infection , perhaps because furin cleavage affects virion production early during the infection when the levels of glycoproteins are lower . Studies focused on NSG function in conjunction with detailed biosynthesis of the various M-polyprotein glycoprotein products are warranted to precise the function of furin cleavage of CCHFV glycoproteins . Even though modest , reduction in PreGn to Gn certainly merits further consideration given the essential role of S1P in Gn biosynthesis [10] and CCHFV infectivity [11] . But how could furin cleavage at RSKR site increase S1P cleavage at RRLL ? One possibility could be trimming of PreGn from its MLD by furin . In this scenario , PreGn would escape S1P cleavage and travel to the trans-Golgi network , where furin would trim off its MLD ( Fig 5 ) ; this , in turn , would facilitate the kinetics of Gn production and subsequent virion production . CCHFV assembly occurs at the Golgi or trans-Golgi network , and after assembly , the intracellular vesicles containing viruses are transported toward the cell surface , where they are released by exocytosis . CCHFV GP38 is secreted by exocytosis , but is not tightly associated with extracellular CCHFV particles [8] . Since we observe a reduction in CCHFV-ASKA production in the extracellular milieu and GP38 , we speculate that GP38 could be involved in the efficient sorting and/or release of mature CCHFV particles . We propose that future studies should consider the possible impact of furin cleavage of the MLD on Gn formation and investigate the role of GP38 on CCHFV production . Recently , an experimental in vivo model of CCHFV transmission from ticks to vertebrates [31] , and a model of CCHF pathogenesis [32] in mice , were reported . These in vivo systems , combined with the power of reverse genetics , offer unique opportunities to characterize CCHFV protein functions in various aspects of virus pathogenesis and biology . Our study lays important groundwork to further define how NSGs and their cleavage by furin-like PCs affect CCHFV biology and pathogenesis in the mouse model , and the persistence and transmission of CCHFV in the tick vector . The function of NSGs is particularly intriguing because filoviruses from the genus Ebolavirus also secrete glycoproteins that contain an MLD and are cleaved by furin-like PCs . Therefore , it is critical to better characterize the function of these secreted glycoproteins that might share a common mechanism to cause severe hemorrhage and death in humans . In conclusion , this work highlights the ability of experimentally producing CCHFV variants entirely from cDNA . Using CCHFV reverse genetics , we efficiently recovered WT virus and a virus variant encoding a glycoprotein precursor resistant to furin-like PC cleavage . This mutant CCHFV was used to specifically examine the role of furin-like PC cleavage on virus propagation . In the future , this reverse genetics systems could be used to experimentally identify genetic determinants of virulence , critical functions of viral proteins , non-coding regions of the viral genome , and in the rational design of live attenuated vaccines .
SW13 cells , a generous gift from P . Leyssen ( Rega Instituut KU Leuven , Belgium ) , were cultured in DMEM supplemented with 10% FBS and 1 mM sodium pyruvate . BSR-T7/5 were a generous gift from K . K . Conzelmann ( Ludwig-Maximilians-Universität , Munich , Germany ) , and maintained as described before [14] . Vero-E6 and A549 ( both from American Type Cell Collection ) cells were maintained in DMEM + 10% FBS . All cell culture media were supplemented with 100 units/mL of penicillin and streptomycin . Parental CHO-derived cells ( Par6 ) , furin-deficient Par6 cells ( FD11 ) , and FD11 stably expressing furin ( FD11-Fur ) were kindly provided by Stephen Leppla ( National Institute of Health , Bethesda , MD , USA ) ; these cell lines were maintained in MEM-alpha medium with 10% FBS . CCHFV strain IbAr10200 ( former Yale Arbovirus Research Unit , CT , USA ) was isolated in Nigeria in 1965 from ticks , and has a passaging history in suckling mice and Vero-E6 cells . CCHFV RNA was extracted with Tripure reagent ( Roche Diagnostics Corp , Indianapolis , IN , USA ) using manufacturer’s protocol . Full-length cDNA from the S , M , and L segments was obtained by reverse transcription of CCHFV RNA at 50°C ( Thermoscript RT , Invitrogen , Grand Island , NY , USA ) using a DNA primer complementary to the 3′ end of the viral genomic RNAs . Complete cDNA were sequence-amplified by PCR using high fidelity Phusion enzyme ( New England Biolabs , Ipswich , MA , USA ) and cloned into the pT7 vector . The pT7 vector was previously described as V ( 0 . 0 ) /B [14] , and contains a BsmB1 cloning site located between a T7 promoter and a hepatitis D ribozyme T7 polymerase terminator motif [14] . The S , M , and L cDNAs were cloned into the BsmB1-digested pT7 vector in viral complementary orientation relative to the T7 promoter ( S1 Fig ) . Sequences of all plasmids used to rescue CCHFV are available in GenBank . Primary T7 transcripts derived from pT7-S ( KJ648914 ) , pT7-M ( KJ648915 ) , and pT7-L ( KJ648913 ) contain an artificial G at the 5′ ends , but this nucleotide is rapidly lost upon replication [9] . pC-N ( KJ648912 ) , pC-L ( KJ648911 ) , and T7-M-Renilla constructs were described previously [9 , 19] . pT7-M-ASKA ( KJ648916 ) was obtained by PCR mutagenesis of pT7-M using QuickChange Lightning Site Directed Mutagenesis Kit ( Agilent Technologies , Santa Clara , CA , USA ) per manufacturer’s instructions . pC-L opti ( KJ648910 ) was obtained by optimizing the codons of CCHFV IbAr10200 L ORF for maximal expression in human cells [18] ( GeneArt , Ratisbonne , Germany ) . Both pC-L and pC-L opti were tagged with an N-terminal V5 epitope . pC-T7 [33] was obtained by cloning human codon-optimized bacteriophage T7 RNA Pol ( Genscript , Piscataway , NJ , USA ) into pCAGGS . The T7-M-Renilla minigenome has been previously described [19] . This minigenome is based on our previous Gaussia luciferase version that necessitated transfection of in vitro minigenome transcripts to minimize background activity [9] . In combination with codon optimization of the L-polymerase , changing Gaussia to Renilla luciferase reporter yielded negligible background levels , thereby eliminating the requirement of producing in vitro minigenome transcripts . All attempts to recover CCHFV were performed in BSL-4 facilities at the Centers for Disease Control and Prevention ( Atlanta , GA , USA ) . Six-well plates were seeded with 3 . 5 × 105 BSR-T7/5 cells/well 1 day prior to transfection in 3 mL of DMEM supplemented with 5% FBS . 16–24 h later , cells were transfected with the indicated amounts of pT7-S , pT7-M , pT7-L , pC-L opti , pC-N , and optionally with pC-T7 , combined with 2 . 5 μL of Mirus LT1 transfection reagent ( Mirus Bio , Madison , WI , USA ) per μg of DNA in 250 μL of OPTI-MEM ( Life Technologies , Grand Island , NY , USA ) . For CCHFV-ASKA rescue , pT7-M was substituted with pT7-M-ASKA . 16–24 h post transfection , 5 mL of DMEM with 2% FBS was added to each transfected well . Cell supernatants were harvested 4 days post infection , and 2 mL of supernatant were passaged to a 75 cm2 confluent flask of SW13 cells grown in DMEM with 10% FBS and 1 mM sodium pyruvate . All viruses were titrated using a standard TCID50 protocol in SW13 cells . Cells were fixed with 10% formalin buffered solution . BSR-T7/5 cells were incubated with a CCHFV-specific glycoprotein antibody ( 7F5 , USAMRIID , Fort Detrick , MD , USA ) or with hyperimmune mouse ascetic fluid ( HMAF ) for SW13 cells , followed by incubation with goat anti-mouse Alexa 488-conjugated antibody ( Life Technologies ) . To determine subcellular localization of CCHFV glycoprotein , Vero-E6 cells were infected with CCHFV-WT or CCHFV-ASKA , and stained with 7F5 mAb and rabbit anti-giantin pAb ( Covance , Princeton , NJ , USA ) , followed by incubation with DAPI to stain nuclei , goat anti-mouse Alexa 488-conjugated secondary antibody , and goat anti-rabbit Alexa 546-conjugated secondary antibody . Images were captured with a TCS SP5 confocal microscope ( Leica Microsystems , Buffalo Grove , IL , USA ) . Western blotting and metabolic labeling were performed as previously described [11 , 12] . Briefly , Gn , Gc and glycoprotein precursors were detected with rabbit anti-PreGn/Gn ( kindly provided by A . Mirazimi , Folkhälsomyndigheten , Sweden ) and anti-PreGc/Gc mAb 11E7 ( USAMRIID ) , while N was detected with CCHFV HMAF . For metabolic labeling , SW13 cells were infected with CCHFV at MOI = 0 . 1 for 6 h . Cells were pulsed overnight with 100 μCi/mL of S35-labeled Met and Cys in normal DMEM supplemented with 1% of low-IgG FBS ( Life Technologies ) . Cell supernatants were cleared of contaminating bovine IgG using protein A/G magnetic beads ( Thermo Scientific , Grand Island , NY , USA ) before immunoprecipitating secreted GP38-containing glycoproteins with 8C11 mAb , kindly provided by A . Garrison and C . Schmaljohn ( USAMRIID ) . Immunoprecipitated proteins were separated on Tris-acetate NuPAGE 4–8% gradient gels , and labeled proteins were revealed by autoradiography . CHO-derived cell lines Par6 , FD11 , and FD11-Fur , seeded in MEM-alpha medium with 5% FBS , were infected with recombinant CCHFV WT or RVFV-ΔNSs:GFP-ΔNSm [34] , and fixed in 10% formalin buffered solution . CCHFV-infected cultures were incubated with CCHFV HMAF and goat anti-mouse Alexa 488-conjugated antibody . Nuclei and cells were respectively counterstained with Hoechst 33342 and HCS CellMask Red stain ( Life Technologies ) . Images were acquired with a 20× objective on an Operetta High Content Imaging system ( PerkinElmer Inc , Waltham , MA , USA ) . To detect RVFV-infected cells , EGFP was measured . RNA was isolated from cell supernatants using Magmax technology ( Life Technologies ) and subjected to one-step qRT-PCR using SuperScript III Platinum One-Step qRT-PCR Kit ( Life Technologies ) . CCHFV S segment amplification was performed on an Applied Biosystems 7500 Real-Time PCR System using sense ( 5′ATGAACAGGTGGTTTGAAGAGTT 3′ ) and antisense ( 5′TGGCACTGGCCATCTGA 3′ ) primers , and TaqMan probe ( 5′[6-carboxy-fluorescein ( FAM ) ] TGTCCAAATTGGGAACACTCTCGCA [BlackBerry Quencher ( BBQ ) ] 3′ ) ( TIB Molbiol , Adelphia , NJ , USA ) .
|
Crimean Congo hemorrhagic fever ( CCHF ) is a severe viral disease characterized by rapid-onset fever , hemorrhage , and high case fatality rates . CCHF virus ( CCHFV ) , the causative agent of CCHF , is a negative-strand RNA virus of the family Bunyaviridae ( genus Nairovirus ) . No specific treatments or efficacious vaccines exist to combat CCHF . To investigate molecular determinants of nairovirus pathogenesis and biology , we developed a reverse genetics system capable of generating CCHFV variants with genome sequences defined by the plasmids transfected into cells for virus recovery . Our system is the first to demonstrate that a nairovirus can be efficiently recovered from the simple transfection of plasmid DNA , paving the way for specifically editing genomes of CCHFV and other nairoviruses . Using this system , we engineered mutations blocking the cleavage of CCHFV’s non-structural glycoproteins at a motif recognized by the host protease furin . Using this furin-resistant CCHFV variant , we demonstrate that direct cleavage of the viral glycoprotein by furin results in a lag in virion production , revealing a function of these glycoproteins in efficient CCHFV replication . Our experiments highlight the utility of a reverse genetics system for developing viral variants for investigating CCHFV protein function and for rationally designing vaccine strains .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Recovery of Recombinant Crimean Congo Hemorrhagic Fever Virus Reveals a Function for Non-structural Glycoproteins Cleavage by Furin
|
Associations between the level of single transcripts and single corresponding genetic variants , expression single nucleotide polymorphisms ( eSNPs ) , have been extensively studied and reported . However , most expression traits are complex , involving the cooperative action of multiple SNPs at different loci affecting multiple genes . Finding these cooperating eSNPs by exhaustive search has proven to be statistically challenging . In this paper we utilized availability of sequencing data with transcriptional profiles in the same cohorts to identify two kinds of usual suspects: eSNPs that alter coding sequences or eSNPs within the span of transcription factors ( TFs ) . We utilize a computational framework for considering triplets , each comprised of a SNP and two associated genes . We examine pairs of triplets with such cooperating source eSNPs that are both associated with the same pair of target genes . We characterize such quartets through their genomic , topological and functional properties . We establish that this regulatory structure of cooperating quartets is frequent in real data , but is rarely observed in permutations . eSNP sources are mostly located on different chromosomes and away from their targets . In the majority of quartets , SNPs affect the expression of the two gene targets independently of one another , suggesting a mutually independent rather than a directionally dependent effect . Furthermore , the directions in which the minor allele count of the SNP affects gene expression within quartets are consistent , so that the two source eSNPs either both have the same effect on the target genes or both affect one gene in the opposite direction to the other . Same-effect eSNPs are observed more often than expected by chance . Cooperating quartets reported here in a human system might correspond to bi-fans , a known network motif of four nodes previously described in model organisms . Overall , our analysis offers insights regarding the fine motif structure of human regulatory networks .
Markers associated with changes in gene expression , called eSNPs have been extensively mapped using high throughput genomic data [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] . They allow effectively delineating regulatory associations between each eSNP source and each of its regulated target transcripts . Taken together , these source-target links comprise a regulatory network that abstracts both the genes at source loci as well as their targets as nodes . Regulatory networks have been characterized as featuring specific motifs as their fundamental building blocks [11] , [12] . These motifs occur significantly more than expected by chance and suggest respective functional mechanisms . Specifically , studies in model organisms highlighted the bi-fan motif which consists of two regulators regulating two genes as having a functional role , e . g . of a filter and synchronizer of feedback loop signals [12] , [13] . While previously studied networks are often derived from TF-DNA or protein-protein binding experiments , this work utilizes genetics-genomics data to study the bi-fan motif across a human regulatory network . Model organisms , amenable to pervasive experimental methods , suggest regulatory networks to commonly include structures more complex than single SNP – single gene links , e . g . mapping genetic interactions in yeast [14] , [15] . In humans , where experimental approaches are more limited , eSNPs provide natural perturbations that inform us of similar regulatory links and systems . Concerted analysis of a multitude of eSNPs allows better understanding of the interactions that establish their network structure . Statistically , epistatic interaction is defined as the deviation from additivity in a linear model involving two or more loci [16] , [17] . Unfortunately , finding such association signal for statistical interaction between a pair of SNPs in even a single phenotype has proven computationally difficult [15] , [18] , [19] , [20] . Association analysis across all pairs of SNPs vs . all transcripts exacerbates this tractability problem . While structures of multiple eSNPs to one transcript offer one lens for genetic-genomic analysis , a complementary perspective is provided by regulatory modules , where a single eSNP is associated to multiple genes [1] , [21] , [22] . Modularity of gene regulatory networks was shown to be a major organizing principle of biological systems [23] , with modules often defining functional units of a biological network: each such units consists of a set of elements ( e . g . genes ) working jointly to perform a distinct function . Analysis of single eSNP-single transcript interactions indicates that variation in genomic DNA can affect transcription in multiple ways . Level of transcripts in cis of an eSNP may be altered due to allelic variation in cis-regulatory elements [24] , while trans association can , for example , be the result of an eSNP in a transcription factor that regulates the expression of its distal targets transcripts . Associations in cis are easier to detect because of favorable testing burden . Unfortunately , such associations are limited in their capacity to inform us regarding the network of regulatory interactions between one gene and another , as both the eSNP and the transcript are from the same locus . In contrast , trans eSNPs can identify downstream effects and previously un-annotated regulatory pathways . Moreover , when considering independent association between more than a single eSNP and more than a single gene , the genomic distances between eSNP sources and their gene targets require special attention . In the case of examining a pair of proximal eSNPs , their frequent co-inheritance would induce statistical dependence ( linkage disequilibrium ) between them . Thus , for most independent pairs of eSNPs that cooperate in regulating the same transcript , at least one of them will have a trans effect . In our previous work [1] , we studied eSNPs associated with simplest modular unit of two transcripts , together creating a triplet . We focus on mutually independent triplets , whereby the eSNP association with either of the two transcript remains nominally significant given the respective other transcript , as well as and directionally independent triplets , where only one of these association signals remains nominally significant given the level of the other transcript . We established the occurrence of such triplets in real data significantly more than expected by chance . In this study , we devise a computational framework for examining pairs of triplets that share the same associated two genes . We hypothesize that such eSNP-transcript quartets will highlight true eSNP associations , and demonstrate that by analyzing their distinct topological and functional properties . These properties differ significantly from those of spurious quartets with candidate association signals . Moreover , we replicated those properties in an independent dataset with a larger number of samples [4] , supporting the robustness of our findings . In particular , the two eSNPs in a quartet tend to have independent , but consistent effect on the pair of genes they co-regulate .
We first record genomic annotation categories of eSNP sources ( Figure 3 and Figure S6 in Text S1 ) . eSNP sources tend to be one in exon and one in TF ( Figure 3a; Fisher's exact p<1 . 9×10−8 compared to the permuted set , see Figure S6a in Text S1 ) , or both in exons ( Fisher's exact p<0 . 013 compared to permuted set ) . We notice that most eSNP sources are located on different chromosomes ( 74% Figure 3b ) . For comparison , there are only 3 . 8% of eSNP sources on different chromosomes in the permuted set ( 13 out of 342; Figure S6b in Text S1 ) . An eSNP is said to be in cis of a target if it resides within the span of the target , and in trans otherwise . We characterize the cis/trans regulation of the four pairs of eSNP sources and their gene targets in each quartet by binning quartet data into three cis/trans categories: ( 1 ) two cis relationships ( 2 ) one cis relationship ( 3 ) two trans relationships . We notice that only a fraction of quartets involves cis regulation ( Figure 3c ) , compared to none in the permuted set ( Figure S6c in Text S1 ) . The target genes are located mostly ( 83% ) on different chromosomes which is consistent with empirical expectation based on permutation . They are observed to be co-expressed significantly ( p<4×10−11 ) more often than in real data when comparing the absolute value of the correlation coefficient . These results highlight unique properties of cooperating eSNPs and their distances from target transcripts . Specifically , we show that pairs of eSNP sources are located on different chromosomes . We examine the dependency across association signals for each quartet source , i . e . , whether the effect is mutually independent or directionally dependent . Dependencies within a quartet are therefore either ( 1 ) pair of mutually independent associations ( 2 ) one directionally dependent association and one mutually independent association , or ( 3 ) a pair of directionally dependent associations . We observe that 82% ( 67 out of 82 ) of the quartets are composed of a pair of mutually independent associations ( Figure 4 ) . This is significantly more than expected according to the permuted set , that includes mostly quartets with a pair of directionally dependent associations ( Fisher's exact p<2 . 3×10−35 , Figure S7 in Text S1 ) . These results suggest that the eSNP sources affect the expression levels of both transcripts in a mutually independent manner rather than through directional dependence . We were interested in examining the direction of SNP effects on gene expression . Within quartets we orient all SNP effects by using the convention of up ( down ) regulation to mean positive ( negative ) correlation between the number of copies of the minor SNP allele and the expression level of the associated gene . Out of the 24 = 16 up/down configurations that are theoretically possible between two sources and two targets , we observe only eight configurations in real data – the ones with an even number of “up” effects ( Figure 5 ) . Consideration of the symmetry between the two sources , as well as the one between the two targets , highlights a sense in which these eight categories involve consistent directions of effect , as we now explain . It is natural to classify the categories into four pairs , each defined by two binary criteria . The first criterion considers whether the two source SNPs have the same directions of effect on one gene as they do on the other or whether directions of effect on the second gene are opposite to the first one . The second criterion distinguishes whether the effect of one SNP on the two target genes is in the same direction as the effect the other SNP has on them , or whether directions of effect of the second SNP are opposite ( Figure S8a in Text S1 ) . In contrast to the real data , where all quartets are consistent , 30% ( 101 of 342 ) of quartets in the permuted set are inconsistent quartets ( Figure S8b in Text S1 ) , meaning that the effects of the two SNPs on one of the targets go in the same direction , while their effects on the other target are opposite ( Figure S9 in Text S1 ) . We hypothesized that quartets in real data may be practically forced to be consistent due to correlation patterns across the expression levels of their targets . Specifically , a source SNP would the same ( opposite ) effect on both target genes due to their expression being correlated ( anti-correlated ) . Indeed , we observe this pattern across all quartets in the real data but not always in the permuted set . There are a couple of statistical challenges involved in comparison of real quartets to those observed in permutations ( Note S1 in Text S1 ) . When these are addresses , specifically by analyzing eSNPs sources from the same quartet but from different chromosomes , we observe them to be enriched for same-direction effects compared to their permuted set counterparts ( Figure 6 and Figure S8c in Text S1 ) and the gene targets to be located on different chromosomes . We listed all characterizing features of cooperating quartets ( Table S1 ) . A particularly illustrative sub-group of 7 quartets includes those with eSNP sources and gene targets along the MHC region of chromosome 6 ( Table S1 ) . This is significantly more ( Fisher's exact p<0 . 0014 ) than 4 out 342 ( ∼1% ) in the permuted set . The eSNP sources collapse to reference alleles of rs9274634 , rs1129740 , rs1142334 , rs9274389 and rs2808143 and non-reference alleles of rs1130034 , rs8227 , rs1130116 and rs9272851 downregulating HLA-DQA1 and HLA-DQB1 and upregulating HLA-DQA2 and HLA-DQB2 . These common variants are shared by specific assembled sequences ( Table S3 in Text S1 ) . All the genes containing eSNP sources and target genes are collapsed into the following four HLA genes: HLA-DQB1 , HLA-DQA1 , HLA-DQB2 and HLA-DQA2 ( Figure 7 ) . All four genes are involved with the MHC class II receptor activity ( enrichment FDR<1 . 4 . 10−12 ) , and serve as an example how quartet structures create functional units . We perform a gene set enrichment analysis to examine if the pair of gene targets shares a GO category significantly more than pairs in the permuted set . In this case we observe a higher number of shared descriptors which is not significant in this dataset ( Fisher exact p-value<0 . 14 ) . Interestingly , when we focus the enrichment analysis on pairs of genes that harbor cooperating SNP sources , we observe a significant difference ( Fisher exact p-value<1 . 5×10−6 ) . This supports our ability to detect SNPs that cooperate together to perform a joint function . We were intrigued to examine if our approach could be applied to understand gene regulatory networks underlying complex diseases . We therefore utilized the GWAS catalog [27] to find all genes that harbor a GWAS SNPs in our dataset . We then intersected this list with the genes that harbor cooperating SNPs in real data and compared to permutations . We observe a significant overlap of GWAS loci with at least one eSNP source , for quartets with sources that reside on different genes ( Fisher exact p-value<0 . 017 ) . This indicates that our approach could shed light on regulatory circuits that are involved in complex disease . For example , in quartet #35 ( Table S2 in Text S1 ) eSNP sources rs16877111 and rs7925000 are on chr5 and chr11 respectively . The eSNP sources reside in genes CMYA5 and RPL27A which are obesity GWAS loci . The gene targets HIST1H1D and HIST1H2AH are part of a histone cluster on chr 6 . Since our initial study was underpowered , we attempted to replicate the discovered properties of cooperating quartets in a larger , more recent dataset . We hypothesized that the fraction of true positives among signals of association to be higher is such a dataset , thereby pointing to true characteristics of quartets , rather than potential artifacts of false positive signals . We repeat our analysis in the Geuvadis [4] dataset for each of its five populations: Utah European ( CEU; n = 91 ) , Finnish ( FIN , n = 95 ) , British ( GBR; n = 94 ) , Italian ( TSI; n = 93 ) and Yoruban ( YRI; n = 89 ) as well as on the combined set of all European samples ( n = 373 ) . We observe that the number of association signals achieving p-value<10−5 is enriched in true positive associations ( ∼5 fold more associations than expected ) . Overall , we replicate all properties ( Same effect of both eSNPs , distal regulation , eSNP sources on different chromosomes , gene targets on different chromosomes and consistency of quartets ) that were found in the smaller dataset , most of them at higher frequencies ( Table S2 in Text S1 ) . This provides an additional support from an independent dataset to the validity of quartets and their characteristics .
Discovering the building blocks of regulatory network has been an active field of research in the last decade [12] , [28] . Specifically , the human regulatory network was the focus of a multiple recent studies involving diverse data types [11] , [29] . In this work we devised a computational framework to study characteristics of cooperating quartets comprised of a pair of cooperating eSNP sources that reside either in exons or in the span of TFs , and a pair of associated target transcripts . Our results establish that the regulatory structure of cooperating quartets is nearly exclusive to real data , and exhibits unique functional , genomic and topological characteristics . Cooperating quartets reported here in a human system might correspond to bi-fans , a known network motif of four nodes , previously described in model organisms [12] . Most cooperating quartets involve pairs of eSNP sources located on different chromosomes , away from their targets , which are themselves mostly located on different chromosomes . These quartets typically comprise of a pair of mutually independent association signals . All quartets are consistent in terms of the direction of eSNP effects on correlated and anti-correlated transcripts . We identify a separate sub-group of quartets with eSNP sources and gene targets all involving 4 MCH Class II genes from chromosome 6 , highlighting a functional unit built from the quartet motif . This study holds the promise for extension beyond its current limitations . First , our focus on causal variants localized to the single-base resolution imposed relying on a dataset of fully sequenced individuals along with their transcription profiles . Such cohort sizes are limited in size , reducing the power to detect association and allowing us to observe only the strongest effects . Potential increase in sample size for expression quantitative trait loci ( eQTL ) data would enable detection of eSNP associations and regulatory motifs at greater significance and confidence . Second , the current analysis focuses on discovering a network motif where pairs of transcripts are co-regulated by a pair of variants . Mining the data for additional motifs can elucidate other structures in the human regulatory network . Overall , both the raw datasets [25] , [26] and supporting databases [30] , [31] , [32] , [33] in this work were noisy and limited . As functional annotation continues to build up , better understanding of motifs would be facilitated . In this and in our previous work [1] we define network motifs showing them to be prevalent in real data , explaining the organization of trans regulation . Comparison of such structures between healthy and affected samples and across different tissues is likely to improve understanding of disease and developmental regulatory processes . Future studies could expand this approach to focus on complex disease circuits by using this framework on a dataset that is focused on GWAS SNPs and find quartets where the eSNP sources are also known GWAS loci . The vast majority of eQTL studies involve analyses that are based on considering a single SNP associated with a single transcript , primarily in cis [4] , [26] , [34] , [35] . While these analyses capture only a fraction of genetic contribution to changes in the regulatory landscape , the advantage is high statistical power for detecting associations . A complementary effort focuses on building networks from eSNP data [21] , [36] , [37] , [38] . While these studies provide much more comprehensive models , they lack the same strength of statistical assurance in their findings . The main advantage of our approach is that it provides a unique framework for analyzing eSNP data by bridging these two approaches , establishing statistical guarantees on our inferred results using permutations . Applying such analysis to different datasets can shed light on the architecture of the human regulatory network and the role genetics plays in shaping it .
We analyze a cohort of 50 Yoruban samples , for which genotypes of SNVs that are fully ascertained from sequencing data [25] along with RNA-seq data [26] are publicly available . Briefly , the raw dataset consists of 10 , 553 , 953 genotyped SNVs and expression measurements ( quantile-quantile normalized values ) of 18 , 147 genes with Ensembl gene ID across these 50 samples . Standard filters have been applied to the genetic data: Minor allele frequency >0 . 05 , SNP missingness rate <0 . 1 and individual missingness rate <0 . 1 [39] . After filtering , data for analysis consists of 50 samples with 7 , 206 , 056 SNPs . The Geuvadis [4] dataset that we use for replication consists of five populations: Utah European ( CEU; n = 91 ) , Finnish ( FIN , n = 95 ) , British ( GBR; n = 94 ) , Italian ( TSI; n = 93 ) and Yoruban ( YRI; n = 89 ) as well as on the combined set of all European samples ( n = 373 ) . After filtering all SNPs with Minor allele frequency <0 . 05 and focusing only on SNPs in exons and TFs , there are 42810 , 43561 , 43279 , 43214 , 61960 and 43365 for CEU , FIN , GBR , TSI , YRI and EUR respectively . For association analysis , we consider only SNPs that reside within candidate regulatory regions along the genome . In Kreimer et al . [1] we detect enrichment in trans association signals for eSNPs in exons and in TFs in this dataset . For TFs , the number of multiple associated transcripts is significantly higher for TFs in the real dataset than in permuted data sets . For exons , there is an excess of the number of eSNPs within exons indicating true positive results . We test for association between a SNP and every gene; we consider SNPs within the span of known exons and TFs ( including introns ) [40] , [41] . We test for association using linear regression performed by the –assoc command in PLINK [39] . Examining the random distribution of association tests is helpful in evaluating the empirical significance of results . This is achieved by generating 100 permutations that shuffle the sample IDs . This allows repeating the analysis of genotypes vs . expression on permuted data while maintaining the correlation structure among the genotype profiles and among the expression profiles , separately . We assemble quartets from directionally and mutually independent triplets that consist of a SNP and two associated genes . A mutually independent triplet is when both of the association pairs remain nominally significant given the respective other gene and a directionally independent triplet is where only one of the association pairs remain nominally significant given the other gene . Two triplets that share the same associated genes define a quartet . We then filter these quartets further using the following rules: The code for all methods presented in this thesis can be found in the following link: http://www . columbia . edu/~ak2996/Software . htm
|
Previous analysis charts ubiquitous associations between the level of single transcripts and single corresponding genetic variants , eSNPs . However , most expression traits are complex , involving the cooperative action of multiple SNPs at different loci affecting multiple genes . The basic structure of variants within two source genes that affect the expression of two different target genes is compelling in terms of its potential to divulge information regarding the features of more complex interactions . We therefore devised a computational framework for the analysis of such variants , as ascertained from genomic sequencing data along with gene expression profiling of the same cohort . We focus on genetic markers that reside within interpretable regions of the genome: exon sequences , or transcription factors . Such cooperating eSNP sources are both associated with the same pair of target transcripts . We characterize such quartets through their genomic , topological and functional properties . Our findings suggest that this regulatory structure of quartets exhibits distinct characteristics and is frequent in real data , but is rarely observed in permutations .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genomics",
"gene",
"regulatory",
"networks",
"gene",
"expression",
"genetics",
"biology",
"and",
"life",
"sciences",
"computational",
"biology"
] |
2014
|
Co-regulated Transcripts Associated to Cooperating eSNPs Define Bi-fan Motifs in Human Gene Networks
|
Primary health care facilities frequently manage dengue cases on an ambulatory basis for the duration of the patient’s illness . There is a great opportunity for specific messaging , aimed to reduce dengue virus ( DENV ) transmission in and around the home , to be directly targeted toward this high-risk ambulatory patient group , as part of an integrated approach to dengue management . The extent however , to which physicians understand , and can themselves effectively communicate strategies to stop focal DENV transmission around an ambulatory dengue case is unknown; the matter of patient comprehension and recollection then ensues . In addition , the effectiveness of N , N-diethyl-3-methylbenzamide ( DEET ) -based insect repellent in protecting dengue patients from Aedes aegypti mosquitoes’ bites has not been investigated . A knowledge , attitude and practice ( KAP ) survey , focusing on the mechanisms of DENV transmission and prevention , was performed using semi-structured questionnaires . This survey was targeted towards the patients and family members providing supportive care , and physicians routinely involved in dengue patient management in Southern Vietnam . An additional clinical observational study was conducted to measure the efficacy of a widely-used 13% DEET-based insect repellent to repel Ae . aegypti mosquitoes from the forearms of dengue cases and matched healthy controls . Among both the physician ( n = 50 ) and patient ( n = 49 ) groups there were several respondents lacking a coherent understanding of DENV transmission , leading to some inappropriate attitudes and inadequate acute preventive practices in the household . The application of insect repellent to protect patients and their relatives from mosquito bites was frequently recommended by majority of physicians ( 78% ) participating in the survey . Nevertheless , our tested topical application of 13% DEET conferred only ~1hr median protection time from Ae . aegypti landing . This is notably shorter than that advertised on the manufacturer’s label . No differences in landing time between febrile dengue cases or matched healthy controls ( n = 19 experiments ) were observed . Our study identifies missed opportunities for primary care physicians to improve public health through communication of strategies that could prevent focal dengue transmission in and around a case household . We advocate better access to more efficient communication methods for physicians and auxilliary health workers , supporting to educate those at high risk of DENV transmission . Our empirical testing of a widely-available 13% DEET-based repellent was limited in its protective efficacy against Ae . aegypti mosquito bites , and therefore DENV transmission , suggesting more frequent application is necessary to be beneficial .
Dengue is the most important arboviral infection of humans globally and is primarily transmitted between humans through the bites of Aedes aegypti mosquitoes [1 , 2] . It is estimated that there are approximately 390 million infections annually , and 3 . 9 billion people living at risk of dengue virus ( DENV ) infection [3] . Vietnam is a country with a 50+ year history of sustained DENV transmission . Dengue epidemics in this country were first reported in 1959 in northern Vietnam [4] . Since then , Vietnam has become a country with hyperendemic DENV transmission ( i . e . : with the co-circulation of all four serotypes of DENV ) , and an increasing annual incidence of hospitalized dengue cases throughout the country [5–7] . Dengue prevention has now entered a new era with a live , attenuated , tetravalent dengue vaccine of Sanofi-Pasteur recently licensed in Mexico , Brazil and the Philippines . However , in Vietnam and many other countries where dengue is endemic , the vaccine has not yet been licensed , and mosquito control therefore remains the mainstay for preventing DENV transmission in the community . Symptomatic dengue patients had been found to transmit DENV to Aedes spp . mosquitoes from ~1 . 5–2 days prior to fever onset until day 3–6 of illness [8–10] . Recently , Nguyet et al . [11] and Whitehorn et al . [12] have identified the plasma DENV viremia levels in Vietnamese dengue patients needed to successfully infect 50% of exposed Ae . aegypti or Ae . albopictus mosquitoes ( 50% mosquito infectious dose; MID50 ) . In addition , most ambulatory dengue cases were revealed to have viremia levels that exceed the MID50 in the first few days of their illness [11] , indicating that most are still infectious to Ae . aegypti when they return to their homes . Epidemiological studies also confirm the home is a location of risk with respect to DENV transmission [13 , 14] . The capacity of asymptomatically-infected individuals to transmit to Ae . aegypti has been recently highlighted , but more work is needed to understand their prevalence and importance to the overall epidemiology of dengue [10] . Physicians managing dengue cases in outpatient or primary care settings , are ideally placed to communicate how best to reduce the risk of DENV in an affected household . Previous studies have explored the Knowledge , Attitude and Practice ( KAP ) of communities affected by dengue , but surprisingly , such studies have never specifically targeted physicians who manage cases , nor the affected dengue patients or their primary caregivers [15–22] . A robust understanding of how DENV is transmitted between humans , and what interventions can effectively reduce the risk of further dengue transmission is central to the success of physician-led initiatives . An equally important issue is the extent to which patients and their families are able to understand and act upon the information given by their primary care physicians . Mosquito repellents containing N , N-Diethyl-3-Methylbenzamide ( DEET ) are the most effective insect repellent [23–29] . Dengue patients , like other febrile patients , may be more attractive to mosquitoes compared to non-febrile individuals due to their higher body temperature , and therefore higher production of carbon dioxide and possibly other volatile compounds [30–32] . As a result , the mosquito repellents may afford reduced protection for febrile dengue patients compared to healthy individuals . The aims of this study were a ) to describe the general KAP of physicians and patients regarding dengue and DENV transmission; b ) to understand the physicians perception of what they communicate to patients regarding local prevention of dengue transmission; and c ) to investigate whether patients understand and recall the information provided by their physicians and then act upon these suggestions . Finally , we aimed d ) to determine the efficacy of a DEET-containing topical mosquito repellent widely-used in Vietnam , and assess whether efficacy differed between febrile dengue patients and non-febrile healthy controls . Findings from this study can improve dialogue about dengue and DENV transmission between physicians and their patients , by highlighting barriers in communication and knowledge gaps .
Protocol and questionnaires of the KAP survey were reviewed and approved by the Ethics Committee of Hospital for Tropical Diseases ( HTD ) , Ho Chi Minh City , Vietnam ( CS/ND/14/25 ) . This survey was considered low risk , hence did not need formal approval from the Oxford University Tropical Research Ethics Committee ( OxTREC ) . The topical repellent study was approved by the Ethics Committee of HTD ( CS/ND/12/12 ) and the OxTREC ( OxTREC 66–11 ) . All patients and healthy volunteers gave written informed consent to participate in the repellent study . This observational study assessed the effectiveness of a commercial 13% N , N-Diethyl-3-methylbenzamide ( DEET ) -based repellent ( “Soffell” , Fountain of Youth , Pty Ltd , Singapore ) against the landing of Ae . aegypti mosquitoes , on Vietnamese adult febrile dengue patients compared to adult healthy controls , using an arm-in-cage method [26] . Baseline characteristics of participants were described with categorical variables presented as proportion , and continuous variables reported as median ( with interquartile range [IQR] ) . Responses of physicians and patients for each question were presented as the proportion of respondents who selected each answer . The distribution of the time to mosquito-landing in febrile dengue cases and healthy controls , respectively , was estimated using the Kaplan-Meier method . The effectiveness of repellent for each participant was calculated as the difference in time elapsed at landing of mosquitoes between the repellent-treated and non-treated arms . The time to landing in the repellent and the non-repellent arms , respectively , were compared between dengue patients and controls with a Cox Proportional Hazard regression model . Robust standard errors were used to account for the matched design . A similar Cox model was used to assess the influence of body temperature on the time to landing . All data analyses were performed with the statistical software R , version 3 . 1 . 1 ( R Foundation for Statistical Computing , Vienna , Austria ) .
A total of 70 physicians were invited to join the survey; 16 declined to participate and four failed to complete the questionnaire , resulting in 50 questionnaires available for analysis . The profile of the responding physicians , including their clinical specialties , is shown in Table 1 . Most physicians said that the primary DENV vector mainly breeds in artificial water containers ( 68% ) , and that an effective vaccine for dengue is not currently available ( 82% ) . Interestingly , seven ( 14% ) physicians thought that Anopheles spp . mosquitoes are the primary vector of DENV ( S1 Information ) . In addition , 52% of physicians believed that only some serotypes of DENV could cause disease . Regarding the likelihood of mosquitoes becoming infected with DENV when blood-feeding on dengue patients , 29 physicians ( 58% ) thought that the mosquitoes would immediately become infected with DENV and could transmit the virus to other people in their next bites , while 14 ( 28% ) thought that the mosquitoes could not be infected if the concentration of DENV in the patient’s blood was too low . There were 21 ( 42% ) respondents who believed that dengue patients could be infectious to mosquitoes throughout the febrile phase of their illness , whereas 4 ( 8% ) thought that infectiousness lasted for 1–2 days; 19 ( 38% ) thought it could last for 1–2 weeks and the remaining six respondents ( 12% ) said that they did not know for how long dengue patients could be infectious to mosquitoes ( Fig 2 and S1 Information ) . Almost all respondents stated that dengue was a very important disease in their daily clinical practice ( 92% ) , and believed it was their responsibility to discuss DENV transmission and prevention with patients and caregivers ( 94% ) . The majority reported that they routinely provide specific information regarding how the patient was infected ( 96% ) and how to prevent patients from receiving further bites from Aedes spp . ( 82% ) , as well as how to reduce the risk of DENV infection for other people living in the same house ( 88% ) ( Fig 3 ) . An integrated programme of interventions comprising use of mosquito bed nets ( 100% ) , personal mosquito repellents ( 78% ) , and insecticide spraying of adult mosquitoes ( 78% ) was commonly recommended by physicians to protect dengue patients from subsequent Aedes spp . bites . In terms of reducing risk of DENV infection in the household , removing mosquito-breeding sites was the most common recommendation ( 98% ) , followed by avoiding mosquito bites ( 86% ) and killing adult mosquitoes ( 82% ) . Patients ( n = 4 ) and the primary caregivers of those who were under 15 years of age ( n = 47 independent cases ) were approached for interview at a median of 14 ( IQR = 12–14 ) days after their initial enrolment into one of the two outpatient studies . Survey responses from two participants ( both of whom were caregivers ) were excluded because they did not complete the survey . Therefore , in total 49 questionnaires were used for further analysis . Baseline characteristics of respondents in this group are presented in Table 2 . Among these 49 patients , 22 ( 45% ) had laboratory confirmation of a DENV infection . The majority of respondents agreed that dengue is a common disease in Vietnam ( 82% ) and that this disease could be serious , even life-threatening ( 96% ) . Ninety-six percent of respondents ( n = 47 ) believed that DENV could be transmitted between humans through mosquito bites , among those 4% ( n = 2 ) believed that DENV are transmitted by all types of mosquitoes while 9% ( n = 4 ) said that they did not know about this statement and 87% ( n = 41 ) disagreed with the statement . In regards to knowledge on the breeding sites of the mosquito vectors , 72% ( n = 34 ) stated they bred primarily in artificial water containers ( Fig 2 ) . A further 60% ( n = 28 ) said the mosquitoes mainly bite humans at night; only 21% disagreed with this statement ( S2 Information ) , and 77% ( n = 36 ) reported routinely performing control measures to reduce mosquito abundance within their home ( at least once a week ) . Killing mosquitoes and removing larval breeding sites were routinely performed by 47 . 2% of households ( S2 Information ) . Among all 49 respondents 61% ( n = 30 ) believed that DENV-infected people were not contagious to others through the respiratory tract , i . e . through coughing or sneezing; 2% ( n = 1 ) believed that this could be another potential mode of DENV transmission in the community , while the remaining 37% ( n = 18 ) of participants said that they did not know . All physicians reported that they delivered information verbally about DENV transmission and prevention during their medical consultation with patients . Leaflets ( 44% ) and booklets ( 4% ) were also reportedly used as additional methods . Nonetheless , 56% of physicians ( n = 28 ) said that they could spend only 1–3 minutes to perform the entire medical consultation , including discussion of the diagnosis , management and any steps to stop further transmission . Only 18 ( 36% ) physicians were able to spend more than 3 minutes with each patient . In turn , most physicians ( 88% ) felt that time was the most common barrier to greater discussion of DENV transmission and disease prevention . Patients’ perceived levels of interest ( 36% ) and physicians’ self-confidence ( 12% ) were also considered barriers of disseminating information between physicians and patients . Moreover , physicians also reported that they lacked the training on DENV transmission cycle and prevention ( n = 1 ) ; and that the role of transferring information about disease prevention is forgotten during the medical consultation ( n = 1 ) ( Fig 4 ) . Most patients ( 86% ) stated that they had received clear and understandable information from their physicians about the patient’s health status . Yet a much smaller proportion of respondents reported not receiving any information about how DENV could be transmitted between humans ( 37% ) , the risk of DENV infection of other members in the home ( 41% ) , how to prevent subsequent mosquito bites to the patient ( 20% ) , or how to reduce the risk of DENV infection to other family members ( 31% ) ( Fig 3 ) . All patients who recalled that a physician had provided recommendations about how to limit mosquito exposure for the patient reported they had acted upon these suggestions ( Tables 3 and 4 ) . In 39 respondents who did not recall receiving suggestions from their physicians , 82% ( n = 32 ) performed actions to prevent the patient from mosquito bites anyway . The use of mosquito bed nets was the most common action to avoid mosquito bites , taken by 81 . 5% ( n = 26 ) of these 32 respondents . Other measures included insecticide sprays , using an electric bat to kill adult mosquitoes and applying topical insect repellent ( Table 3 ) . One family reported that one of the actions they performed to prevent DENV transmission within their house was to isolate the dengue patient from the other family members . The effectiveness of topical insect repellent for preventing Ae . aegypti landing on either dengue cases or matched healthy controls was investigated . Of the 20 independent mosquito exposure experiments performed , 19 were assessable , and the baseline characteristics of the participants are shown in Table 5 . The effectiveness of 13% DEET-based repellent , as measured by the median difference between landing times of repellent-treated and non-treated arms , was 49m32s ( IQR = 16m01s–01h19m29s ) and 56m43s ( IQR = 15m55s–02h20m00s ) for healthy controls and patients , respectively ( Fig 5 ) . For the 13% DEET-based repellent arms , there was no significant difference in elapsed time to landing between patients versus controls ( Hazard ratio 0 . 71; 95% CI 0 . 42–1 . 19; p = 0 . 20 ) , and in approximately half ( 11/19 , 58% ) of the experiments mosquitoes landed on the patient’s skin first . There was also no clear evidence that the host’s tympanic temperature affected the duration of protection from Ae . aegypti landing , irrespective of the hosts’ febrile status ( Hazard ratio 0 . 89; 95% CI 0 . 61–1 . 29; p = 0 . 53 per +1°C increase in tympanic temperature ) . Further , Ae . aegypti appeared to be similarly attracted to both febrile dengue patients and healthy controls when no repellent was worn , with a median time to landing of 18s ( 10s–40s ) in the control group versus 28s ( 17s–44s ) in dengue patients ( Hazard ratio of dengue patients vs . control: 0 . 64; 95% CI 0 . 36–1 . 14; p = 0 . 13 ) .
In southern Vietnam , more than 60 , 000 dengue cases are hospitalized each year [6] . The number of clinically suspected dengue patients presenting at outpatient clinics , but whose illness does not warrant hospitalization ( and therefore is not mandated for reporting to the Ministry of Health ) , is believed to be much higher . Some visits represent the patients’ first contact with health care services , others represent follow-up visits as part of a clinical management plan . In principle , physicians engaged in these consultations are superbly placed to deliver messages that could help stop DENV transmission within an affected household and/or to the surrounding community . Yet remarkably , this is to our knowledge , the first reported KAP survey of both physicians and patients with regard to their understanding of DENV transmission and prevention . The results demonstrate that a proportion of Vietnamese physicians involved in dengue case management lack of full understanding of DENV transmission dynamics . For instance , the confusion of Anopheles spp . with Aedes spp . mosquitoes as the primary vector of DENV; or knowledge of the peak biting times of the Aedes spp . mosquitoes ( S1 Information ) . As a result , these physicians may disseminate the incorrect information , creating confusion amongst the community due to different messages provided by health workers in the preventive medicine program , thereby reducing the overall benefit of community-based health education on dengue control [33] . In Vietnam , the national health system requires that all physicians , both general and specialized ( in any field ) , must work at outpatient clinics on rotation . Physicians may also provide medical consultation at their own private office after their normal working time . As a result , dengue patients presenting to outpatient clinics may be seen not only by infectious disease specialists or pediatricians , but by those specializing in internal medicine , emergency medicine or general practitioners , for example . Such physician demographics were represented in our survey , with 50% of physicians being specialized in fields other than infectious diseases and pediatrics . Accordingly , without a background in infectious diseases or pediatric illnesses , the main focus of the physician is more likely to be on patients’ immediate well-being as opposed to dissemination of public health messages that aim to reduce subsequent transmission . Moreover , due to the very small number of physicians available to treat a very large number of people within the general public ( only 7 . 2 physicians ( in all fields ) per 10 , 000 population in 2010 [34] ) , the high case load of suspected dengue patients presenting at the outpatient clinics every day , and the limited time available to spend with each patient , it is often challenging for physicians to dispense clinical advice , sometimes combined with public health messages about DENV transmission . Nonetheless , as per the WHO guidelines in dengue diagnosis and treatment [35] , each suspected dengue patient , who presents at the outpatient clinics during the early phase of their illness , should come back for follow-up visits during their illness . Therefore , these recurrent visits provide additional opportunity for physicians not only to discuss with patients and caregivers about patients’ illness , but also about how to prevent further DENV transmission . To assist physicians and other health workers ( nurses , assistant physicians ) in meeting these demands , health care services can develop more effective strategies to communicate practical dengue control measures , and make these available to health workers in outpatient clinics , for distribution to members of dengue-affected households . Leaflets and booklets are useful for communicating clear and succinct information to patients about dengue and minimizing risk of DENV transmission . Further , this information can be kept on hand , and used as a reference , encouraging preventive measures to reduce further DENV transmission . Interestingly however , despite some patients having incomplete knowledge about the specifics of DENV transmission , the majority of patients and their families had an acute awareness of DENV and knowledge of ways to prevent further transmission in the household . For instance , misunderstanding about the peak biting times of Ae . aegypti probably explains the reliance on mosquito bed nets by some families , a practice unlikely to significantly reduce the risk of being bitten by Ae . aegypti mosquitoes , and therefore the risk of DENV infection , since these mosquitoes are day-time feeders , with peak biting periods early in the morning and evening before dusk [1] . Yet , most patients reported avoiding mosquito bites and killing adult mosquitoes to avoid further DENV transmission . Despite the finding that majority of physicians reported a sense of responsibility for educating patients about DENV transmission , more than half of the patients said they did not receive , could not recall , or apparently misunderstood some specific information from their physicians during the consultations . Both time constraints , and sometimes a lack of specific knowledge on DENV transmission among physicians can prevent patients being provided with the required knowledge to maximize effective vector control in their homes , and hence minimize focal DENV transmission within households [36] . Support from the health system through provision and distribution of brochures and leaflets at outpatient clinics may alleviate this problem , by relaying consistent and accurate advice . The disparity in the responses of physicians and families about DENV transmission and prevention is likely due to several factors . First , the physicians were likely to respond to questions on their preventive practice based on what they consider to be ideal responses , as opposed to what they actually do in their daily clinical practice , so these results need to be interpreted conservatively . Second , these results suggest that patients and families may have failed to understand or recall all the information presented by their physicians during the consultation . On the other hand , information on DENV transmission and its prevention are also periodically disseminated to community , especially before and during dengue season , by auxiliary health workers at the commune level and preventive medicine centers using posters , brochures , leaflets or multimedia tools ( e . g . radio , television ) . Therefore , even though patients and their families did not receive any recommendation from their physicians during the medical consultation , they may voluntarily act to reduce risk of DENV infections in community . Methods for assessing the behavioral impact of these communication tools , such as the Communication for Behavioral Impact ( COMBI ) methodology [37] , should be employed to measure the effectiveness of communication tools that promote vector control behavior in the community . Participants in the patient group KAP survey were recruited from among subjects already enrolled into other dengue research studies being conducted by our group; thus dedicated study physicians ( who did not participate in the physician survey ) had provided information about dengue to these participants or their family members during the informed consent process less than 2 weeks prior to presentation of our survey . Therefore , the information that patients received from these study physicians was likely to have been more detailed than that received by the average patient at a local clinic . Despite this , results indicate that the understanding and/or recollection of information disseminated by their physicians during the consultation by patients was incomplete . Further , although the patient responses to the KAP survey here may not be representative of the general experience of most dengue patients when seeking treatment , there is no reason to suggest that their motivation to act upon their physician’s recommendations are any different to the general public . It is important to note that none of the physicians participating in our survey were involved in either of the two dengue studies through which we recruited participants into the patient group . A further study with a larger sample size is needed to better understand the actual situation regarding KAP in different health care levels , on dissemination of medical and practical information relating to DENV infection and transmission . In the KAP survey , the use of insect repellent was one of the common measures that physicians recommended to their patients to help protect both patients and other relatives in the household from mosquito bites . Insect repellents have been demonstrated to effectively repel insects , among which , DEET-based preparations provide the greatest protection [23–29] . In testing the efficacy of a locally available 13% DEET-based repellent , however , we found no difference in the duration of protection from the DEET-based repellent between dengue patients and healthy control participants . This suggests that DENV infection , and the accompanying fever , does not reduce the repellent’s effectiveness , and personal repellent use by both the dengue patient , and people living in the same household , should continue to be a public health recommendation . This is the first study in which the effectiveness of mosquito repellent on febrile dengue patients , as compared to healthy controls , has been tested . Although dengue patients in this study were in days 3–6 of illness , we know that dengue patients can be infectious to mosquitoes even in the first days of illness , and before they become symptomatic [8 , 11] . We therefore advocate an integrated approach to stop focal transmission in the home; frequent use of a topical repellent by the patient will stop new mosquito infections , while killing existing mosquitoes ( that were potentially already exposed to the virus ) can prevent subsequent infections in naïve human hosts . The duration of effectiveness of our chosen DEET-based mosquito repellent on non-febrile healthy participants was shorter than that of other products reported in other studies , as well as that stated by manufacturer on the product label ( up to 10 hours protection ) [23–29] . Differences in formulation and strength are known to alter the protective duration of repellent activity [38–40] . Variations in efficacy of mosquito repellent may be influenced by a variety of biological and non-biological factors , such as environmental conditions ( light , temperature , humidity ) [41] , experimental procedures ( repellent dose , exposure time , mosquito cage size ) [42 , 43] , mosquito age , species and strain [44 , 45]; as well as the attractiveness of individual volunteers [30 , 31 , 41 , 45 , 46] . Despite this , even with a higher concentration of DEET ( 13% versus 4 . 75% ) , and a larger volume of repellent ( 2 mL versus 1 mL ) , the product we used protected participants from a host-seeking mosquito for a relatively shorter duration ( 00h49m32s versus 01h28m24s ) than the commercial DEET-based repellent ( OFF ! Skintastic for Kids ) , reported in the study of Fradin et al . [26] . The product we tested also provided a considerably shorter duration of protection ( 00h49m32s versus 04h00m00s ) than a more comparable 15% DEET-based product ( OFF ! Active ) , used by Bissinger et al . [23] . Therefore , while our study advocates that physicians’ continue to recommend the use of insect repellent to protect dengue patients as well as other family members from mosquito bites , our data suggest that the frequency of application should be considerably greater than that stipulated on the label to maintain effective protection . In conclusion , our KAP survey highlights the importance of physicians , as well as other heath care workers , being adequately knowledgeable about dengue and the DENV transmission cycle , because these data indicate that patients respect , and adhere to the practical advice provided by their consulting physician . In addition to knowledge gaps among physicians , we have demonstrated that insufficient consultation time to disseminate information is a common and important problem . These issues become barriers to what could otherwise be an effective medium for spreading practical information about preventing DENV transmission to those who would benefit from it most . The results of this survey can be used to help develop a suitable training programme for physicians involved in dengue case management . Patients and caregivers also failed to recall information from their physicians , likely a result of brief physician consults and/or ineffective modes of communication . In a region like Southern Vietnam where DENV is hyperendemic year-round , a combination of targeted messaging , and community-wide education campaigns would benefit all community residents . Future surveys should compare effectiveness of communication methods to enhance the quality and frequency of interventions that householders can apply to prevent further dengue transmission in their home and community . The use of insect repellent was commonly recommended by physicians to protect dengue patients and their family members from mosquito bites . Our repellent experiments therefore investigated DEET-based mosquito repellent , and found that it provides similar duration of protection to febrile and afebrile people against mosquito bites . We advocate that physicians continue to recommend the application of DEET-based repellent to avoid mosquito bites , however that this application be more frequent than that on the manufacturer’s label . This , in conjunction with active vector control efforts in the home should help to subsequently reduce the risk of further DENV transmission . Moreover , findings from the KAP survey and mosquito repellent experiments can also be used to assist developing effectively vector control for DENV and other flaviviruses transmitted by Aedes spp .
|
In endemic countries many dengue cases are managed on an ambulatory basis throughout their illness . Many of these ambulatory cases will be infectious to Ae . aegypti mosquitoes for some period of the febrile phase . A survey of two key actors , physicians who manage dengue cases and the primary caregiver to dengue cases , was performed to assess their knowledge , attitudes and practices on the mechanism of DENV transmission and prevention . In addition , the duration of protection from mosquito landing was measured and compared between febrile dengue patients and healthy volunteers using a locally available , and widely-used 13% N , N-Diethyl-3-methylbenzamide ( DEET ) -based topical insect repellent . This KAP survey demonstrated an incomplete understanding of DENV transmission among several patients and physicians , leading to some inappropriate attitudes and inadequate preventive practices in the household . This suggests that aspects of the knowledge , communication between physicians and patients about dengue , and what patients have retained from the clinical consultation , can be improved . The use of topical insect repellent to protect patients and relatives from mosquito bites was recommended by a large proportion of physicians . However , the repellent used in this experiment provided only a modest repellent effect to Ae . aegypti mosquitoes , with no difference in protection time when applied to either dengue patients or healthy controls . Future recommendations to patients should continue to promote repellent use , but advocate much more frequent application . To directly target those patients at risk of onward DENV transmission , this research highlights the potential benefits of physicians imparting practicable and effective advice on preventive measures of dengue case management , as part of a holistic approach to dengue control . Finally , findings of this work can provide data for approaching effectively preventive practices for DENV and other flaviviruses transmitted by Aedes spp .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"physicians",
"medical",
"doctors",
"geographical",
"locations",
"patient",
"advocacy",
"animals",
"health",
"care",
"pediatrics",
"research",
"design",
"health",
"care",
"providers",
"surveys",
"pediatric",
"infections",
"patients",
"insect",
"vectors",
"research",
"and",
"analysis",
"methods",
"epidemiology",
"vietnam",
"disease",
"vectors",
"insects",
"arthropoda",
"people",
"and",
"places",
"professions",
"mosquitoes",
"survey",
"research",
"asia",
"population",
"groupings",
"biology",
"and",
"life",
"sciences",
"organisms"
] |
2016
|
Physicians, Primary Caregivers and Topical Repellent: All Under-Utilised Resources in Stopping Dengue Virus Transmission in Affected Households
|
The emergent behaviors of communities of genotypically identical cells cannot be easily predicted from the behaviors of individual cells . In many cases , it is thought that direct cell-cell communication plays a critical role in the transition from individual to community behaviors . In the unicellular photosynthetic cyanobacterium Synechocystis sp . PCC 6803 , individual cells exhibit light-directed motility ( “phototaxis” ) over surfaces , resulting in the emergence of dynamic spatial organization of multicellular communities . To probe this striking community behavior , we carried out time-lapse video microscopy coupled with quantitative analysis of single-cell dynamics under varying light conditions . These analyses suggest that cells secrete an extracellular substance that modifies the physical properties of the substrate , leading to enhanced motility and the ability for groups of cells to passively guide one another . We developed a biophysical model that demonstrates that this form of indirect , surface-based communication is sufficient to create distinct motile groups whose shape , velocity , and dynamics qualitatively match our experimental observations , even in the absence of direct cellular interactions or changes in single-cell behavior . Our computational analysis of the predicted community behavior , across a matrix of cellular concentrations and light biases , demonstrates that spatial patterning follows robust scaling laws and provides a useful resource for the generation of testable hypotheses regarding phototactic behavior . In addition , we predict that degradation of the surface modification may account for the secondary patterns occasionally observed after the initial formation of a community structure . Taken together , our modeling and experiments provide a framework to show that the emergent spatial organization of phototactic communities requires modification of the substrate , and this form of surface-based communication could provide insight into the behavior of a wide array of biological communities .
The collective migration and spatial organization of cellular communities are often the result of integration of chemical signals [1] , [2] , spatial cues , and epigenetic differentiation within the population [3] . In natural environments , microbes live in communities that range from relatively simple to very complex in terms of species diversity [4]–[10] , structure [11]–[13] , and metabolic functions and pathways [14]–[16] . In complex communities , such as medically relevant biofilms [17] , [18] , swarms of social bacteria such as Myxococcus xanthus [19] , [20] , or microbial mats in the environment [4]–[6] , community structure can be dynamic , involving the collective migration of cells in response to environmental cues [21] , [22] , and may depend on the production of an extracellular matrix , which can facilitate stabilization of spatial structure [23]–[27] . In the face of this complexity , mechanistic models of cellular interactions that recapitulate environmentally relevant community behaviors can enhance our understanding of structure-function relationships , particularly those that are at the interface of biological and physical phenomena . Since many of these interactions are dynamic and not easily amenable to standard genetic and molecular analysis , biophysical models built on experimental observations have the potential to make testable predictions by connecting cellular behaviors to community-scale architectures . One such example of community behavior is the directed surface-dependent motility of cyanobacteria either toward or away from a light source [28] , [29] . This phenomenon , known as phototaxis , is easily visualized in the unicellular cyanobacterium Synechocystis sp . PCC 6803 ( hereafter Synechocystis ) . In a typical phototaxis assay , cells spotted on a wet surface such as a low concentration agarose plate and placed in a directional light source begin to move in coordinated groups , followed by the formation of finger-like projections [30] , [31] . To explore the molecular underpinnings of this striking community behavior we have previously used a combination of forward and reverse genetics [32]–[34] . These approaches have revealed that motility requires Type IV pili ( TFP ) . TFP are multifunctional appendages found in many bacterial phyla and are required for surface-dependent motility , adhesion , and competence [35]–[38] . In addition , a number of photoreceptors [28] , [39] , [40] , surface proteins [41] , and signaling molecules such as cyclic AMP [41] appear to be involved in this highly regulated behavior . However , it remains unclear how single cells with a limited light bias [42] eventually organize into large groups of cells that exhibit predictable , coordinated phototactic behavior . To dissect this community behavior , we developed a minimal biophysical reaction-diffusion model based on our experimental observations in which cells undergo a light-biased random walk with motility dictated by the local concentration of a cell-secreted substance . Simulations based on this model recapitulate the wide range of observed motility patterns . Furthermore , exploration of the phase space of this model showed that varying the cell density , light bias , and persistence of the cell-dependent surface modification could tune the shape , dynamics , and steady-state speed of the community , consistent with experimental observations . Based on physical arguments and our computational modeling , we present heuristics for the scaling of these features that could apply to a broad class of motile , structured communities . We were also able to confirm key qualitative predictions of our model by performing experiments in which we systematically varied the initial cellular concentration of the community . Thus , the computational models developed in this study predict that the physical properties of cellular microenvironments play a critical role in regulating single-cell behavior and that these behaviors are transduced into community organization .
We used a well-established phototaxis assay in which a small volume of exponentially growing Synechocystis cells was spotted onto a low-concentration ( 0 . 4% ) agarose plate , which was subsequently placed in the path of a directional light-emitting diode ( LED ) light source and imaged using time-lapse microscopy ( Materials and Methods ) [41] , [42] . Typically , cells were initially randomly distributed across the surface and exhibited motility within 30 minutes after spotting . Within a 12–24 hour period many cells had migrated to the edge of the spot closest to the light , resulting in a typical crescent-shaped grouping of cells; next , a ruffled edge formed , indicating a transition in which cells begin to separate into spatially distinct groups . After 24 hours , long ( mm-scale ) , finger-like projections were formed in which the majority of the cells accumulated at the tip and the group moved in a nearly straight-line path toward the light source ( Fig . 1 ) . The spatially separated , finger-like projections were surrounded by an optical halo distinguished by a different index of refraction from the surface ( Fig . 2A inset ) . Moreover , cells at the front of a moving finger left behind a trail that was subsequently followed by other cells . This suggested that the material in the trail might have specific properties that affect cellular motility . To test this hypothesis , we reoriented the light direction by rotating the plate 90 degrees . The tips of the fingers , where the cell concentration was highest , reoriented and moved toward the new direction of the light source within a few minutes after rotating the plate ( Fig . 2B , C ) , indicating that the time scale of change in the direction of light bias was short compared to that of finger formation . Using custom tracking software to measure the instantaneous velocities of single cells in the fingertip ( Materials and Methods ) , we determined that the cells re-established their previous steady-state velocity distribution within approximately 5 minutes after turning ( Fig . 2C ) . When the cells in one finger encountered the trail left by cells in a neighboring finger , we observed two changes that indicated that the trail affected motility . First , cells in the merging finger sped up upon encountering the trail left by a neighboring finger: both the mean and width of the velocity distribution increased approximately three-fold , indicating a faster and less coordinated group of cells ( Fig . 2D ) . Second , the cells in the merging finger became more dispersed , indicating a reduction in the need for group coherence during movement . These observations indicate that trails left by cells locally enhance the motility of other cells , and groups of cells intersecting these trails can maintain their motility without maintaining the same levels of aggregation . Thus , our results suggest that cells secrete an extracellular substance that alters the agarose surface properties to increase motility . Although the composition and specific nature of this extracellular substance are unknown , we will refer to it as extracellular polymeric substance ( EPS ) , by analogy with other community-forming species [16] , [43] such as Myxococcus xanthus in which secreted substances play an important role in motility and group behaviors [44] . These observations of cell-mediated surface modification motivated the development of a biophysical model that could reveal the minimal requirements for finger formation . Our finger merging experiments indicated that the motility and coherence of cellular groups at the tips of Synechocystis fingers change on the time scale of minutes once cells encounter a pre-existing EPS trail , suggesting that cell movement is dependent on the local EPS concentration ( Fig . 3A ) . In contrast to previous models that rely on direct cell-cell communication or variable single-cell behavior to produce fingering patterns [30] , [31] , [45] , we hypothesized that the observed spatiotemporal community dynamics could be explained simply by cell-mediated physical alteration of the surface ( Fig . 3B ) . To test this prediction , we developed a reaction-diffusion model linking cellular and EPS concentrations to motility and the motion bias due to a directed light source . We assume that cells produce EPS at a constant rate ks such that ( 1 ) where is the EPS concentration at a point x on the two-dimensional surface at time t , and is the cellular concentration . Both concentrations are measured as the height that the EPS or cells would occupy if locally spread with a uniform thickness . In the absence of a directed light source , previous single-cell tracking experiments revealed that cells move in an approximately random walk fashion [42] . To incorporate our observations of EPS-mediated motility , we defined a phenomenological function M that is an effective diffusion constant that can vary in space and time; we assume that its spatiotemporal dependence is incorporated through the EPS concentration . By analogy with physics nomenclature , we term the cellular mobility , since it describes the ease with which cells move across a surface . Based on our experimental observations that cells exhibit increased movement at higher EPS levels , the mobility should be a monotonically increasing function of the EPS concentration , and we assume the mobility saturates at high levels of EPS . To limit the number of parameters in this function , we assume a simple functional form ( 2 ) where σ is the saturation depth above which additional EPS does not significantly increase the mobility , and m0 is the maximum mobility in the presence of saturating EPS . In the absence of a directed light source , we assume based on the observed , approximately random walk behavior that the cellular concentration spreads diffusively over time [42] . The effect of light is to bias the random walk toward the light source , and thus we model the cellular flux J as ( 3 ) where the first term on the right hand side corresponds to the flux from diffusive random motion of the cells , and the second term corresponds to the flux driven by a force vector b pointed toward the light source whose magnitude corresponds to the strength of the light bias . Since the distance to the light source is much larger than the cellular community even after fingering , we assume that b has constant magnitude and direction throughout the typical time scale of an experiment , except in simulations designed to mimic our finger merging experiments . The flux from Eq . 3 determines the dynamics of the local cellular concentration through ( 4 ) For simplicity , we ignore reproduction in all of the following simulations in order to define the phase space of community patterns in terms of a fixed total cellular mass ; we note that our model can easily be modified to account for nonzero rates of reproduction . For a light source incident from the y direction , with corresponding net motion also in the y direction and spatially varying mobility M , Eq . 4 becomes the biased diffusion equation ( 5 ) with nonlinear behavior emerging from feedback to the local EPS production rate through the spatially varying mobility M . With these assumptions , our model has four parameters: ks , m0 , σ , and the magnitude of the constant bias vector , . However , dimensional reduction reduces the number of free parameters without any loss in descriptive power . We define a natural time scale by , a natural length scale by , and a natural density scale for cell mass and EPS by and . The model is therefore reduced to two dimensionless parameters: ( i ) the mean initial cellular concentration , , normalized by the mobility saturation concentration σ , and ( ii ) the bias strength , β , normalized by . The model then takes a simpler form where variables marked with a tilde are understood to be dimensionless . The EPS concentration evolves according to ( 6 ) and relates to the mobility by , where is the normalized mobility . The cellular concentration field evolves according to ( 7 ) with and the spatial derivatives normalized by . This reduction of the parameter space to two free variables , and , dramatically simplifies the comprehensive mapping of system behavior without any loss of information or generality . In order to determine the conditions under which our model predicts phototactic fingering to occur and the types of spatiotemporal dynamics that are accessible , we explored the phase space of emergent behaviors by solving our reaction-diffusion model ( Eqs . 6 and 7 ) numerically for a wide range of values of and . Although the equations are deterministic , we introduce stochasticity by initiating each simulation with a random distribution of cells over a fixed , contiguous portion of the simulation area with a given mean value . Each simulation started with zero initial EPS concentration . To mimic a directed light source in the far field , a constant light bias was oriented toward the top of the rectangular simulation area ( Fig . 3A ) . For a moderate value of the mean cellular concentration and a bias force , our simulations recapitulated the initial gathering of cells at the front of a spot and the subsequent ruffled edge , and eventually developed distinct , finger-like projections similar to those observed in experiments ( Fig . 3B ) . To link our dimensionless parameters to the length and time scales exhibited by our experiments , we obtained estimates of the microscopic parameters and from the expansion of the EPS halo and single-cell movements , respectively ( Materials and Methods ) . This predicted a time scale for fingering of approximately 24 hours , in agreement with our experiments . In addition , the natural length scale , which defines the distance over which motion due to cellular diffusion is limited by the rate of EPS secretion , was in agreement with the characteristic widths of fingers in our experiments . Finally , our model reproduced the dynamic changes that we observed experimentally when one motile group encounters the EPS trail of a neighboring motile group ( Fig . 4 ) ; the concentration field of the incident motile group exhibited both the rapid increase in speed and group de-coherence ( Fig . 2B ) . These simulations of cellular and EPS concentrations exhibited morphological and dynamic properties that were amenable to quantification ( Fig . 5 ) . Simulated cell density images were computationally segmented to track the size ( measured in units of volume ) , position , and velocity of each distinct motile group ( finger ) over time , and the corresponding EPS concentration field was used to track when two nearby fingers merged . We also measured the time scale associated with the transition from a random distribution of cells to steady-state motion of motile groups toward the light source , which we refer to as the “ramp time . ” This comprehensive exploration allows us to map the phase space of possible behaviors , and determine scaling laws linking features such as finger speed to mean cellular concentration that provide insight into the physical consequences of motility feedback via surface modification . To quantify the extent of the region of parameter space for which our model produces morphologies relevant to the biological system , we used simulations to comprehensively map the space of possible community behaviors by varying the dimensionless total cellular biomass and the dimensionless light bias strength ( Fig . 6A; Materials and Methods ) . Within the range of parameters studied , a region emerged in which our model generated motile groups of varying sizes and speeds , with a wide range of time scales for the establishment of steady state motion . In addition , large subsets of simulations with other parameter values exhibited qualitatively different morphologies from the characteristic finger-like projections typically seen in experiments ( Fig . 6B ) . Given the complex range of behaviors represented in our simulations , we wrote custom software to quantify the community morphologies at every simulation time point ( Materials and Methods ) . Simulations were split into three classes according to the overall degree of cellular movement and the magnitude of non-uniformity of the advancing front , forming a phase diagram with dimensionless cellular concentration and bias force as the independent parameters ( Fig . 6A ) . For every pair of values and , we performed three simulations with different initial random distributions of cells . For all cases , the classification of resulting morphologies was consistent across initial conditions; finger speed and ramp time typically varied by only ∼5% and ∼10% , respectively . In the first class ( colored dots in Fig . 6A ) , cells had sufficient EPS production to become motile , and sufficient bias force to generate finger-like projections; the number and size of the motile groups varied depending on the parameters ( Fig . 6B , frames 1–4 ) . In the second class ( gray dots in Fig . 6A; Fig . 6B , frame 5 ) , the community was motile , but the high concentration of cells led to high levels of EPS production , causing the front to advance uniformly without splitting into distinct groups . In the third class ( black dots in Fig . 6A; Fig . 6B , frame 6 ) , the community was non-motile due to a relatively weak light bias and/or a low cellular concentration that was insufficient to produce enough EPS for movement over the time scale of the simulation . Therefore , our simulations predict that for a range of cellular concentrations and light biases outside the finger formation region , cells should exhibit uniform and/or non-motile fronts . For the subset of parameters that exhibited finger-like projections , we quantified motile group size , speed , and ramp time as a function of mean cellular concentration ( Fig . 7 ) to extract general rules that underlie community behavior . In addition , we used physical arguments to predict the scaling properties of each of these variables that could be compared with our numerical simulations . Based on our measurements of m0 and ks , the natural length scale , , sets the approximate width of a finger-like projection , independent of the cellular concentration or bias force . In our simulations mimicking a 2 mm wide region of the surface , we expected approximately 10 distinct finger-like projections , with some amount of random variation . Indeed , over the relevant region of phase space in Fig . 6A , the number of distinct motile groups ranged from 5–20 , with only a slight dependence on mean cellular concentration , which varied by more than three orders of magnitude in the simulations , and bias force , which varied by an order of magnitude . Thus , by linking cellular-scale properties ( m0 and ks ) to the patterning of community-scale motility , this physical argument successfully predicts that the number of fingers should remain relatively constant as the mean cellular concentration increases , while the number of cells in each finger increases approximately linearly ( Fig . 7A ) . In our simulations , each finger reaches a steady-state velocity that approximately scales as the mean cellular concentration to the 1/3 power ( Fig . 7B ) . This scaling can also be explained via the relationship between EPS production and cellular concentration . The rate at which a group of cells secretes EPS is proportional to the size of the group ( Eq . 6 ) . At steady state , this rate is balanced by the rate at which the front of cells in each group deposits EPS onto virgin substrate during the forward motion of the finger , which is proportional to ( i ) the depth of the EPS trail , ( ii ) the width of the trail as dictated by the finger width , and ( iii ) the forward velocity of the finger . If we assume that cells cluster at the tip of an individual finger in a shape that can be reasonably approximated by a hemisphere , then the width of the trail should scale as the number of cells to the 1/3 power . Taken together , the conservation of EPS secretion and deposition rates for a single finger at constant velocity dictates that ( 8 ) where N is the number of cells in the finger , d is the depth of the EPS trail , and is the steady-state velocity of the finger . For the low values of the dimensionless mean cellular concentration ( ) that produce finger-like projections , cellular mobility is linear in the depth of the EPS trail ( ) . Likewise , the velocity of a finger moving under light bias is proportional to the mobility ( Eq . 3; ) , and therefore the depth of the EPS trail is proportional to the velocity ( ) . In combination with Eq . 8 , this analysis gives ; since ( Fig . 7A ) , the predicted scaling relationship is , which is demonstrated empirically in Fig . 7B . For the initial , transient phase of our simulations , when cells begin to aggregate into distinct fingers , the ramp time is the time scale over which the fingers reach a terminal velocity . In this phase , the bias force leads small collections of cells to move forward with an approximately constant velocity as they travel over the EPS left by cells in front of them . The cells eventually collect at the leading edge of the initial cellular deposition , a behavior that mimics the crescent morphology observed experimentally in Figs . 1 and 3B . The rate at which cells accumulate at the leading edge is proportional to the velocity of these small groups of cells and the mean cellular concentration , such that the concentration of cells at the leading edge increases as ( 9 ) where is the concentration at the leading edge , is the constant velocity of cells heading toward the leading edge , and w is the relatively fixed width of the crescent zone . Thus , the concentration of cells at the leading edge scales roughly as , and hence the concentration of EPS at the leading edge scales quadratically in time as . The transient phase ends when enough EPS has been deposited at the leading edge such that a finger achieves terminal velocity , and therefore the ramp time to reach a fixed level of EPS should scale as . Our simulations predict a similar scaling ( Fig . 7C ) , indicating that a higher initial concentration of cells will lead to the faster development of fingers . These physical arguments indicate that the rate of finger development , number of cells in a finger , and finger speed are all positively related to mean cell concentration and to each other , at all relevant bias forces ( Fig . 7D ) . Similarly , increasing light bias is correlated with decreased ramp times , increased finger speeds , and a slight reduction in motile group size . To test these predictions , we performed experiments in which we systematically varied the initial cellular concentration and measured the community morphologies over time . As predicted by our model , the ramp time decreased and the number of cells in each finger increased with increasing initial cellular concentration ( Fig . 8 ) . While all experiments performed with sufficient initial cell number and in the presence of a directed light source showed the formation of distinct fingers at the front edge of the spot of cells , some experiments also displayed fingers forming within the deposition area ( Fig . 9A , inset ) . We hypothesized that a time-dependent decrease in the efficacy of EPS-enhanced mobility , as would be the case if the EPS decayed or relied on a volatile component , contributed to this separation between the internal fingers and the front edge . To test whether this mechanism could produce multiple fronts , we introduced time-dependent EPS decay into our model by ( 10 ) where is a dimensionless time constant such that S decreases according to in the absence of cellular production of EPS . For a given cellular concentration , the EPS concentration plateaus at a steady-state value . This limitation on the maximum value of the EPS concentration can cause a thresh-holding effect on the ability of a group of cells to move . If the relationship between mobility and EPS concentration , M ( S ) , were switch-like ( e . g . sigmoidal ) , cells in groups below a critical size would not be able to move because the steady-state EPS concentration would be too low . In contrast , in the absence of EPS decay , any group could move given enough time to produce sufficient levels of EPS . For the mobility function in Eq . 2 , it is similarly the case that in the presence of EPS decay , the maximum mobility is no longer determined by how long a group of cells produces EPS at a particular location , but instead by how many cells are present in the group . We performed simulations using our reaction-diffusion model with Eq . 6 modified to Eq . 10 , with randomly distributed initial densities and the same motility relationship used for Figs . 4–7 , at a fixed bias force of to ensure that the simulations encompassed a fingering region in Fig . 6A . We sampled values of the dimensionless time constant and mean cellular concentration to calculate a new phase diagram of system behaviors ( Fig . 9B ) ; these behaviors were divided into the same three classes depicted in Fig . 6A . For a wide range of cellular concentrations and EPS decay time constants , we found a region where cells split into distinct motile groups ( red dots in Fig . 9B ) . Within that region , longer time constants resulted in slower decay of EPS that led to a single unstable front ( Fig . 9C , frame 1 ) , similar to Fig . 6B , frames 1–4 . For shorter time constants , the initial mass of cells split into multiple non-uniform fronts , creating staggered motile groups ( Fig . 9C , frames 2–4 ) . We also identified a motile region without any fingering that resulted from high levels of EPS at higher cell concentrations ( gray dots in Fig . 9B and Fig . 9C , frame 5 ) . Finally , we identified a non-motile region caused by cell densities insufficient to sustain the EPS levels required for motility when decay times were short ( black dots in Fig . 9B and Fig . 9C , frame 6 ) . Interestingly , in simulations that resulted in multiple fronts , motile groups that formed later and hence lagged behind the most forward groups often advanced by following the transient EPS trails of earlier fingers . Upon catching up , the two groups coalesced to form a larger , faster moving group that even more easily followed other transient EPS trails ( Fig . 9C , inset of frame 2 ) . This resemblance to our experimental data ( Fig . 9A ) suggests that the EPS trail can both separate cells into distinct groups ( fingers ) within a front , and gather separated fronts of cells into a single front via this dynamic coarsening mechanism .
We have developed a minimal biophysical model of the phototactic motility of Synechocystis cells whose behavior is regulated by cell density and the strength of the bias created by a directed light source . Our finger merging experiments , which indicated that modification of the agarose surface increases cell motility ( Fig . 2 ) , suggest that the two major factors underpinning fingering pattern formation are a positive bias towards light exhibited by single cells in combination with modification of the substrate surface . The close agreement between our model of Synechocystis motility and experimental observations of finger formation and subsequent reorganization after the plate was rotated relative to the light direction ( Figs . 3 , 4 ) suggest that phototactic fingering patterns are a consequence of how the physical properties of the surface , which are dynamically remodeled by the local population of cells , affect cell motility; no change in single cell behavior or direct cell-cell communication is required . The community swarming behavior of the bacterium M . xanthus has many similar features to Synechocystis phototaxis , including TFP-dependent motility and central role of EPS [20] , [46] , [47] . Agent-based models have shown that maximal outflow of cells from a M . xanthus swarm relies on regular reversals of EPS-mediated gliding [48] and a flexible , rod-like cell shape [49] . By contrast , Synechocystis phototactic patterns form independent of a known reversal mechanism , and cells are spherical in shape . Moreover , light provides a unique switchable cue with which to manipulate behavior , for example by rapidly altering finger trajectories ( Fig . 2 ) , making Synechocystis an excellent system for probing general properties of community motility . Our model predicts the formation of distinct groups of cells ( fingers ) under a variety of light intensities ( bias ) and cell densities ( Fig . 6 ) . Finger volume is predicted to increase linearly with the cell density ( Fig . 7A ) ; in our simulations , larger fingers have a shorter ramp time ( Fig . 7C ) and collectively move more quickly toward the light with a speed that scales with the cell density ( Fig . 7B ) . In contrast with extremely low cell densities , these results indicate that fingers can move in a more directed fashion toward light and at speeds that single cells cannot achieve . These conclusions are even more accentuated in simulations with increased rates of EPS decay , in which the motility of an individual cell toward a light source may be negligible in comparison with a group that can maintain a high local density of EPS ( Fig . 9 ) . Therefore , the model predicts that under a wide range of combinations of light bias , cell concentration , and EPS decay conditions , cells that are part of a finger are likely to exhibit increased motility [30] . Given that our analysis is based on a dimensionless set of reaction-diffusion equations , we note that the emergent behaviors predicted by our model are general properties of any system that uses surface enhancement and biased diffusion for motility . Several of these predictions corroborate what we have observed empirically in the laboratory . Our model now provides a rigorous framework in which various predictions can be further tested and refined using specific mutants or defined conditions . For example , our studies predict that EPS released into the medium by cell cultures or collected from the surface of the agarose may be provided exogenously to alter motility in spatially dependent patterns , and our computational model provides a useful tool for predicting the effects of such exogenous EPS addition . Secreted EPS provides information about the concentration of cells that have recently resided at a particular location on the surface , a situation similar to chemical quorum sensing in which autoinducer molecules indicate the cell density of the population [50] . As Synechocystis groups develop , EPS trails provide a persistent mechanism of long-range , indirect communication that guides the coalescence of lagging groups with cells at the front of the drop . This dynamic coarsening of group size as cells move toward the light source is similar to water droplets on a window that follow the paths of previous droplets and coalesce to form larger water droplets that move even faster down their gravitational potential . Our model also suggests that EPS decay may play an important role in group motility by providing only a transient trail to guide other groups of cells that are farther from the light source ( Fig . 9 ) . While it may be advantageous to guide the groups immediately behind a leading group toward a light source , it is possible that changing environmental conditions such as light quality or direction may make it disadvantageous to have all cells exclusively follow the trail of cells at the leading edge . Our goal is to connect the microscopic cellular properties incorporated into our model with the macroscopic , observable behavior of cellular communities . Whereas previous models explicitly assumed that neighboring cells experience local interactions or that cells switch between discrete states associated with different behaviors [30] , [31] , [45] , our model seeks to reproduce the observed phototactic behavior using a minimal set of assumptions about the underlying factors controlling motility at the single-cell level . While our results do not rule out the possibility of chemical communication or changes in gene expression as contributors to the phototactic response of Synechocystis , our model provides a mechanistic explanation for finger formation that does not require these elements , and yet correctly predicts several trends in experimental data . In particular , we experimentally verified our model prediction that increasing the density of cells in the initial colony decreases the time required for finger formation and increases the subsequent finger size , with comparatively little variation in the number of fingers along the front of the spot ( Figs . 7 , 8 ) . Our use of a mean-field reaction-diffusion model assumes that the stochasticity of single-cell movement can be averaged over the population of cells; similar models have been applied to intracellular protein networks [51] , [52] and have also been used to describe a combination of phototaxis and chemotaxis observed in Dictyostelium slugs [53] , to model multiple competing cell populations [54] , to explain phototaxis in non-equilibrium chemical systems [55] , and to decipher the morphology of dendritic bacterial colonies [56] , [57] . In each case , these models clarified the important factors for generating a particular behavior by demonstrating the sufficiency of a subset of potential variables . Although discrete , agent-based models can be used to study the behavior of a group of cells [42] , [53] , [58] , we have focused on a continuous , mean-field model in order to map the behavior of an entire community across a large region of parameter space in a computationally tractable manner . It is relatively straightforward to introduce other factors into our model , including a diffusive chemical signal , cell-cell interactions mediated by Type-IV pili [35] , crowding , or EPS production levels [59] . Future experiments examining single-cell behaviors will also help to elucidate whether these factors and/or stochasticity in single-cell motility are manifested . Moreover , quantitative characterization of community morphologies as a function of light intensity , direction , and wavelength should provide a calibration for the effective strength of the light bias in different conditions . Finally , targeted mutants in the synthesis of extracellular polysaccharides and varied surface properties will provide the opportunity to tune community dynamics and test predictions of our model . The role of the local microenvironment in regulating both motility and the structure of the community can have a strong impact on a wide range of biological systems , including the migration of germ layer progenitor cells in the developing zebrafish embryo [60] and cancer cell metastasis [61] . The contribution of EPS to Synechocystis motility investigated in this study suggests that modification of surfaces as cells move across them may be an important parameter for understanding emergent community structure . The use of biophysical models to evaluate the physical basis of collective cell migration provides new avenues for further experiments and may underlie future efforts to control community behavior .
Synechocystis sp . PCC 6803 cells were grown from an original single colony of phototaxis-positive cells in BG-11 media [62] at 30°C with continuous shaking at 100 rpm under overhead warm white fluorescent light ( Super Saver Warm white F40WW/SS , 34W , Osram Sylvania Inc . , MA , USA ) . All imaging experiments were performed using exponentially growing cells with OD730 = 0 . 6–1 . 3 ( 25 , 000–55 , 000 cells/µL; measured with an Ultrospec 3100 pro spectrophotometer , Amersham Biosciences , Sweden ) . Motility assays were carried out on 0 . 4% ( w/v ) agarose in BG-11 in 50-mm plastic petri dishes ( BD Falcon , New Jersey , USA ) at 30°C . One microliter of cells ( OD730 = 0 . 8 , or ∼40 , 000 cells ) was placed in the center of a plate , and then inverted to minimize evaporation of the agarose . In Fig . 8 , cells were diluted with fresh BG-11 media and one microliter of cells from each dilution was placed on a plate . A warm white LED ( 5 mm , 7000 mcd , 35° spread; Super Bright LEDs , MI , USA ) was used to illuminate each plate . To induce directed phototaxis , the LED was placed 50 mm away from the center of the cell droplet , which was approximately 2 . 5 mm in diameter , at the level of the agarose . The incident light intensity was approximately 20 µmol photons/m2s , as measured with an LI-189 light meter ( LI-COR Biosciences , NE , USA ) . Entire drops ( Figs . 1 , 2 , 3 , 8 , and 9 ) were imaged using a Canon 60D DSLR camera ( Canon U . S . A . , Inc . , New York , USA ) attached to a Leica MZ12 stereoscope ( Leica Microsystems , IL , USA ) . To induce cells to move into an existing EPS trail ( Fig . 2 ) , a Petri dish containing cells that had been under directional light for 24 hours was rotated by 90° . Time-lapse imaging at single-cell resolution was conducted at 20× magnification , 1 frame/sec , and 30°C using a Coolsnap-Pro Monochrome camera ( Photometrics , Arizona , USA ) attached to a Nikon TE-300 inverted microscope ( Nikon Instruments Inc . , Melville , NY , USA ) . Cell tracking was performed using custom MATLAB ( The Mathworks , Natick , MA , USA ) software to quantify the positions and velocities of individual cells over time . In each frame , individual cells were segmented using thresh-holding and a watershed transform , and the locations of their centers of mass were recorded . The track of each cell was found using probabilistic nearest-neighbor connected-component analysis across frames . The average speeds of single cells were calculated from the total path length traveled over the preceding 50 seconds . Simulations were performed with custom code written in MATLAB . Simulations were carried out on a rectangular grid 2×4 mm in physical size , corresponding to 360×720 simulated grid elements with a grid spacing of 0 . 25 natural length scale units . In all instances , the simulation area was subject to zero flux boundary conditions and the EPS concentration was initially set to zero everywhere . The initial cellular mass was spread in a uniform random distribution over a region covering the bottom 20% of the simulation area . The cellular and EPS concentrations were calculated using a forward Euler method with a spatial and temporal resolution high enough for numerical stability . The time step ( dt = 0 . 01 ) was chosen to be small in comparison to the dimensionless time scale of the system set by the EPS production rate , and simulations were carried out for 600 , 000 s ( 300 dimensionless time units ) , or until motile groups reached the end of the physical simulation area . To compute phase diagrams , free parameters were sampled in log-base 2 across a large range to ensure that all possible classes of relevant behaviors were comprehensively explored . For each point in the phase space , three distinct random initial conditions were tested . We performed a total of 570 simulations for Fig . 6A and 504 simulations for Fig . 9B , varying initial biomass and bias force over the ranges shown . The EPS production rate ks and the maximum cellular mobility m0 set the fundamental length and time scales of the biophysical model . Both of these parameters were estimated from time-lapse imaging data . We assume that the optical halo around groups of cells is formed by the liquid ( EPS ) /air interface under which the cells reside . For regions where the width of the halo was small compared with the size of the EPS covered region , we assumed that the movement of this interface resulted from production of new EPS , and thus used this edge to approximate the area covered by EPS . We estimated the EPS production rate at by observing the rate of increase of this area over time , and by assuming that the EPS was approximately as thick as the 1-µm thick monolayer of cells from which it was secreted . We estimated the maximum cellular mobility at by measuring the root-mean-squared displacements of single cells over time in the absence of light and in a region where EPS had clearly been deposited , thereby assuring that cells could move relatively freely and in unbiased directions . Custom MATLAB software was written to quantify the morphological features of the simulations . Simulations in which less than 50% of the cell mass had moved into fresh territory by the end of the simulation were considered non-motile ( black dots in phase diagrams ) . For the majority subset that was motile , we used an adaptive threshold to determine the position of the moving cellular front as a function of time . We calculated the standard deviation of the points forming the cell front normalized by the mean distance that the cell front had moved , yielding a dimensionless metric of the growth rate of the morphological instability; a perfectly flat cell front would have a value of zero , and a highly unstable front with finger-like projections would have a value close to 1 . Simulations that showed an instability growth rate below 0 . 01 were considered to have a stable front without distinct groups ( gray dots in phase diagrams ) . Simulations with front instability >0 . 01 exhibited distinct cellular groups that moved toward the simulated light source . In Fig . 6A these points were colored according to their bias force , while in Fig . 9B these points were colored red since the bias force was fixed . For simulations without EPS decay ( Figs . 3–7 ) , the distinct motile groups ( if present ) were segmented to quantify their biomass ( measured as an integral from the beginning of the EPS trail ) , speed ( measured from the group center-of-mass ) , and ramp time .
|
Communities of bacterial cells exhibit social behaviors that single cells cannot engage in alone . These behaviors are often a product of direct interactions that allow cells to communicate with each other . In the unicellular photosynthetic cyanobacterium Synechocystis , groups of cells collectively move along a surface toward a light source in characteristic , spatial patterns that do not seem to require that cells directly communicate or change their individual behavior . By varying the direction of the light source , we show experimentally that cells indirectly interact by secreting a substance that allows them to move more rapidly and to follow the paths left by other cells . We develop a biophysical model demonstrating that this form of interaction is sufficient to reproduce our experimental observations , and complement this model with simulations and physical scaling laws that provide a useful tool to design and interpret future experiments . Based on our results , we propose that physical modification of the cellular microenvironment may play an important role in inducing group behaviors in other systems .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
Motility Enhancement through Surface Modification Is Sufficient for Cyanobacterial Community Organization during Phototaxis
|
Activation of group I metabotropic glutamate receptors ( subtypes mGluR1 and mGluR5 ) regulates neural activity in a variety of ways . In CA1 pyramidal neurons , activation of group I mGluRs eliminates the post-burst afterhyperpolarization ( AHP ) and produces an afterdepolarization ( ADP ) in its place . Here we show that upregulation of Cav2 . 3 R-type calcium channels is responsible for a component of the ADP lasting several hundred milliseconds . This medium-duration ADP is rapidly and reversibly induced by activation of mGluR5 and requires activation of phospholipase C ( PLC ) and release of calcium from internal stores . Effects of mGluR activation on subthreshold membrane potential changes are negligible but are large following action potential firing . Furthermore , the medium ADP exhibits a biphasic activity dependence consisting of short-term facilitation and longer-term inhibition . These findings suggest that mGluRs may dramatically alter the firing of CA1 pyramidal neurons via a complex , activity-dependent modulation of Cav2 . 3 R-type channels that are activated during spiking at physiologically relevant rates and patterns .
Metabotropic glutamate receptors ( mGluRs ) are a class of G-protein coupled receptors that may mediate a variety of effects through presynaptic and postsynaptic actions . Because these receptors are activated by glutamatergic neurons during network activity , they are in a position to regulate neural function in an activity-dependent manner . The effects of mGluR activation may be rapid or long-lasting , so they are important for short-term and long-term regulation of neural activity [1] . They have been implicated in physiological functions , such as learning [2]–[5] , as well as in a number of neurological disorders [6] , including mental retardation , epilepsy , and Alzheimer's disease [7]–[11] . Among the many effects of mGluR activation , the modulation of neuronal excitability has a direct effect on the response of cortical pyramidal neurons to excitatory synaptic input . The effects of mGluRs on excitability are commonly mediated by group I mGluRs , resulting in modulation of voltage-gated Na+ , Ca2+ , or K+ currents , as well as Ca2+-activated K+ currents , nonselective cation currents , or ion exchanger currents [1] . Modulation of these targets by group I mGluRs typically increases postsynaptic excitability [12] , [13] . Thus , group I mGluRs may modulate network function through modulation of multiple ion channels , resulting in enhanced excitability of glutamatergic pyramidal neurons . Activation of group I mGluRs in cortical and hippocampal pyramidal neurons has been reported to reduce the post-spike AHP and induce an ADP [14]–[18] , but the receptors , signal transduction mechanisms , and ion channels responsible for this effect are incompletely understood . Because of the importance of these modulatory effects for hippocampus-dependent functions and diseases , we studied the effects of activating group I mGluRs on the excitability of hippocampal CA1 pyramidal neurons . We report here that activation of these receptors results in enhanced activity of Cav2 . 3 R-type calcium channels , thus producing a medium ADP lasting a few hundred milliseconds . A slow ADP lasting for seconds is mediated by different mechanisms .
We obtained whole-cell current-clamp recordings from CA1 pyramidal neurons in rat hippocampal slices . The effects of the group I mGluR activation on responses to intracellular current injection were examined following bath application of the group I mGluR agonist DHPG ( 2–4 µM , see Materials and Methods ) . Step current injections ( 0 . 6-s long ) that were just above threshold for action potential firing in control elicited increased action potential firing in the presence of DHPG ( Figure 1A ) . In response to longer current injections ( 4 . 5 s ) , DHPG converted a simple pattern of spike-frequency accommodation to a more complex pattern consisting of a high-frequency burst of action potentials , followed by a period of silence , and finally a continuous train exhibiting spike-frequency accommodation . In response to noisy current injections , the number of action potentials was also increased when DHPG was applied ( Figure 1B and 1C ) . DHPG produced a small but statistically significant increase in the input resistance ( 54 . 7±3 . 0 MΩ for control , t = 0 min; 59 . 7±4 . 1 MΩ for DHPG , t = 15 min; n = 26 , paired t test , p<0 . 01 ) . This did not noticeably change the subthreshold response to noisy current injection , but enhanced spiking appeared to be attributable to a reduction in the post-spike AHP in the presence of DHPG ( Figure 1D ) . The effect of DHPG on the post-spike AHP was studied systematically by examining responses to bursts of action potentials evoked by five brief ( 2 nA , 2 ms ) current injections . In normal artificial cerebrospinal fluid ( ACSF ) , a 100 Hz burst of five spikes was followed by an AHP ( −3 . 1±0 . 2 mV ) that reached a peak at 59±5 ms after the last spike . Application of DHPG ( or quisqualate , another group I mGluR agonist , Figure S1 ) eliminated the AHP , resulting in a post-burst ADP ( +18 . 7±0 . 7 mV ) that reached a peak at 34±3 ms after the last spike and decayed to 25% of the peak value in 200±23 ms ( n = 20 , Figure 1E ) . We refer to this ADP as a medium ADP to distinguish it from the fast ADP following a single spike in normal ACSF [19]–[23] and a slow DHPG-induced ADP described later in this article . Functionally , the change from post-burst AHP into a post-burst medium ADP made the pyramidal neurons more excitable; current injections that were subthreshold during the post-burst AHP evoked action potential firing during the medium ADP ( Figure 1F ) . Application of DHPG resulted in a gradual reduction of the AHP and conversion to an ADP . The medium ADP reached its maximum value after several minutes in DHPG and was fully reversible , with a similar time course , upon washout of DHPG ( Figure S2 ) ; however , the slow onset and reversal of the medium ADP is attributable to the slow speed of the perfusion system , as rapid application of DHPG produced more rapid responses ( see below ) . In all experiments , the amplitude of the post-burst potential ( AHP or medium ADP ) was quantified at a fixed time , corresponding to the peak of the AHP in normal ACSF ( 59±5 ms after the last spike ) . For some analyses , the effect of DHPG was quantified by the change in the post-burst potential ( Δ post-burst potential ) at this time point in the response ( see Figure S3 ) . This measure required comparison of the response in normal ACSF ( AHP at t = 0 min ) to the response at a fixed time after application of DHPG ( e . g . , ADP at t = 15 min ) . Importantly , this measure was not complicated by any slow , drug-independent effects , as the post-burst AHP was stable for typical recording duration in normal ACSF ( Figure S3 ) . To determine which group I mGluR subtype was responsible for the DHPG-induced medium ADP , we used the subtype-selective antagonists LY367385 ( 25 µM , mGluR1 antagonist ) and MPEP ( 10 µM , mGluR5 antagonist ) . LY367385 blocked the medium ADP ( t = 15 min ) by 16% , while MPEP blocked it by 73% ( Figure 2A ) , suggesting that the effect of DHPG is mediated primarily by mGluR5 and partially by mGluR1 . When applied together , the two drugs blocked the medium ADP by 88% ( Figure 2A ) , suggesting that the effects of mGluR1 and mGluR5 are approximately additive . To determine the subcellular localization of the mGluRs mediating the medium ADP , DHPG was applied locally , by pressure application from a large patch pipette , to either the perisomatic region or the apical dendrites ( see Materials and Methods for details ) . Application of DHPG ( 500 µM in application pipette ) to the apical dendrites had no effect , while perisomatic application produced a medium ADP ( Figure 2B ) . Similarly , when DHPG was present in the bath , application of normal ACSF reduced the medium ADP when applied to the soma , but not when applied to the apical dendrites ( Figure 2C ) . The effects of DHPG were induced within seconds of its application and reversed rapidly when the DHPG application was terminated . Together , these results suggest that perisomatic mGluRs must be activated to elicit an medium ADP in response to somatic action potential firing and that the effect is rapidly induced and reversed . The lack of effect with application to the dendrites does not necessarily imply the absence of group I mGluRs , as backpropagating action potentials could have a different effect from action potentials in the soma ( see Discussion ) . To determine whether group I mGluR-mediated modulation of the post-burst potential could occur in response to synaptically released glutamate , the post-burst AHP/ADP was compared during control conditions and during high-frequency ( 50 Hz ) activation of Schaffer collaterals . Fast synaptic responses were prevented by blocking glutamate and GABA receptors ( see Materials and Methods ) . Under control conditions , each burst was followed by an AHP . During synaptic stimulation , however , each burst was followed instead by an ADP ( Figure 3A ) . To determine the role of group I mGluR activation in the induction of the post-burst ADP , we applied blockers of mGluR1 and mGluR5 ( Figure 3B and 3C ) . The results of these experiments were compared to a separate group of control experiments occurring over the same time course but without blocker application . In the absence of synaptic stimulation , the post-burst AHP was stable over time , both in control and in the presence of group I mGluR blockers ( Figure S4 ) . In the presence of synaptic stimulation , the size of the ADP in the control group gradually increased over the course of the experiment ( 156%±12% of initial value , n = 5 ) . By contrast , blocking the group I mGluRs resulted in a decrease in the size of the ADP ( 66%±7% of initial value , n = 5 ) . The magnitude of the ADP at the end of the experiment ( 60 min ) in the presence of group I mGluR blockers was 42% of control , consistent with a substantial contribution of these receptors to induction of the ADP triggered by synaptically released glutamate . The long-term effects of synaptic stimulation on the ADP are interesting but are not considered here . Instead , our focus is on the acute modulation of the AHP/ADP during activation of group I mGluRs . Bath application of DHPG increased the fast ADP following a single spike , and the size of the medium ADP increased with both the number and the frequency of action potentials , reaching medium ADP values of nearly 15 mV for 5 spikes at 100 Hz ( Figure S3 ) . Longer trains ( 20 or 50 spikes ) did not increase the ADP further and in fact resulted in a decrease in the size of the ADP ( Figure S5 ) , perhaps due to inactivation of the ADP-producing current or enhanced activation of an AHP-producing current . The medium ADP following a burst of spikes evoked by a step current injection was blocked by application of tetrodotoxin ( TTX; 0 . 5 µM ) to eliminate Na+-mediated spikes; however , increasing the magnitude of the current injection to elicit a Ca2+ spike [24] restored the medium ADP ( Figure 4A and 4B ) . Under these conditions , the amplitude of the medium ADP increased with the magnitude and duration of the current injection , reaching a maximum of about 10–15 mV for current injections of at least 1 . 4 nA for 40 ms ( Figure 4C and 4D ) . The requirement for action potential firing or a Ca2+ spike suggests that the medium ADP may require Ca2+ entry through voltage-gated calcium channels ( VGCCs ) . Consistent with this idea , we found that the medium ADP was eliminated by switching to a Ca2+-free ACSF ( Figure 5A ) or by bath application of micromolar concentrations of NiCl2 ( Figure 5B–E; IC50 = 23 µM ) . Nimodipine ( 10 µM , an L-type calcium channel blocker ) did not block the medium ADP ( Figure 5B–D ) . To test whether the medium ADP required elevation of internal Ca2+ concentration , we performed experiments with patch-clamp electrodes containing BAPTA ( 10 mM ) and found that this strongly reduced the medium ADP ( Figure S6 ) . Blocking Ca2+ release from internal stores with cyclopiazonic acid ( CPA; 20 µM ) also reduced the medium ADP ( Figure S6 ) , suggesting that this is another important mechanism for induction of the medium ADP . The requirement for Ca2+ entry through VGCCs and the elevation of internal Ca2+ concentration is consistent with two different models of the medium ADP . In the first model , the medium ADP is mediated by Ni2+-sensitive VGCCs , with their modulation ( enhanced activity ) by DHPG requiring elevated intracellular Ca2+ . In the second model , Ca2+ entry through VGCCs contributes little to the medium ADP directly but acts as a trigger for a downstream conductance that is modulated by DHPG . For example , Ca2+ entry and release from internal stores could activate nonselective cation currents , such as ICAN , which mediate the medium ADP . Alternatively ( or in addition ) , downregulation of Ca2+-activated K+ channels by DHPG could unveil the medium ADP . To distinguish between these possibilities , we examined the voltage dependence of the medium ADP . We reasoned that in the first model , where the medium ADP is mediated by VGCCs directly , hyperpolarization should accelerate deactivation of the VGCCs , thus reducing the medium ADP . In the second model , hyperpolarization would not eliminate Ca2+ entry during the action potentials , and it would increase the driving force on the cation channels , thus increasing the medium ADP . We found that holding the cell at a hyperpolarized holding potential strongly reduced the amplitude and duration of the medium ADP ( Figure 6A and 6B ) , a finding most consistent with the first model , in which the medium ADP is mediated by VGCCs directly . However , the slowest component of the medium ADP was not reduced by hyperpolarization ( Figure 6A ) , suggesting a contribution of Ca2+-activated channels to a slow ADP ( see below ) . To further examine the voltage dependence of the medium ADP , we delivered short hyperpolarizing current injections ( −6 nA , 2 ms ) either 8 or 48 ms after the last action potential in a burst ( Figure 6C ) . On its own , the brief current injections produced a hyperpolarization to about −120 mV ( see Materials and Methods ) and returned to rest in 46±2 ms ( n = 12 ) . Hyperpolarization at the early time point reduced the medium ADP amplitude more than expected by simply summing the medium ADP and the short hyperpolarization alone ( Figure 6D and 6E ) , again consistent with the notion that the medium ADP is mediated by a voltage-dependent conductance that can be deactivated by hyperpolarization . This effect was measured at the time when the hyperpolarizing response on its own decayed back to rest ( 54±2 ms after the last spike ) . When the hyperpolarizing current step began 48 ms after the last action potential , its effect ( again measured when the hyperpolarization on its own decayed to rest; 100±2 ms after the last spike ) was not statistically significant ( Figure 6D and 6E ) . This finding suggests that the VGCCs responsible for the medium ADP remain activated for at least 50 ms , but not longer than 100 ms , while the conductance responsible for the slow ADP is not deactivated by hyperpolarization . The voltage dependence of the medium ADP , combined with its sensitivity to low concentrations of Ni2+ , suggests that the medium ADP may result from upregulation of Cav2 . 3 R-type calcium channels . However , previous work has indicated that mGluR activation can downregulate K+ channels [17] , [18] , [25]–[28] , which could result in inhibition of the AHP and activation of R-type channels , without any actual modulation of the calcium channels by mGluRs . To test this alternative hypothesis , we converted the medium AHP to a medium ADP by injecting a ramp current that followed action potential firing , resulting in an artificial medium ADP in control ACSF , which resembled the medium ADP following application of DHPG . This artificial medium ADP was unaffected by bath application of Ni2+ , suggesting that the Ni2+-sensitive calcium channels are not significantly activated by a post-burst medium ADP in the absence of mGluR activation ( Figure 7A and 7B ) . We also tested the voltage dependence of the medium ADP in the absence of DHPG by varying the initial amplitude of the ramp current . The relationship between the medium ADP amplitude and the current injection was linear ( Figure 7C and 7D ) , suggesting that voltage-dependent conductances do not amplify the medium ADP over this voltage range in the absence of mGluR activation . To further test the hypothesis that upregulation of R-type calcium channels is responsible for the DHPG-induced medium ADP , we performed experiments on Cav2 . 3 knockout mice [29] . The medium ADP induced by DHPG was significantly smaller in the knockout mice , compared to wild-type controls ( Figure 8 ) . Because the Cav2 . 3 knockout mice are a hybrid of C57BL/6J ( black ) and 129S1/SvImJ ( brown ) mice , pups had different coat colors ( black , dark brown , light brown; see Materials and Methods ) . We analyzed the results from different-colored mice separately and found no differences between these groups . Furthermore , we performed control experiments using black and brown mice and found similar DHPG-induced medium ADP amplitude in each of the wild-type strains ( Figure S7 ) . We also used voltage-clamp recording to measure isolated R-type calcium currents in CA1 pyramidal neurons ( see Materials and Methods ) . The resulting currents were upregulated by DHPG when activated by large but not small depolarizing steps ( Figure 9A and 9B ) , consistent with modulation of R-type ( high voltage activated ) but not T-type ( low voltage activated ) calcium currents . Finally , to explore the ability of mGluR5 activation to regulate current mediated by calcium channels , we co-expressed , in Xenopus oocytes , mGluR5 along with either Cav2 . 3 α1 ( plus α2δ1 and β3 ) or Cav3 . 2 α1 ( see Materials and Methods; [30] ) . We found that DHPG application upregulated barium currents in oocytes expressing mGluR5 and Cav2 . 3 but not mGluR5 and Cav3 . 2 , suggesting subunit selective modulation of R-type , but not T-type , calcium channels by mGluR5 activation with DHPG ( Figure 9C and 9D ) . The observation that the slow ADP was not eliminated by hyperpolarization suggests that a different class of channels may contribute to the slow ADP . We therefore examined this component of the ADP pharmacologically . It was not blocked by NiCl2 ( Figure S8A and S8B ) , consistent with the idea that it is not mediated by the VGCCs responsible for the medium ADP . The slow ADP was also unaffected by nimodipine ( Figure S8A and S8B ) . We performed a battery of pharmacological experiments to explore the signal transduction mechanisms responsible for the post-burst medium ADP and slow ADP . These experiments ( Figure S8C and S8D ) required either intracellular drug application or pre-incubation of the drug in the bath . Like the medium ADP , the slow ADP was blocked by group I mGluR antagonists , especially the mGluR5 blocker MPEP . The slow ADP was also reduced by intracellular BAPTA , but it was not blocked by drugs that interfere with Ca2+ release from intracellular stores ( CPA , ruthenium red , or heparin ) . All of these drugs inhibited the medium ADP , suggesting the Ca2+ release is required for the medium ADP , but not the slow ADP . The medium ADP was blocked by intracellular GDP-β-S , which interferes with G-protein coupled signaling , or by the PLC inhibitor U73122 ( but not the inactive analog U73343 ) . None of these drugs blocked the slow ADP , however , suggesting further that distinct signaling mechanisms mediate the DHPG-induced medium ADP and slow ADP . We examined the activity dependence of the DHPG-induced post-burst medium ADP by delivering pairs of bursts at intervals of 0 . 1 to 20 s . Three-spike bursts were used in order to limit the size of the medium ADP so that either facilitation or inhibition could be observed . At intervals up to 200 ms , the second burst evoked a medium ADP almost twice the size of the first; at intervals of 1–5 s , the second burst was reduced by about 25% ( Figure 10 ) . The data were well fit by a model consisting of two processes: a facilitation process with a decay time constant of 0 . 25 s and an inhibition process with a decay time constant of 10 s ( Figure S9 and Text S1 ) . In the model , inhibition affected the fraction of the current available to be activated and facilitation affected the probability of activation ( by a burst ) of the available current . A key feature of the model was that any portion of the current could be inhibited , regardless of whether or not it was activated . This model predicted that facilitation is not expected , but inhibition persists , for a third burst delivered following two bursts ( Figure S9 ) . We conducted this experiment and the results were consistent with the predictions of the model ( Figure 10 ) . By contrast , a model in which inactivation was limited to the activated channels could not explain the results of the three-pulse experiment ( Figure S9 and Text S1 ) .
The findings reported here suggest that activation of group I mGluRs , which can occur as a result of synaptically released glutamate , increases the excitability of CA1 pyramidal neurons primarily by converting the post-spike AHP to an ADP via group I mGluR-mediated upregulation of Cav2 . 3 R-type calcium channels . The largest component of this change is a medium ADP lasting a little over 200 ms . A longer-lasting slow ADP ( seconds ) was smaller and mediated by different ion channels and signal transduction pathways than the medium ADP . The medium ADP required action potential firing , although calcium spikes also activated the medium ADP in DHPG . The medium ADP was not affected by blocking L-type VGCCs with nimodipine , but calcium entry through Ni2+-sensitive channels was required for the medium ADP , as was intracellular Ca2+ elevation and Ca2+ release from internal stores . Block of the medium ADP by micromolar Ni2+ and the strong reduction of mGluR-mediated modulation of the post-burst potential in Cav2 . 3 knockout mice suggest that activation of R-type VGCCs are required for conversion of the medium AHP to a medium ADP [31]–[33] . Although it is difficult to distinguish between direct and indirect contributions of these channels , the voltage sensitivity of the medium ADP—including inhibition of the medium ADP by hyperpolarization after the triggering action potentials—suggests that R-type VGCCs contribute directly to the medium ADP . Our voltage-clamp experiments suggest the existence of an R-type Ca2+ current even prior to activation of mGluRs . The presence of an AHP under control conditions suggests , however , that K+ currents are larger than Ca2+ currents . Activation of mGluRs may downregulate K+ currents , but this downregulation is not sufficient to explain a voltage-dependent ADP as indicated by the ramp current experiments ( Figure 7 ) , which show that upregulation of R-type Ca2+ current is required to produce the ADP . We cannot rule out the possibility that Ca2+ entry activates a voltage-dependent cation current underlying the medium ADP . Indeed , a number of reports implicate the activation of cation currents following group I mGluR activation in hippocampal neurons [34]–[39] . Some of these currents are Ca2+ sensitive , some are voltage sensitive , and others are both Ca2+ and voltage sensitive . Expression of these currents varies between CA3 and CA1 pyramidal neurons [40] . All of these currents are slower than the medium ADP reported here . Furthermore , none of these currents have been reported as sensitive to micromolar concentrations of Ni2+ . Thus , the most parsimonious explanation of our data is that DHPG upregulates Ni2+-sensitive R-type VGCCs , which remain active for about 100 ms following a burst of spikes . At least some of the slow , group I mGluR-activated currents described previously may be responsible for the slow ADP we observed here . Other candidate mechanisms are inhibition of slow K+ currents , including Ca2+-activated K+ currents , which have been reported in hippocampal neurons [26]–[28] , [35] , [41]–[43] , and activation of exchanger currents [1] . These effects may also contribute somewhat to the medium ADP , as DHPG induced a small medium ADP even at very negative holding potentials , where activation of VGCCs is limited . At normal ( i . e . , resting ) membrane potentials , inhibition of K+ currents could enhance the contribution of VGCCs . If activation of R-type Ca2+ channels were the only requirement for the medium ADP , we would not expect its inhibition by chelating intracellular Ca2+ or interfering with Ca2+ release from internal stores . Indeed , either of these findings could be presented in support of a Ca2+-activated cation current as the primary mechanism . However , it is possible that intracellular Ca2+ elevation is required for modulation of the VGCCs following activation of mGluRs by DHPG . Indeed , the pharmacology suggests that the medium ADP requires activation of mGluR5 ( and to a lesser extent mGluR1 ) and activation of G proteins ( likely Gq ) and PLC . This pathway can lead to several other signal transduction events , including Ca2+ release via activation of IP3 receptors . Several previous studies have shown that activation of group I mGluRs triggers Ca2+ release from internal stores in hippocampal neurons [26] , [36] , [44]–[48] . In most cases the IP3 receptor is the primary mediator of Ca2+ release; however , our finding that the medium ADP is inhibited by heparin and ruthenium red suggests that both IP3 receptors and Ca2+-induced Ca2+ release via ryanodine receptors are involved ( Figure S8D ) [49] . The consequences of this Ca2+ release are unknown , but we postulate it is an essential step in the complex cascade of signal transduction events that ultimately result in modulation of the Cav2 . 3 subunits responsible for the medium ADP . Our finding that both mGluR5 and mGluR1 activation are required for the full effect of DHPG is consistent with previous work demonstrating the effects of both receptor subtypes in CA1 pyramidal neurons [26] , [41] , [44] , despite greater expression of the mGluR5 subunit [50]–[55] . PLC-dependent and PLC-independent effects have been reported [10] , [16] , [18] , and tyrosine phosphatases have also been implicated in mediating the effects of group I mGluR activation [17] . Clearly , more work is required to elucidate all of the pathways involved in activation of the medium ADP . Even more work will be needed to uncover the transduction mechanisms of the slow ADP . One possible mechanism is a current similar to the inward current mediated by mGluR1 activation previously described in CA3 pyramidal neurons , which was not dependent on G-protein activation ( like the slow ADP reported here ) but required activation of a Src-family tyrosine kinase [39] . Although bath application of DHPG produced a gradual onset of the medium ADP , it appeared more rapidly ( <3 s ) when DHPG was applied by pressure application . This suggests that the signal transduction pathways can be activated and reversed very rapidly , an observation that has been used to suggest a membrane delimited mechanism [1] . The block of the medium ADP by chelating intracellular Ca2+ or interfering with Ca2+ release suggests , however , that membrane delimited signaling alone may be insufficient . The lack of effect of DHPG when applied to the apical dendrites should be interpreted with caution . Although it is tempting to conclude that the relevant mGluRs may have a perisomatic location , it is also possible that direct activation of the dendrites ( e . g . , synaptic activation or dendritic Ca2+ spikes ) could lead to a medium ADP when dendritic mGluRs are activated . More work is needed to determine the distribution of mGluR1 and mGluR5 in CA1 neurons and their physiological effects when activated in various cellular compartments . An intriguing aspect of the medium ADP is its activity dependence . It was markedly enhanced when pairs of bursts were delivered at intervals of less than 1 s but suppressed during pairs of bursts at longer intervals or when triplets of bursts were delivered to the neurons . Similarly , while short bursts of action potentials produced an ADP , longer trains of spikes did not produce an ADP . The molecular steps responsible for these activity-dependent effects are unknown , but the data using pairs and triplets of bursts were well described by a model consisting of a short-lasting facilitation of unactivated channels and a longer-lasting inhibition of a fraction of all of the channels , independent of activation . These interesting properties may offer a clue to identification of the underlying currents in future voltage-clamp experiments . Identifying the contribution of mGluR activation to neuronal excitability in vivo will be a crucial step for ultimately establishing the importance of this mechanism for hippocampal function . Accomplishing this task will require that the balance of two competing factors be determined: the enhanced activation of mGluRs during periods of high activity and the activity-dependent inhibition of the ADP during high-frequency spiking . In general , identifying the underlying conductances , their possible molecular composition , and the signal-transduction steps and molecular players involved in their activation and modulation will be critical for determining how excitability is regulated via changes in the AHP/ADP in vivo . This knowledge would facilitate the use of molecular genetics to study the effects of these mechanisms on hippocampal function in vivo with single-unit recordings and behavioral analysis .
Hippocampal slices were prepared from male Wistar rats 25–45 d old or mice ( C57BL/6J or 129S1/SvImJ or Cav2 . 3 knockout ) 21–35 d old . Voltage-clamp experiments were done on slices prepared from younger rats ( 13–18 d old ) in order to reduce space-clamp problems . Knockout mice were derived from 129S1/SvImJ ( brown mouse ) embryonic stem-cell injections in C57BL/6J ( black ) mice [29] . The founder mice were bred to C57BL/6J females; therefore , offspring of the knockout mice had either light brown , dark brown , or black coat color . Following anesthesia with halothane or isoflurane , animals were perfused through the heart with ice-cold ACSF ( see below ) . The brain was removed rapidly and mounted in a near-horizontal plane for preparation of 300 µm hippocampal slices using a vibratome . Slices were prepared in either ice-cold ACSF or sucrose-based solution , then transferred to a chamber containing oxygenated ACSF ( no sucrose ) at approximately 35°C for half an hour . The slice chamber was then maintained at room temperature and slices were removed individually for electrophysiological recordings . Whole-cell current-clamp recordings were obtained at 33±2°C . Patch-clamp electrodes were pulled from 2 . 0 mm outer diameter borosilicate glass and filled with a K-gluconate-based intracellular solution ( see below ) . Electrode resistance was 3–6 MΩ in the bath and series resistance was 5–20 MΩ during the recordings . Current-clamp recordings were obtained with Dagan BVC-700 amplifiers , using appropriate bridge balance and electrode-capacitance compensation . Voltage-clamp recordings with appropriate capacitance and series resistance compensation were performed at room temperature ( 23–25°C ) and monitored with an Axopatch 200B amplifier ( Molecular Devices , Union City , CA ) . Data acquisition and analysis was performed using custom software written for Igor Pro . Statistical tests included the paired or unpaired t test and analysis of variance ( one-way ANOVA or repeated measures one-way ANOVA ) with Tukey's post hoc comparisons . All statistical analyses were performed using Prism 4 software and in most cases detailed results are provided in the figure legends . For the hyperpolarizations shown in Figure 6 , the −6 nA , 2 ms current steps produced hyperpolarizations that briefly ( 1–8 ms ) exceeded the −10 V limit of the analog-to-digital converter . The peak hyperpolarization was therefore estimated by extrapolating double-exponential fits of the response to the peak time predicted from linear fits of the rising phase . The estimated peaks were −119±3 mV for the early steps ( n = 12 ) and −117±2 mV for the late steps ( n = 11 ) . The small clipping effect had an insignificant effect on the subtraction procedure used ( see Results ) , because the effect is measured when the hyperpolarization had decayed back to rest , more than 40 ms after the clipping ended . To test whether synaptic activation can induce the post-burst ADP , 5 brief action potentials were somatically injected either with or without synaptic stimulation and the responses were monitored once every 5 min with 1 min delay between two conditions . In both the MPEP/LY and control groups , experiments were performed in the presence of blockers of ionotropic glutamate receptors ( 30 µM CNQX and 50 µM D-AP5 ) and GABA receptors ( 2 µM SR95531 and 1 µM CGP55845 ) . Bipolar borosilicate theta glass stimulation electrodes ( Sutter Instruments ) filled with ACSF were used in conjunction with Dagan BSI-950 biphasic stimulus isolator . Stimulating electrodes were placed in proximal stratum radiatum and at least 100 µm away from the recorded cell and toward CA3 . Stimulus intensity was set to produce a 5–11 mV ADP during synaptic stimulation . Normal ACSF had the following composition ( mM ) : 125 NaCl , 2 . 5 KCl , 25 NaHCO3 , 1 . 25 NaH2PO4 , 1 MgCl2 , 2 CaCl2 , 25 Dextrose ( Fisher Scientific; Sigma ) . In some cases slices were prepared in a modified ACSF in which 125 mM NaCl was replaced with 75 mM NaCl and 75 mM sucrose . In many experiments , drugs were added to the bath ( see below ) . The bath perfusion rate was 2–3 ml/min . The intracellular recording solution had the following composition ( mM ) : 115 K-gluconate , 20 KCl , 10 Na2phosphocreatine , 10 HEPES , 2 MgATP , 0 . 3 NaGTP , 0 . 1% Biocytin ( Fisher Scientific; Sigma ) . In some experiments , drugs were added to the intracellular solution ( BAPTA , GDP-β-S , U73122 , U73343 , ruthenium red , and heparin; see below ) ; for BAPTA-containing internal solution , the K-gluconate concentration was reduced to 100 mM . The K-gluconate based internal solution was used because the properties of CA1 pyramidal neurons are more stable with this solution than with K-Methylsulfate based solutions [56] . For voltage-clamp experiments in slices , patch electrodes ( 3–6 MΩ in bath ) were filled with intracellular solution containing the following ( in mM ) : 110 Cs-gluconate , 25 TEA-Cl , 10 HEPES , 2 EGTA , 4 Mg-ATP , and 0 . 5 Na-GTP , 5 Na2-phosphocreatine , pH 7 . 3 with CsOH . R- and T-type calcium currents were isolated pharmacologically by preincubating the slices in a mixture containing ω-conotoxin MVIIC ( 2 µM ) , ω-conotoxin-GVIA ( 2 µM ) , and ω-agatoxin IVA ( 0 . 2 µM ) to block N- , P- , and Q-type Ca2+ currents and cytochrome c ( 0 . 1 mg/ml to block nonspecific toxin binding ) for>1 h at room temperature . Nifedipine ( 20 µM ) and TTX ( 1 µM ) were bath applied to block L-type Ca2+ currents and Na+ currents , respectively . Recordings were performed in modified ACSF solution containing the following ( in mM ) : 125 NaCl , 25 NaHCO3 , 2 . 5 KCl , 1 . 0 MgCl2 , 2 . 0 CaCl2 , 1 . 25 NaH2PO4 , 25 NaHCO3 , and 10 dextrose , 2 CsCl , 5 4-AP , 10 TEA-Cl , pH 7 . 4 . The following drugs were obtained from Tocris: ( S ) -3 , 5-Dihydroxyphenylglycine ( DHPG ) , ( S ) - ( + ) -a-Amino-4-carboxy-2-methylbenzeneacetic acid ( LY367385 ) , 2-Methyl-6- ( phenylethynyl ) pyridine hydrochloride ( MPEP ) , Ammoniated ruthenium oxychloride ( Ruthenium Red ) , 1 , 4-Dihydro-2 , 6-dimethyl-4- ( 3-nitrophenyl ) -3 , 5-pyridine dicarboxylic acid 2-methyloxyethyl 1-methylethyl ester ( Nimodipine ) , ( 6aR , 11aS , 11bR ) -rel-10-Acetyl-2 , 6 , 6a , 7 , 11a , 11b-hexahydr o-7 , 7-dimethyl-9H-pyrrolo[1′ , 2′:2 , 3]isoindolo[4 , 5 , 6-cd] indol-9-one ( CPA ) , D- ( − ) -2-Amino-5-phosphonopentanoic acid ( D-AP5 ) , 6-Cyano-7-nitroquinoxaline-2 , 3-dione disodium ( CNQX disodium salt ) , ( 2S ) -3-[[ ( 1S ) -1- ( 3 , 4-Dichlorophenyl ) ethyl]amino-2-hydro xypropyl] ( phenylmethyl ) phosphinic acid hydrochloride ( CGP 55845 hydrochloride ) , and Octahydro-12- ( hydroxymethyl ) -2-imino-5 , 9∶7 , 10a-dimethan o-10aH-[1] , [3]dioxocino[6 , 5-d]pyrimidine-4 , 7 , 10 , 11 , 12-pen tol citrate ( Tetrodotoxin citrate ) . The following drugs were obtained from Sigma: 1 , 2-Bis ( 2-aminophenoxy ) ethane-N , N , N' , N'-tetraacetic acid tetrapotassium salt ( BAPTA ) , 1-[6-[ ( ( 17β ) -3-Methoxyestra-1 , 3 , 5[10]-trien-17-yl ) amino]hexyl]-1H-pyrrole-2 , 5-dione ( U73122 ) , 1-[6-[ ( ( 17β ) -3-Methoxyestra-1 , 3 , 5[10]-trien-17-yl ) amino]hexyl]-2 , 5-pyrrolidinedione ( Uf73343 ) , Guanosine 5′-[β-thio]diphosphate trilithium salt ( GDP-β-S ) , Heparin sodium salt ( from porcine intestinal mucosa average mol wt ∼3 , 000 kD ) , Nickel ( II ) chloride hexahydrate ( NiCl2 ) , 2- ( 3-Carboxypropyl ) -3-amino-6- ( 4 methoxyphenyl ) pyridazinium bromide ( SR 95531 ) , Dextrose , K-gluconate , sodium phosphocreatine , HEPES , MgATP , NaGTP , and biocytin . In most experiments , DHPG was applied by bath perfusion at a concentration of 4 µM . Some batches of DHPG were more potent than others , so in some cases it was necessary to reduce the DHPG concentration to as low as 2 µM in order to prevent additional spiking following the triggered burst of action potentials . Experiments using 2–4 µM DHPG were pooled together for analysis . In most experiments , the membrane potential was held at −65 mV , which required very small current injections ( < 50 pA ) . DHPG application resulted in a depolarization of 3–5 mV , so the holding potential was adjusted to −65 mV with hyperpolarizing holding current . In some experiments ( as noted ) , DHPG ( 500 µM DHPG ) or ACSF was applied to the cell via pressure application from a broken patch pipette . Pressure ( 10 psi , 0 . 2 s ) was applied via a Dagan PMI-100 pressure micro-injector . Current was injected to the cell within 3 s of pressure application . For experiments with mouse slices , bath application was performed using 10 µM DHPG , yielding an ADP similar to that observed with 4 µM DHPG in rat slices . The cDNAs for the Cav3 . 2 ( accession number AF051946 ) , Cav2 . 3 ( L27745 ) , β3 ( M88751 ) , α2δ1 ( M86621 ) , and mGluR5 ( D10891 ) were subcloned into a high expression vector pGEMHEA , which contains the 5′ and 3′ untranslated regions of the Xenopus β globin gene , linearized , and transcribed into cRNA using T7 RNA polymerase according to the manufacturer's protocol ( Ambion , Austin , TX , USA ) . Oocytes were obtained from female Xenopus laevis ( Nasco , WI , USA ) using a standard procedure . Several ovary lobes were surgically removed from mature female Xenopus laevis and torn into small clusters in SOS solution ( in mM: 100 NaCl , 2 KCl , 1 . 8 CaCl2 , 1 MgCl2 , 5 HEPES , 2 . 5 pyruvic acid , 50 µg/ml gentamicin , pH 7 . 6 ) . The follicular membranes were removed by digestion in Ca2+-free Barth's solution ( in mM: 88 NaCl , 1 KCl , 2 . 4 NaHCO3 , 0 . 82 MgSO4 , pH 7 . 4 ) containing 20 mg/ml collagenase ( SERVA , Heidelberg , Germany ) . Oocytes were injected under a stereo-microscope with 2–20 ng of cRNA using a Drummond Nanoject pipette injector ( Parkway , PA , USA ) attached to a Narishige micromanipulator ( Tokyo , Japan ) . Barium currents were measured at room temperature 4 to 8 d after cRNA injection using a two-electrode voltage-clamp amplifier ( OC-725C , Warner Instruments , Hamden , CT , USA ) . Microelectrodes ( Warner Instruments , Hamden , CT , USA ) were filled with 3 M KCl and their resistances were 0 . 2–1 . 0 MΩ . The 10 mM Ba2+ bath solution contained ( in mM ) : 10 Ba ( OH ) 2 , 90 NaOH , 1 KOH , 5 HEPES ( pH 7 . 4 with methanesulfonic acid ) . The currents were sampled at 5 kHz and low pass filtered at 1 kHz using the pClamp system ( Digidata 1322A and pClamp 8; Axon instruments , Foster City , CA , USA ) . Peak currents and exponential fits to currents were analyzed using Clampfit software ( Axon instruments , Foster City , CA , USA ) .
|
The hippocampus is an essential structure in the brain for the formation of new declarative memories . Understanding the cellular basis of memory formation , storage , and recall in the hippocampus requires a knowledge of the properties of the relevant neurons and how they are modulated by activity in the neural circuit . For many years , we have known that various chemical neurotransmitters can modulate the electrical excitability of neurons in the hippocampus . Here , we report new experiments to reveal how the chemical neurotransmitter glutamate increases neuronal excitability . The effect we study is the conversion of the afterhyperpolarization ( a cellular consequence of firing an action potential ) to an afterdepolarization . We identified the metabotropic glutamate receptors involved in this conversion ( receptors called mGluR1 and mGluR5 ) as well as the final target of modulation ( R-type calcium channels composed of Cav2 . 3 subunits ) , which cause the neurons to exhibit altered excitability in the presence of glutamate . We also determined some of the intermediate steps between activation of the glutamate receptors and modulation of the calcium channels responsible for the change in excitability , offering further mechanistic insight into how synaptic transmission can regulate cellular and network activity .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"neuroscience/neuronal",
"signaling",
"mechanisms"
] |
2010
|
A Post-Burst Afterdepolarization Is Mediated by Group I Metabotropic Glutamate Receptor-Dependent Upregulation of Cav2.3 R-Type Calcium Channels in CA1 Pyramidal Neurons
|
Multifunctional proteins , which play a critical role in many biological processes , have typically evolved through the recruitment of different domains that have the required functional diversity . Thus the different activities displayed by these proteins are mediated by spatially distinct domains , consistent with the specific chemical requirements of each activity . Indeed , current evolutionary theory argues that the colocalization of diverse activities within an enzyme is likely to be a rare event , because it would compromise the existing activity of the protein . In contrast to this view , a potential example of multifunctional recruitment into a single protein domain is provided by CtCel5C-CE2 , which contains an N-terminal module that displays cellulase activity and a C-terminal module , CtCE2 , which exhibits a noncatalytic cellulose-binding function but also shares sequence identity with the CE2 family of esterases . Here we show that , unlike other CE2 members , the CtCE2 domain displays divergent catalytic esterase and noncatalytic carbohydrate binding functions . Intriguingly , these diverse activities are housed within the same site on the protein . Thus , a critical component of the active site of CtCE2 , the catalytic Ser-His dyad , in harness with inserted aromatic residues , confers noncatalytic binding to cellulose whilst the active site of the domain retains its esterase activity . CtCE2 catalyses deacetylation of noncellulosic plant structural polysaccharides to deprotect these substrates for attack by other enzymes . Yet it also acts as a cellulose-binding domain , which promotes the activity of the appended cellulase on recalcitrant substrates . The CE2 family encapsulates the requirement for multiple activities by biocatalysts that attack challenging macromolecular substrates , including the grafting of a second , powerful and discrete noncatalytic binding functionality into the active site of an enzyme . This article provides a rare example of “gene sharing , ” where the introduction of a second functionality into the active site of an enzyme does not compromise the original activity of the biocatalyst .
The different activities displayed by multifunctional proteins are typically domain specific . A natural enzyme system that contains large numbers of proteins with complex molecular architectures is presented by the plant cell-wall–degrading apparatus from a range of microbial species . Plant cell wall degradation , now of great environmental significance , particularly with respect to the generation of renewable and sustainable biofuels [1 , 2] , is a challenging process that requires a large consortium of different enzyme activities . The plant cell wall consists primarily of an array of interlocking polysaccharides . While cellulose , which forms crystalline microfibrils , has a simple chemical structure consisting of β-1 , 4-linked glucopyranoside moieties , the matrix polysaccharides are chemically complex molecules in which the backbone polymers are protected with both sugars and organic esters [3] . Plant cell-wall–degrading systems thus feature glycoside hydrolases , which cleave the glycosidic bonds that link the sugars , and esterases that remove the diverse acylations [4 , 5] . In addition to their chemical complexity , plant cell walls have a physical structure that presents a physical barrier to enzyme attack . To compensate for the accessibility problem , plant cell-wall–degrading enzymes generally contain a noncatalytic carbohydrate binding function that , by bringing the biocatalyst into prolonged and intimate contact with its substrate , increases the rate of catalysis [6] . In general these diverse catalytic and noncatalytic carbohydrate-binding activities are housed in discrete modules within the same protein [7] . A family of carbohydrate esterases ( family CE2 in the CAZy classification ( http://www . cazy . org/ [7 , 8] ) are especially intriguing with respect to the multiple functions required of plant cell-wall–degrading systems . CE2 enzymes are acetyl esterases ( that are generally not appended to other catalytic modules ) , which are reported to be active on synthetic aryl-esters and acetylated xylan [9] . The bacterium Clostridium thermocellum contains a single CE2 member , designated CtCE2 , which is linked to the cellulase , CtCel5C , within the modular protein designated CtCel5C-CE2 . The enzyme also contains a type I dockerin module that , by binding to cohesin modules in the scaffoldin protein , incorporates CtCel5C-CE2 into the multienzyme plant cell-wall–degrading complex known as the cellulosome [10] ( Figure 1 ) . Intriguingly , CtCE2 was previously characterized as a carbohydrate-binding module ( CBM ) by virtue of its cellulose-binding capacity and its ability to potentiate the cellulase activity of the linked CtCel5C catalytic module [11 , 12] . This unusual , potentially dual , activity of the CtCE2 module prompted us to investigate the functional nuances within the CE2 family . Here we report the biochemical properties and crystal structure of several CE2 members , both single-module CE2 enzymes and CtCE2 , a component of CtCel5C-CE2 . The data show that CE2 enzymes are α/β-hydrolases in which CtCE2 displays a unique dual function within the same region of the protein scaffold . The enzyme module displays acetyl esterase activity that is spatially coupled to a noncatalytic cellulose-binding function . The binding ability directs the appended cellulase module CtCel5C to , and facilitates its activity on , cellulose . Structural and biochemical analyses reveal that the grafting of aromatic residues into the substrate binding cleft of the enzyme , primarily in harness with His-791 , a critical component of the Ser-His catalytic apparatus , also plays an important role in the cellulose-binding function . By contrast , the single-module CE2s , although exhibiting substantial structural homology and similar catalytic activities to the Clostridium esterase , do not bind cellulose . We demonstrate how subtle modifications in the active centres of different members of an enzyme family leads to functional divergence , which is manifested by the gain of dual function within the same environment of the protein .
To probe the function and role of the diverse members of the CE2 esterase family , which currently contains 31 members ( http://www . cazy . org/fam/CE2 . html ) , the biochemical properties of five CE2 enzymes were assessed . In addition to CtCE2 , which comprises the C-terminal region of C . thermocellum CtCel5C-CE2 ( formerly EGE; [11 , 12] ) we also characterised three Cellvibrio japonicus CE2 members , CjCE2A , B and C and the Bacteroides thetaiotaomicron esterase BtCE2 , identified from the genome sequence of the two bacteria [13 , 14]; these latter four enzymes are not appended to other enzyme modules ( Figure 1 ) . All of these CE2 enzymes act as acetyl esterases , releasing acetate from activated artificial substrates such as 4-nitrophenyl acetate ( 4-NPAc; see Table 1 ) and , to different extents , the acetylated plant polysaccharides xylan and glucomannan , Table 2 ( note that only very small amounts of the Bacteroides CE2 could be produced and so only initial qualitative assays could be carried out with this enzyme ) . Based on their catalytic efficiencies , CtCE2 and CjCE2B exhibit a significant preference for acetylated glucomannan over xylan , whereas CjCE2A and CjCE2C do not distinguish between the two polysaccharides . It should be noted that the specificity of CtCE2 for glucomannan reflects an extremely low KM . The esterases appear specific for acetyl groups and do not hydrolyse aryl-ferulates or aryl-coumarates ( unpublished data ) . The only other reported analysis of the catalytic activity of CE2 enzymes are the esterases from Neocallimastix patriciarum [9] that display xylan esterase activity and hydrolyse 4-NPAc , but their activity on other substrates was not assessed . The activities reported here extend the substrates known to be deacetylated by CE2 enzymes For three of these enzymes , we were able to obtain 3D crystal structures ( Figure 2A–2C ) . These include the CE2 module of CtCel5C-CE2 and two of the Cellvibrio enzymes , CjCE2A and CjCE2B . All three 3D structures reveal a bi-domain enzyme in which an N-terminal β-sheet “jelly roll” domain ( around 130 residues ) is linked to a C-terminal domain of approximately 220 residues . The C-terminal domains possess an atypical α/β-hydrolase ( SGNH-hydrolase ) fold [15] , consisting of repeating β-α-β motifs that form a curved central five-stranded parallel β-sheet , in the strand order β2 , β1 , β3 , β4 , and β5 with strand β2 interrupted by loop insertion . The sheet packs against two α-helices ( α1 and α6 on the concave side and three α-helices , α2 , α4 , and α5 , on the convex side ) , all of which are antiparallel to the β-strands . There is also a small α-helix ( α3 in the loop connecting β3 and α4 and a 310 helix between β1 and α1 . Superimposition of CtCE2 with the two Cellvibrio esterases reveals that both the N-terminal β-sheet domain ( root mean square deviation [rmsd] of 1 . 5 and 1 . 9 over 107 and 94 Cα atoms of CjCE2A and CjCE2B , respectively ) and the C-terminal catalytic domain ( rsmd of 1 . 3 and 1 . 4 over 196 and 195 Cα atoms of CjCE2A and CjCE2B , respectively ) display considerable structural conservation . Structural similarity searches using secondary-structure mapping [16] indicate that the C-terminal α/β-hydrolase domain contains the esterase catalytic centre in which , to date , a Ser-His dyad is invariant across the whole CE2 landscape . CjCE2A is a canonical serine hydrolase , with a classical Ser-His-Asp triad , in which Ser-160 is the catalytic nucleophile , His-335 activates the serine , and Asp-333 makes a hydrogen bond with Nδ1 of His-335 thus completing the catalytic triad ( Figure 2D ) . The oxyanion hole , which stabilises the incipient tetrahedral transition-state , comprise the N of Ser-160 and Gly-205 and the Nδ2 of Asn-255 . Indeed , in the pocket within the cleft of CjCE2A is a formate molecule , mimicking the reactive intermediate , which makes hydrogen bonds with the residues that form the oxyanion hole . In the case of CtCE2 the putative catalytic dyad is provided by Ser-612 and His-791 consistent with the observation that the mutants S612A and H791A display no esterase activity , Tables 1 and 2 . In this enzyme the oxyanion hole , similar to CjCE2A , comprise the N of Ser-612 and Gly-658 and the Nδ2 of Asn-705 with formate again mimicking the tetrahedral transition state . CtCE2 along with CjCE2B does not possess a side-chain residue equivalent to the Asp of a classical Ser-His-Asp triad . Instead , these enzymes display a catalytic dyad with stabilisation of the histidine provided by main-chain carbonyl groups ( Figure 2E and 2F ) . CtCE2 does possess an aspartate whose carboxylate group is approximately 5–6 Å from the His-791 Nε1 , which could , conceivably and with considerable conformation change , make an appropriate interaction with the imidazole ring . In order to probe this possibility , the D789A and D789N mutants were constructed but both retain significant catalytic activity against 4-NPAc and the polymeric substrates ( Tables 1 and 2 ) . Instead of the more typical Ser-His-Asp , the “triad” geometry is completed through the Nε1 of His-791 making a hydrogen bond with the backbone carbonyl of Glu-788 , in the case of CtCE2 ( Figure 2F ) , whilst the equivalent interaction in CjCE2B is with the main-chain carbonyl of Cys-333 ( Figure 2E ) . Catalytic dyads , although rare , have been observed in different forms elsewhere . Other than the CE2 enzymes reported here , IroE is an α/β-hydrolase peptidase that also features a catalytic Ser-His dyad [17] . Given that current wisdom suggests that the Asp of the triad does not act as a base [18] , both IroH and now CE2 highlight the need for correct orientation of histidine , which may , apparently , equally well be achieved through an interaction with a carbonyl moiety rather than a carboxylic acid . The unusual divergence to a dyad geometry in CE2 likely reflects the inclusion of a loop modification incorporating a tryptophan residue . In CtCE2 , for example , one of the key residues involved in cellulose recognition is Trp-790 in the sequence Asp-Trp-His . Inclusion of the Trp forces the adjacent aspartate into a conformation that prevents it hydrogen-bonding with His-791 ( Figure 3 ) . The dyad geometry and the topology of the active-centre binding surfaces are described below in light of the dissection of polysaccharide recognition in CE2 enzymes . Sequence analysis suggests that the dyad geometry is not unique to these two enzymes , with 14 of the 26 CE2 members lacking the putative catalytic aspartate . Furthermore , of the remaining 12 CE2 enzymes that appear to have a canonical catalytic triad , 10 have a tryptophan between the His and Asp , as observed with CtCE2 , suggesting that in these enzymes the aromatic ring may also place the Asp into an orientation that prevents it forming a classical triad . The most intriguing CE2 member is CtCE2 , which is derived from the multidomain cellulase/esterase CtCel5C-CE2 . Historical work had shown that CtCE2 not only bound cellulose but also potentiated the activity of the cellulase catalytic module on insoluble substrates [12]: the CE2 module thus behaved as a classical CBM [19] . In this report we show that CtCE2 binds to insoluble cellulose ( Figure 4 ) . Affinity gel electrophoresis ( AGE ) also demonstrated that the protein interacts with β-glucan ( β-1 , 3:β-1 , 4 mixed linked glucan ) and soluble derivatized forms of cellulose ( carboxymethylcellulose and hydroxyethylcellulose; Figure 4 ) , but displays no affinity for laminarin ( β-1 , 3-linked glucan ) , α-linked glucans or the β-1 , 4-linked xylose polymer , xylan ( unpublished data ) . Isothermal titration calorimetry ( ITC ) revealed that CtCE2 binds to cellooligosaccharides with a KD for cellohexaose of 33 μM ( Figure 5; Tables 3 and 4 ) , cellopentaose of 71 μM , cellotetraose of 333 μM and cellotriose >1 mM ( Table 4 ) , but no binding to mannohexaose or xylohexaose was detected ( unpublished data ) . The KD for β-glucan was found to be similar to that for cellohexaose ( Table 4 ) . Interestingly , whilst all CE2s possess a β-sheet domain that is reminiscent of many CBMs [19] , it is not this domain that interacts with cellulose in CtCE2 . As was revealed in the 3-D complex structure ( Figure 2 ) and discussed in more detail below , cellooligosaccharide binding , with a stoichiometry of 1 , occurs across the esterase active centre with both the Ser and His of the dyad influencing ligand recognition . The S612A mutation of CtCE2 yields an approximately 8-fold increase in affinity ( Figure 5; Tables 3 and 4 ) , whilst the H791A amino acid substitution reduces binding with the KD increasing approximately 20-fold ( Tables 3 and 4 ) . Furthermore , cellohexaose and β-glucan binding inhibit the esterase activity of the wild-type CtCE2 enzyme ( Table 3 ) , which is discussed in detail below . This capacity to bind cellulose is not a common feature within the CE2 family . Pull-down assays revealed that none of the Cellvibrio enzymes bound to insoluble cellulose ( Figure 4 ) . Furthermore , the C . japonicus CE2 enzymes and the B . thetaiotaomicron esterase were not inhibited by cellohexaose , mannohexaose , or xylohexaose ( unpublished data ) at 300 μM , while ITC also revealed no binding of the C . japonicus enzymes to these oligosaccharides ( Table 3 ) . While the binding of CjCE2A or CjCE2C to polysaccharides could not be detected by ITC ( unpublished data ) or AGE ( Figure 4 ) , CjCE2B interacted with , β-glucan , albeit with ∼17-fold lower affinity than CtCE2 ( Table 3 ) , but did not recognize carboxymethylcellulose ( unpublished data ) or hydroxyethylcellulose ( Figure 4 ) . These data reveal that the capacity of CtCE2 to recognise cellulose is , possibly , a unique feature within the CE2 family , and one that prompted its study by X-ray crystallography . The catalytic apparatus of the CE2 esterases is located within a cleft that extends across the catalytic domain and is likely to constitute the substrate binding site of these enzymes . To explore this possibility , the effect of removing the aromatic side chains ( which play a pivotal role in protein-carbohydrate recognition [19] ) that line the putative substrate binding site ( Figure 3 ) on the catalytic activity of the esterases was investigated . The data , reported in Table 2 , show that none of the aromatic residue mutations ( Y665A , W746A , and W790A ) influenced the kinetic parameters of the esterase against xylan . While the catalytic efficiencies of the CtCE2 variants against glucomannan were similar to the wild-type enzyme , there was a significant increase in KM , exemplified by the Y665A mutant ( KM > 0 . 6 mM ) , which was mirrored by a similar increase in kcat . It should also be noted that the activity of the Tyr-665 , Trp-790 , and Trp-746 mutants of CtCE2 against acetylated glucomannan was subject to substrate inhibition . Thus , these aromatic residues not only bind glucomannan but also guide the polysaccharide into the substrate binding cleft such that the acetyl groups are presented at the active site . These mutational studies indicate that the aromatic residues lining the substrate binding cleft of CtCE2 not only contribute to the tight binding of acetylated glucomannan , reflected by the very low KM , but also limit oligosaccharide product release after k2 , which appears to be the rate-determining step in catalysis . Mutation of the single aromatic residue in the substrate binding cleft of CjCE2A , Trp-212 , although not effecting kcat , leads to a significant increase in KM for both glucomannan and xylan ( Table 2 ) . These data suggest that Trp-212 contributes to substrate binding but , in contrast to CtCE2 , oligosaccharide product departure at the completion of k2 is not the rate-limiting step , and thus mutation of the tryptophan does not lead to an increase in kcat . In addition to the catalytic α/β-hydrolase domain , the CE2 members also contain an all β-sheet domain , somewhat reminiscent of a CBM . In CtCE2 this jelly-roll domain appears to extend the substrate/cellulose binding cleft of the catalytic domain . To address this issue Tyr-683 and Trp-127 , which are located in or at the interface , of the CBM-like domains of CtCE2 and CjCE2A , respectively , were mutated and the activities of the two esterase variants were assessed . The data , reported in Table 2 , show that the W127A CjCE2A mutation caused a considerable increase in KM but not kcat for glucomannan and xylan , while the catalytic properties of the CtCE2 mutant Y683A were similar to the wild-type enzyme . It would appear , therefore , that the CBM-like domain contributes to substrate recognition in CjCE2A , but its role in the catalytic activity of CtCE2 is less evident . The structure of the α/β-hydrolase domain of CtCE2 revealed a deep cleft . One wall of the cleft is formed by the loops derived from β2 , β3 , and β4 , while the other face contains the extended loop that links β5 to α5 . The structure of both wild-type CtCE2 and the S612A mutant were determined in the presence of cellohexaose and clear electron density for five β-1 , 4-linked d-glucose molecules was observed in the cleft ( Figure 3 ) , consistent with the affinities revealed by ITC ( Table 4 ) . The interaction of the oligosaccharide with CtCE2 is dominated by planar hydrophobic contacts between the pyranose rings of Glc-1 with Trp-746 , Glc-2 and Trp-790 , and Glc-4 and Tyr-665 ( Figure 3 ) . Hydrophobic interactions represent the major mechanism by which most sugars are recognised by proteins ( see for example [20–22] ) . Although the two structurally characterised Cellvibrio esterases do not bind cellohexaose or cellulose , they do contain aromatic residues in the cleft that houses the active site . Thus , Tyr-665 in CtCE2 is conserved in the C . japonicus esterases ( Trp-231 in CjCE2A and Tyr-206 in CjCE2B ) , while Trp-335 in CjCE2B is equivalent to Trp-790 in the Clostridium enzyme ( Figure S1 ) . The importance of all three hydrophobic interactions in the binding of CtCE2 to cellulose was demonstrated by the observation that the mutants W746A , W790A , and Y665A displayed no , or extremely weak , affinity for cellohexaose or β-glucan ( Figure 5; Table 3 ) . Importantly , Trp-746 in CtCE2 is not conserved within CE2 members ( Figure S1 ) ( only one other CE2 member appears to contain an aromatic residue at the equivalent position ) , including the Cellvibrio enzymes , suggesting that the insertion of this residue into CtCE2 contributes considerably to cellulose recognition . It should be emphasised , however , that the sequence and conformation of the loop in CtCE2 , which contains the critical aromatic residue Trp-746 , is different in the other CE2 enzymes . Thus , cellulose recognition is not caused by the simple insertion of a tryptophan into the substrate binding site of the Clostridium esterase , but is also influenced by the context of the introduced aromatic residue . In CtCE2 , interaction of cellohexaose with the hydrophobic platform is augmented by several polar contacts ( Figure 3 ) . Notably , the O6 of Glc-4 makes a hydrogen bond with Oγ of Ser-612 ( in one of its two conformations ) in the wild-type esterase and Nε2 of His-791 in the S612A mutant , while O6 of Glc-2 interacts with Oε1 of Gln-780 . As discussed above , mutation of His-791 to alanine reduces affinity for cellohexaose ∼20-fold but , intriguingly , the S612A mutant binds to the oligosaccharide ∼8-fold more tightly than the native protein ( Tables 3 and 4 ) . In the wild type enzyme Ser-612 adopts two conformations . In one conformation the hydroxyl makes a polar contact with cellohexaose forcing the ligand away from the histidine . In the S612A mutant the hexasaccharide adopts a different conformation in which it is now able to make a , presumably more productive , hydrogen bond with the histidine . Indeed , the bond distance between Ser-612 and Glc-4 is 3 . 3 Å ( the two conformations adopted by the serine suggests a relatively weak interaction with the glucose ) , while the bond distance between His-791 and Glc-4 is reduced to 2 . 8 Å , pointing to a stronger interaction . It is possible that the CBM-like domain , which appears to abut onto the substrate binding cleft in CtCE2 , may also contribute to cellulose recognition . The observation that the Y683A mutant ( Tyr-683 is the only aromatic residue in the cleft anterior to the catalytic apparatus ) of the esterase retains its capacity to bind cellohexaose ( Table 4 ) and cellulose ( unpublished data ) , however , indicates that the recognition of this polysaccharide is mediated solely by the α/β-hydrolase domain . Cellohexaose inhibits the deacetylation of glucomannan by CtCE2 with a Ki of 32 μM ( Figure 6A ) , similar to the KD of the esterase for the hexasaccharide determined by ITC ( Table 4 ) . Consistent with its capacity to interact with the catalytic apparatus and with aromatic residues in the substrate binding cleft , cellohexaose is a competitive inhibitor of glucomannan , as indicated by the double reciprocal plots which intersect on the y-axis ( Figure 6A ) . In contrast , cellohexaose inhibits the hydrolysis of 4-NPAc by CtCE2 with a Ki of 3 . 1 μM , which is ∼10-fold lower than the ITC-determined KD of the enzyme for the ligand . In addition , the inhibition by cellohexaose displayed noncompetitive kinetics ( Figure 6B: double reciprocal plots intersect on the x-axis ) , suggesting that the ligand inhibits the deacetylation of glucomannan and the aryl-acetate by different mechanisms . CtCE2 , typical of enzymes that mediate catalysis through a covalent intermediate , displays biphasic kinetics against substrates , such as 4-NPAc , which contain good leaving groups . Pre-steady state kinetics , deploying 4-NPAc as the substrate , revealed a k2 ( pre-steady state rate of 4-nitophenolate release ) value of ∼21 , 790 s−1 with an amplitude that was ∼80% of the predicted value , while k3 , extracted from the steady state rate , was ∼4 , 725 s−1 ( unpublished data ) . Significantly , cellohexaose inhibited enzyme deacylation ( the steady state rate; k3 ) , with a Ki of 7 μM , but did not significantly affect enzyme acylation ( the pre–steady state burst of 4-nitrophenolate release; k2 ) . The data described above are consistent with the observed noncompetitive kinetics displayed by cellohexaose when 4-NPAc is the substrate , and the difference between Ki and KD . ITC measures binding to the apoenzyme ( available to the substrate at k2 ) while Ki determines affinity of the oligosaccharide for the enzyme at the rate-limiting step of the reaction , k3 , when the enzyme is in complex with acetate . Indeed , the kinetics of inhibition are consistent with the observation that cellohexaose binds more tightly to the enzyme when it makes a polar contact with His-791 rather than Ser-612; when the serine is in complex with acetate ( or formate in the crystal structure ) it is not available to hydrogen bond with the hexasaccharide that is now able to interact with His-791 . By contrast , k2 is the rate-limiting step when glucomannan is the substrate and thus cellohexaose must now bind to the apo form of the enzyme to inhibit the reaction leading to a lower affinity ( as it will make a polar contact with Ser-612 and not His-791 ) and competitive kinetics .
Family CE2 esterases raise intriguing questions about the optimization of plant cell-wall–degrading systems . The most interesting feature of the divergence of function within the CE2 family is that CtCE2 , derived from the CtCel5C-CE2 multimodular enzyme , is uniquely able to act as a CBM , with structural and biochemical studies highlighting Trp-746 , a residue observed in only one other CE2 enzyme , as a key recognition element . It is probably highly relevant that CtCE2 is the only member of CE2 that is a component of a modular enzyme . Thus , CtCE2 is an example of an emerging biochemical theme that protein scaffolds possess a latent potential to acquire new , orthogonal functionality [23 , 24] . This report is in contrast to the Ohno model of protein evolution , which proposes that mutations that provide new functionalities are introduced into a redundant copy of a duplicated gene [25 , 26] . A central component of the Ohno model is that the introduction of new functions into a protein compromises its original activity , hence the requirement for gene duplication subsequent to mutation . The observation that the chicken structural protein δ-crystallin and the metabolic enzyme , argininosuccinate lyase , are encoded by the same gene led to the “gene sharing” hypothesis , which states that a gene can be recruited for a novel function without significant changes to its protein coding sequence [27] . Bergthorsson et al . [28] and others argue that Ohno's model poses a dilemma , because it requires that the duplicated copy of the gene , which incurs an energetic cost , must be retained within a population prior to acquiring the mutations that confer the new activity . These concerns led to the proposal that gene products display secondary activities prior to duplication of the encoding gene , and that there is a selection for both activities subsequent to amplification ( these models are reviewed in [29] ) . A major caveat with models based on “gene sharing , ” however , is that amino acid changes that introduce new functions often compromise the stability or original activity of the protein [30] . Thus , in general , the introduced functionality replaces the endogenous activity of the enzyme . Here we show that the active site of CtCE2 displays dual activities; it catalyses the deacetylation of plant polysaccharides and also potentiates the activity of its appended cellulase catalytic module through its noncatalytic cellulose binding function . As such , CtCE2 provides an example of gene sharing in which the new function ( cellulose binding ) has developed without having an obvious deleterious effect on the existing esterase activity of the enzyme . It would appear , therefore , that there are different ways to evolve new enzyme functions without compromising the original activity displayed by the protein . Which of these pathways a protein follows depends on ( i ) what the new function is , ( ii ) whether it is advantageous to retain both activities , and ( iii ) whether the chemistry or steric constraints of the two divergent functions preclude the housing of these activities in the same active site . Throughout plant cell-wall–degrading systems , noncatalytic binding to polysaccharides by enzymes that attack insoluble substrates is primarily conferred by CBMs that are linked to , but spatially distinct from , the catalytic module [19] . In general these targeting modules are joined by flexible linker sequences , although noncatalytic carbohydrate binding regions , while spatially distinct from the active site , can occasionally be distal components of the catalytic module itself [31 , 32] . This report shows that the presentation of an additional tryptophan residue within the substrate binding cleft of the esterase enables the enzyme to acquire cellulose-binding capacity—potentiating the activity of the appended cellulase catalytic module—whilst still retaining its original catalytic activity . Indeed , the importance of cellulose recognition by CtCE2 in the function of the appended catalytic module CtCel5C is supported by the observation that mutation of Trp-790 , Tyr-665 , or Trp-746 in the full-length enzyme ( CtCel5C-CE2 ) reduces the activity of the cellulase against insoluble substrates by 3- to 5-fold . Hence the CE2 family displays a spectrum of activities reflecting the grafting of new functionality upon the α/β framework . This manifests itself most powerfully in the acquisition of cellulose binding by CtCE2 , demonstrating how nature has exploited latent carbohydrate recognition to introduce additional “noncatalytic” polysaccharide binding features that are complementary to the activities displayed by a complex modular enzyme . The composite structure of the plant cell wall , which requires the synergistic interactions of multiple catalytic and binding functions to elicit its degradation , exerts selection pressures that lead to the generation of single proteins with multiple activities . The CE2 family , and CtCE2 in particular , reveal an extreme example of this functional complexity and provides a platform for the future directed engineering of plant cell-wall–degrading enzyme systems , which is one of the key environmental goals of the twenty-first century .
The region of the C . thermocellum cellulase–esterase gene , cel5C-ce2 , encoding the C-terminal CE2 esterase module , CtCE2 ( residues 482 to 814 of the full-length enzyme ) , was amplified by PCR from genomic DNA ( strain ATCC 27405 ) using the thermostable DNA polymerase Pfu Turbo ( Stratagene ) and primers ( Table S1 ) that contain NdeI and XhoI restriction sites , respectively . The DNA product was cloned into the NdeI and XhoI sites of the Escherichia coli expression vector pET22b ( Novagen ) to generate pCtCE2 . CtCE2 encoded by pCtCE2 contains a C-terminal His6 tag . The region of the genes encoding the mature C . japonicus and B . thetaiotaomicron VPI-5482 CE2 esterases were amplified by PCR from genomic DNA , using primers listed in Table S1 , and cloned into NdeI- and XhoI-restricted pET22b such that the proteins encoded by the recombinant plasmids contained a C-terminal His6 tag . E . coli BL21 cells harbouring the CE2 esterase-encoding recombinant expression vectors were cultured in Luria-Bertani broth at 37 °C to mid-exponential phase ( A600nm ∼0 . 6–1 . 0 ) , and recombinant protein expression was induced by the addition of 1 mM isopropyl 1-thio-β-D-galactopyranoside ( IPTG ) and incubation for a further 5 h at 37 °C . The CE2 esterases were purified by immobilized metal ion affinity chromatography ( IMAC ) using Talon resin ( Clontech ) and elution in 20 mM Tris/HCl buffer containing 300 mM NaCl and 100 mM imidazole . The eluted esterase was then dialyzed against 20 mM Tris/HCl buffer , pH 8 . 0 ( Buffer A ) and applied to a Bio-Rad Q12 column . The esterases were eluted with a 400 ml gradient of 0–500 mM NaCl in Buffer A . The fractions containing esterase activity were concentrated using a 10 kDa MWCO Vivaspin 20 centrifugal concentrator and applied to a Superdex 200 26/60 Hiload column ( Amersham ) equilibrated in 10 mM Tris-HCl buffer , pH 8 . 0 , containing 150 mM NaCl . Protein was eluted at a flow rate of 1 ml/min . Both chromatography steps employed a Bio-Rad FPLC system . Purified enzymes were adjudged homogenous by SDS-PAGE . To produce seleno-methionine–containing proteins the same protocol was employed except that the enzyme was expressed in E . coli B834 ( Novagen ) using growth conditions described previously [33] ) , and 2 mM 2-mercaptoethanol was included in all buffers during purification up to the point of IMAC and 10 mM for all subsequent steps . The purified seleno-methionine enzyme eluted from the gel filtration column was exchanged into ddH2O containing 10 mM DTT using Amersham PD10 column . Site-directed mutagenesis was carried out using the PCR-based QuikChange site-directed mutagenesis kit ( Stratagene ) according to the manufacturer's instructions , using pCtCE2 or pCjCE2A as the template and primers pairs that are listed in Table S1 . Substrates used in the enzyme assays described below were purchased from Sigma except acetylated Konjac glucomannan , which was purchased from Megazyme International ( Bray , Ireland ) ; acetylated xylan , which was prepared from birchwood xylan ( Sigma ) using acetic anhydride following the method of Johnson et al . [34] , and O-acetyl galactoglucomannan that was extracted from spruce ( Picea abies ) [35] . To determine activity against 4-NPAc , 1 ml enzyme reactions were carried out in 50 mM sodium phosphate buffer , pH 7 . 0 , containing 1 mg/ml of BSA and substrate concentrations up to 10 mM . The reaction was initiated by the addition of an appropriate concentration of enzyme; 10 nM for wild type and 10 μM for the most inactive mutants , and the release of 4-nitrophenolate was monitored at 400 nm . To determine the rate of deacetylation of acetylated polysaccharides , the release of acetate was determined using an acetic acid detection kit ( Megazyme International ) following the manufacturer's recommendation except that the product was measured continuously rather than in a stopped reaction . Pre-steady-state kinetics were performed using a stopped-flow apparatus ( Applied Photophysics Model SX-17MV ) , with the flow path thermostatically controlled at 15 °C , and a 2-mm light path to monitor the formation of the product 4-nitrophenolate at A400 . Equal volumes ( 50 μl ) of solutions mixed with a dead time of 1 . 5 ms , contained , respectively , 10 μM CtCE2 and 2 mM 4-NPAc in 50 mM sodium phosphate buffer , pH 7 . 0 . These solutions were supplemented with 0–100 μM cellohexaose . The capacity of the CE2 enzymes to bind to soluble saccharides was determined by AGE or by ITC . AGE was performed as described previously [33] with the polysaccharide ligand included in the polyacrylamide gel at 0 . 1 % . ITC [36] was carried out in 50 mM HEPES-Na buffer , pH 8 . 0 , containing 300 mM NaCl at 25 °C . Data collected for titrations with 10 mg/ml of β-glucan were fitted with a molar concentration of 5 mM , the value at which n = 1 . The concentrations of cellooligosaccharides in the syringe ranged from 0 . 7 to 5 mM and the CE2 enzymes in the reaction cell were at 80–100 μM . The determined KA and ΔH values were used to calculate ΔS from the standard thermodynamic equation . Binding to insoluble cellulose was determined by pull-down assay using phosphoric acid swollen cellulose ( PASC ) . Briefly , 1 mg of washed PASC in 50 mM Tris-HCl , pH 8 . 0 , containing 300 mM NaCl ( Buffer B ) , was mixed with 50 μg of protein in a total volume of 100 μl and incubated on ice for 30 min . This was then centrifuged for 1 min at 13 , 000g and the supernatant containing the unbound protein removed . The cellulose was then washed three times with 100 μl of ice-cold Buffer B and each wash discarded , before 50 μl of SDS-PAGE loading buffer ( containing 10 % SDS ) was added and the bound protein eluted from the cellulose by boiling for 5 min . Approximately equal volumes of starting protein and material eluted from the cellulose were then subjected to SDS-PAGE to assess binding . CjCE2A: Native and seleno-methionine crystals of CjCE2A were grown by hanging drop vapour-phase diffusion in 1 M diammonium hydrogen phosphate/0 . 1 M sodium acetate , pH 5 . 0 , for 1–2 d at 20 °C ( 30 % v/v glycerol was added as the cryoprotectant ) . Data from seleno-methionine derivatised CjCE2A were collected on ID23–1 at a wavelength of 0 . 97880 Å using an ADSC Q315R CCD ( charge-coupled device ) detector the wavelength was optimised for the f′′ component of the anomalous signal using a fluorescence scan . Data from crystals of native CjCE2A were collected on ID14 . 2 using an ADSC Q4 CCD detector at a wavelength of 0 . 9330 Å . Data for all CE2 enzymes were processed with either DENZO [37] or MOSFLM from the CCP4 suite ( Collaborative Computational Project Number 4 1994 ) . The structure of CjCE2A was solved using the single-wavelength anomalous dispersion ( SAD ) method . Selenium positions were determined using SHELXD [39] and the phases were subsequently calculated using SHELXE . 5% of the data were set aside for cross validation analysis and the behaviour of Rfree was used to monitor and guide the refinement protocols . ARP/wARP [40] in conjunction with REFMAC [41] was used to automatically build the sequence into the electron density . Refinement was undertaken in REFMAC with manual correction to the model using COOT [42] . CtCE2: Crystals of both wild-type and S612A CtCE2 in complex with cellohexaose were grown by hanging drop vapour-phase diffusion from 20% PEG3350 and 0 . 2–0 . 3 M ammonium iodide , with protein at ∼8 mg ml−1 and cellohexaose at approximately 1 mM . Crystals were harvested in rayon fibre loops before being bathed in cryoprotectant solution ( crystallisation conditions augmented with 25% v/v glycerol ) and flash frozen in liquid nitrogen . Data were collected at the ESRF from single crystals at 100 K for cellohexaose complexes of both wild-type and S612A mutant forms with data processed as previously . The structure of CtCE2 was solved by molecular replacement using PHASER with the search model being the CjCE2A structure . ARP/wARP was used to build the initial model and refinement was undertaken as described above . CjCE2B: Native and seleno-methionine crystals of CjCE2B were grown by hanging drop vapour diffusion in 0 . 1 M imidazole , pH 8 . 0 , 10 % PEG 8 , 000 , for 3 d at 20 °C ( 25 % v/v glycerol was added as the cryoprotectant ) . Native data were collected at ESRF on ID14 . 2 using ADSC Q4 CCD detector at a wave length of 0 . 933 Å . The structure was solved using a combined approach in which molecular replacement using a combined CtCE2/CjCE2A model was used in PHASER [43] was used in conjunction with the Se-Met derived SAD phases for map calculation and verification of methionine positions . The final model was build using COOT and refined using REFMAC . CjCE2B crystals contains two molecules in the asymmetric unit . The final models of CjCE2B in chain A was ordered from 27 to 361 containing five missing regions in amino acids 41–44 , 61–63 , 81–84 , 98–98 , and 127–137 , while chain B was ordered from 26 to 361 and contained one missing region between residues 99 and 107 . Coordinates and observed structure factor amplitudes have been deposited at the Worldwide Protein Data Bank ( wwPDB , http://www . wwpdb . org/ ) , and the crystal structure statistics are in Table 5 .
|
Proteins that display multiple activities have typically evolved through the recruitment of different domains , each of which has a specific function . Thus , in a multifunctional protein , the different activities are mediated by spatially distinct domains such that a single domain can provide the specific chemical requirements for one activity . Indeed , current evolutionary theory argues that the colocalization of diverse activities within a single-domain enzyme is likely to be a rare event , as it would compromise the existing activity of the protein when a new function evolves . Nonetheless , a potential example of multifunctional recruitment into a single protein domain is provided by an enzyme that contains a cellulase enzyme module and a discrete noncatalytic cellulose-binding module . In this article , we show that the cellulose-binding module displays esterase activity and that these diverse activities are housed within the same site on the protein . Structural analysis of the enzyme reveals that its catalytic residues also contribute to the noncatalytic cellulose-binding function . This report provides a rare example of “gene sharing , ” whereby the introduction of a second functionality into the active site of an enzyme does not compromise the original activity of the catalyst .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry"
] |
2009
|
The Active Site of a Carbohydrate Esterase Displays Divergent Catalytic and Noncatalytic Binding Functions
|
The CD8+ cytotoxic T lymphocyte ( CTL ) response is an important defence against viral invasion . Although CTL-mediated cytotoxicity has been widely studied for many years , the rate at which virus-infected cells are killed in vivo by the CTL response is poorly understood . To date the rate of CTL killing in vivo has been estimated for three virus infections but the estimates differ considerably , and killing of HIV-1-infected cells was unexpectedly low . This raises questions about the typical anti-viral capability of CTL and whether CTL killing is abnormally low in HIV-1 . We estimated the rate of killing of infected cells by CD8+ T cells in two distinct persistent virus infections: sheep infected with Bovine Leukemia Virus ( BLV ) and humans infected with Human T Lymphotropic Virus type 1 ( HTLV-1 ) which together with existing data allows us to study a total of five viruses in parallel . Although both BLV and HTLV-1 infection are characterised by large expansions of chronically activated CTL with immediate effector function ex vivo and no evidence of overt immune suppression , our estimates are at the lower end of the reported range . This enables us to put current estimates into perspective and shows that CTL killing of HIV-infected cells may not be atypically low . The estimates at the higher end of the range are obtained in more manipulated systems and may thus represent the potential rather than the realised CTL efficiency .
Virus replication is countered by a range of innate and adaptive host defences . One important adaptive defence is the CD8+ cytotoxic T lymphocyte ( CTL ) response which controls infection by a number of mechanisms including perforin/granzyme and Fas/FasL-mediated lysis and secretion of anti-viral cytokines . Although CTL-mediated cytotoxicity has been widely studied for many years , the typical rate at which virus-infected cells are killed by the CTL response in vivo is poorly understood as only three viral systems have been studied and these yield estimates that differ considerably . Quantification of the in vivo lytic capability of CTLs is essential for a detailed understanding of the immune response . This includes understanding the balance between viral replication and viral clearance , understanding the rate limiting steps in CTL killing and thus how killing can be increased and understanding the failure of CTL vaccines . To date , the in vivo rate of CTL killing of virus-infected cells has been estimated in Lymphocytic Choriomeningitis Virus ( LCMV ) [1]–[5] , Polyoma virus [6] and Human Immunodeficiency Virus Type 1/Simian Immunodeficiency Virus ( HIV-1/SIV ) [7]–[14] . These studies consistently find that CTL killing is considerably more rapid in LCMV and Polyoma virus than in HIV-1 infection ( Table S1 ) . In the LCMV system there are 5 studies of two data sets [1]–[5] . Both data sets were generated using a similar experimental approach in which labelled peptide-pulsed target cells were transferred into mice acutely or chronically infected with LCMV . It was found that target cells were killed by a single NP396 or GP276-specific CTL response , defined as a clone or clones specific for a single epitope , at a rate of 21–500 d−1 in acute infection and by either NP396- or GP33-specific CTL responses at a rate of 3–42 . 2 d−1 in chronic infection ( Text S1 ) . Even if we assume that there are no other effective CTL responses these estimates are extraordinarily high; in reality there are probably at least 2 or 3 ( studies suggest 10 to 28 [15] , [16] ) other responses , yielding even higher estimates of killing attributable to the total CTL response , defined as all clones specific for a given virus . In Polyoma virus , using a similar experimental approach , killing rates of the same order of magnitude were found: a rate of 67 . 7 d−1 and 21 . 6 d−1 for a single MT389-specific response in acute and chronic infection respectively [6] . In HIV-1/SIV there are nine studies using three basic approaches . In the first approach [7] , [8] the rate of CTL killing was estimated from the decline in cells productively infected with HIV-1 following reinfusion of ex vivo activated autologous HIV-1-specific CD8+ T cells . It was found that the killing rate attributable to the total CTL response was 4 . 4–9 . 8 d−1 . This is almost certainly an overestimate of the rate of death attributable to natural CTL as it implies a death rate due to CTL that is higher than the total death rate of cells productively infected with HIV-1 [17] . In the second approach , the rate of CTL killing was estimated from the dynamics of HIV-1 escape from the CTL response in humans chronically infected with HIV-1 [9] . Here , much lower killing rates were calculated; it was estimated that a single CTL response is only responsible for about 2% of productively infected cell death , equivalent to a killing rate of 0 . 02 d−1 for a single response or 0 . 1–0 . 2 d−1 for the total response . This approach was later extended to primary HIV-1 infection [10] and SIV-1-infected macaques [11]–[14] , yielding median estimates of 0 . 14 d−1 and 0 . 12 d−1 for a single CTL response respectively [10] , [11] . Finally , indirect quantification of the contribution of CTL killing to total cell death in SIV-infection resulted in a killing rate of 0 . 3–0 . 4 d−1 for the total CTL response [18] , [19] . Thus at one extreme , a target cell exposed to CTL specific for one LCMV peptide will have a lifespan ( 1/killing rate ) of approximately 16 seconds; at the other extreme an HIV-1-infected cell that was killed by a CTL response against a single peptide would have a lifespan of nearly 2 months . Is it plausible that CD8+ T cell killing varies by 5 orders of magnitude between LCMV and HIV-1 ? If these are genuine differences , can we learn from the efficiency of the LCMV-specific response to therapeutically enhance the HIV-1-specific response ? More generally , do these estimates span the physiological range , that is are they representative of the in vivo lytic action of CTL and thus define the maximal rate at which a virus can replicate and still be controlled or is the true physiological range even wider ? With estimates for only three viruses it is impossible to answer these questions . Arguably , the estimates in LCMV may be higher than in the physiological system due to excess levels of peptide on the peptide-pulsed targets but conversely , CTL killing in HIV-1 infection may be abnormally low due to virus-induced damage to the immune system . The aim of this paper is to estimate the rate of in vivo killing of CD8+ T cells in two distinct persistent virus infections: sheep infected with Bovine Leukemia Virus ( BLV ) and humans infected with Human T Lymphotropic Virus type 1 ( HTLV-1 ) .
Bovine leukemia virus ( BLV ) is an exogenous retrovirus that naturally infects cows but which also establishes a persistent infection in B cells in sheep upon experimental infection [20] . Directly ex vivo most BLV-infected cells do not express viral protein . A more complete description of the experimental methods and resulting data is presented elsewhere [21] . Briefly , 6 BLV-infected and 3 uninfected sheep were studied . For each sheep , blood was collected and half the volume was incubated at 37°C for two hours while the other half was incubated at 4°C . The fractions were then labeled with carboxyfluorescein succinimidyl ester ( CFSE ) and PKH26 respectively , pooled and re-injected into the jugular vein . In the fraction that had been incubated at 37°C ( CFSE-labelled ) BLV-infected B cells expressed viral proteins , which was quantified by the up-regulation of the early viral protein Tax [21] . In contrast , cells incubated at 4°C ( PKH26-labelled ) did not express Tax . Blood was taken at multiple time points over a two week period and the fraction of labelled cells and the mean fluorescence intensity ( MFI ) of the label were measured . The proportion of CFSE-labelled cells consistently declined more rapidly than PKH26-labelled cells in the same sheep , except in animal BLV6 where no difference between the two cell populations was found ( Figure 1 ) . Swapping the labels ( i . e . labelling cells incubated at 37°C with PKH26 and cells incubated at 4°C with CFSE ) confirmed that it was the incubation temperature not the choice of label that determined the dynamics [21] . Furthermore , in uninfected sheep the loss of CFSE-labelled and PKH26-labelled cells was identical ( Figure S1 ) . The experiment was repeated in three animals injected intravenously with Cyclosporine A ( CsA ) , a drug that interferes with TCR-mediated activation of the transcription factor NF-AT and subsequently with T cell activation , proliferation and the formation of the immunological synapse 22–26 . Following CsA treatment , the CFSE-labelled and PKH26-labelled cells declined at similar rates ( Figure 2 ) . As CsA abrogates T cell function this implies that the majority of the excess loss of CFSE-labelled cells compared to PKH26-labelled cells could be attributed to CTL-mediated killing . We conclude that in the absence of CsA , CFSE-labelled cells are lost more rapidly than PKH26 labelled cells because CFSE-labelled cells expressed more viral protein and thus were more susceptible to CTL killing . In animal BLV6 proviral load is low , hence only a small fraction of CFSE-labelled cells are susceptible to CTL-killing resulting in an undetectable difference in loss of the two cell populations . We constructed a mathematical model to describe B cell dynamics in vivo . Three cell populations were considered: uninfected B cells , infected B cells that expressed viral proteins ( henceforth called ag+ ) and silently-infected B cells that did not express viral proteins ( henceforth ag− ) . We initially assume there are no ag− cells labelled with CFSE at time zero; we later relax this assumption . Infected B cells that express viral proteins can be recognized and killed by CTL . Silent BLV-infected cells can up-regulate viral protein expression and thus become susceptible to CTL killing . The intensity of CFSE and PKH26 label is also modelled . The model parameters are the rate of CTL killing , the rate of up-regulation of viral proteins , the fraction of ag+ cells in the PKH26-labelled population at the start of the experiment and the proliferation rate and disappearance rate of the cell populations . There is evidence that Tax promotes cell proliferation [27] , so the proliferation rates of the ag+ and ag− populations were allowed to differ . The fraction of infected cells in each animal was calculated from the proviral load ( Methods and Table S2 ) . The model was fitted to the data – the proportion of CFSE- and PKH26-labelled B cells and the MFI of CFSE and PKH26 label – using least squares regression . We estimated a median rate of killing by the total BLV-specific CTL response of ag+ BLV-infected cells of 1 . 98 d−1 ( Table 1 and Figure 3 ) . However , for three of the animals confidence intervals are large; this is partly because lower proviral load in these three animals makes the data less sensitive to changes in the killing rate . If we limit our analysis to killing rates we can estimate with reasonable confidence , i . e . estimates of animals BLV1–3 , we find a median value of 1 . 60 d−1 . Estimates of the other model parameters are provided in Table S3 . The rate of killing in CsA-treated animals was non-zero ( Table 1 ) , though not all rates were significantly different from zero . To test our model assumptions and the robustness of the estimates of CTL killing we considered a number of alternative models . Firstly , we relaxed the assumption that all CFSE-labelled infected B cells express viral proteins and instead we estimated the fraction of ag+ CFSE+ infected cells as a free parameter . We found that the fraction of ag+ CFSE+ infected cells was estimated to be ∼100% for all animals except BLV6 , which has a very low proviral load , confirming our assumption ( Table S5 ) . Secondly , we tested a model in which we set the fraction of infected cells at the start of the experiment equal to the proviral load ( rather than calculating it from the proviral load ) , but found that this gives a worse fit for most data sets ( Table S6 ) . Finally , we tested three further models representing , one with a damaged population that has a reduced lifespan due to the treatment , one with the same proliferation rate for ag+ and ag− infected cells and one in which uninfected cells can become infected , which all yielded similar estimates of the rate of CTL killing ( Text S2 ) . Killing is commonly considered to take place in organs like spleen and lymph nodes . Our model is based on the assumption that the balance between blood and lymphoid populations is established fast after reinjection of the labelled cells [28] compared to the timescale of the experimental sampling schedule so that the dynamics in the blood are representative of the dynamics in the organs . Fitting a model that includes the lymphoid compartment and the circulation to and from the blood is problematic; circulation parameters are highly dependent and this compartment was not measured experimentally . Nevertheless , we extended our model to include a lymphoid compartment . The rate at which cells leave the blood was fixed; multiple runs were performed with this fixed parameter taking different values from a realistic range . We found that the ratio of the concentration of lymphocytes in the blood to the concentration of lymphocytes in the lymphoid compartment and the time to steady state between these two compartments was of the order reported in the literature [28] , [29] resulting in a median residence time in the lymphoid compartment of 2 . 6 h , which is at the faster end of the range reported in the literature . Forcing the residence time to 10 h does not have a large effect on either killing rate estimate or fit . Resulting killing estimates were very similar to what we found previously when we did not include the lymphoid compartment ( Text S3 and Table S7 ) . Human T cell lymphotropic virus type 1 ( HTLV-1 ) is a CD4+ T cell-tropic virus that establishes a persistent infection in humans . HTLV-1 is the etiological agent of a range of inflammatory diseases , of which HTLV-1-associated myelopathy/tropical spastic paraparesis ( HAM/TSP ) , a chronic inflammation of the central nervous system , is best described . HAM/TSP is associated with a high HTLV-1 proviral load and so therapeutic intervention aims to reduce proviral load . One approach is to use the histone deacetylase inhibitor valproic acid ( VPA ) to activate HTLV-1 gene expression and expose infected cells to the host immune response thus reducing proviral load [30] . A more complete description of this approach and resulting data is presented elsewhere [30] . Briefly , 16 HAM/TSP patients were given VPA orally ( 20 mg/kg per day ) and HTLV-1 proviral load was quantified at the start and after 1 and 3 months of treatment . We previously showed that , at physiological doses in vivo , VPA had no detectable impact on the efficiency of the CTL response though at higher doses in vitro a toxic effect has been reported [30]–[32] . Before VPA treatment , proviral load is relatively constant and most HTLV-1-infected cells do not express viral proteins [33] , [34] . After VPA treatment , a consistent decline in proviral load was seen in all 16 HAM/TSP patients ( Figure 4 ) . VPA treatment does not affect the proliferation and intrinsic death rates of HTLV-1-infected CD4+ T cells [35] , and NK cells are thought to play little cytotoxic role in the control of HTLV-1 infection [36] so we assume this decline is mainly due to the increased exposure of HTLV-1-infected CD4+ T cells to the CTL response . To estimate the CTL killing rate we converted proviral load to the fraction of infected cells ( Methods and Table S2 ) and constructed a mathematical model describing the dynamics of infected cells after VPA-treatment . In the model the population of infected cells was divided into an ag+ and ag− population . The ag+ population can be recognized and killed by CTL; the ag− population can up-regulate viral protein expression and thus become susceptible to CTL killing . Both populations proliferate and have a CTL-independent death rate . We used estimates of proliferation and natural death rates from the literature . There are no quantitative estimates of how VPA increases the expression of viral proteins by HTLV-1 infected cells in vivo , so we assumed different values for the up-regulation rate ( u ) and estimated CTL killing rate ( k ) by fitting the model to the number of infected cells . We found that the value of k changes only minimally with the value of u . Only at very small values of u , when the availability of ag+ cells becomes limited by the slow up-regulation rate , do estimates of k change considerably , but in this range of u-values the quality of the fit , measured by the sum of squared residuals , is substantially worse ( Figure S3 ) . We found a median killing rate of 0 . 10 d−1 and a range of 0 . 03–0 . 13 d−1 ( Table 2 ) . This killing estimate is very similar if we assume the number of infected cells is equal to the proviral load ( Table S8 ) .
The two systems we used to estimate the rate of CTL killing of virus-infected cells in vivo offer a number of benefits . Firstly , CTLs are unmanipulated and present in physiological quantities in the correct in vivo context . Secondly , targets are naturally infected cells . Finally , the large blood volume in sheep and humans permits repeat sampling as opposed to murine models , where it is usually necessary to perform serial sacrifices which introduces noise into the data . The limitations of this approach are the requirement to infer the dynamics of ag+ and ag− cells from the total population of infected cells and the manipulation of the infected cells to induce viral protein expression . In BLV-infected sheep we estimated the median killing rate of virus-expressing B cells by the total CTL response was 1 . 6 d−1 . In HTLV-1-infected humans we estimated the median killing rate of virus-expressing CD4+ T cells by the total CTL response was 0 . 1 d−1 . Previous estimates of the lytic potential of CD8+ T cells in persistent infection span a wide range , from 0 . 2 d−1 to 380 d−1 [5] , [9] . Although both BLV and HTLV-1 infection are characterised by large expansions of virus-specific chronically activated CTL with immediate effector function ex vivo and no evidence of overt immune suppression [37] , [38] , our estimates are at the lower end of this range ( Figure 5 ) . This raises the possibility that the rate of CTL killing of targets in LCMV-infected mice is atypically high . In the LCMV studies the targets were peptide-pulsed splenocytes which are unlikely to represent naturally infected cells either in the levels of antigen presented or in frequency . However , Ganusov et al [6] found only 1–2 orders of magnitude between in vivo estimates of the killing rate of more physiological peptide concentrations and the maximal killing rate of peptide-pulsed target cells in Polyoma virus . Like many viruses , HIV-1 , LCMV , HTLV-1 and BLV all employ strategies to evade the immune response e . g . [39] , [40]; these strategies would not be utilised by peptide-pulsed targets , potentially leading to an overestimate of the true physiological rate of killing . It is likely that the estimate of CTL killing in HIV-1 infection , calculated by Wick et al [7] , is also an overestimate of the natural killing rate of CD8+ T cells in the unmanipulated system because it is an order of magnitude higher than the total death rate attributable to all causes ( including CD8+ T cell killing , activation induced cell death and any cytopathic effect of HIV-1 ) , which has been estimated at 0 . 7–1 d−1 [17] . Given that the CTLs studied by Wick were expanded and activated in vitro it is not unlikely that they were more effective than in the absence of intervention . The large difference between these two killing rates and the rates found with more physiological target and effector cells raises the question if the total CTL response can potentially kill at high rates but in the physiological setting only realise a fraction of their maximum killing capacity , giving scope for the development of CTL based immunotherapy . We estimated the CTL killing attributable to the total or single CTL response . Estimating the per capita CTL killing is non-trivial . Firstly it requires an estimate of the frequency of lytic CTL in lymphoid tissue ( not necessarily the same as the frequency of IFNg secreting CD8+ T cells in the blood ) . More importantly it requires an assumption of the relationship between the rate of killing and the frequency of effectors and targets [4] . Therefore we do not attempt to express our estimates as a per-capita rate . CTL killing in vitro has been quantified for HIV-1 , LCMV and HTLV-1 [41]–[43]; the in vitro estimates differ considerably from the in vivo estimates but there is no systematic pattern in the discrepancy . Unexpectedly , although the time course of CFSE and PHK26-labelled cells in CsA treated animals was virtually indistinguishable ( Figure 2 ) , the model fitting estimated a non-zero rate of killing of ag+ cells . This is because the loss of labelled cells is biphasic , i . e . we see an initially steep decline in labelled cells followed by a slower decline . In the simplified model we used , the only way in which cell loss can be biphasic is if ag+ and ag− cells die at different rates and thus the model “forces” a non-zero estimate of CTL killing . The model could be extended to incorporate an alternative cause of biphasic decline . For instance if we assume a fraction of cells are damaged by the ex vivo treatment we find zero killing rates for two out of three CsA-treated animals and a lower killing rate for the third . For the untreated animals , this model gives similar estimates to the simplified model ( Text S2 ) . However , without a clear biological argument to extend the model in one way or another we felt it was preferable to use the simplified model . Induction of viral protein expression will not only result in presentation of antigenic peptides on MHC; viral proteins will also be expressed on the surface of infected cells rendering them susceptible to antibody-dependent cytotoxicity . This mechanism of infected cell loss is not included in our model and our estimates of CTL killing rate may therefore represent an upper bound . However , in vitro studies have shown , for HTLV-1 that NK cells do not affect proviral load [36] . The estimates of proliferation rate we used to estimate killing rate in HTLV-1 infection were obtained using deuterated glucose labelling . Comparison of proliferation rates of T cell subsets estimated with either deuterated glucose or deuterated water has shown that the literature estimates of proliferation rate , measured with deuterated glucose , might be an overestimate [44] , [45] . Consequently , HTLV-1 killing rates could be even lower than estimates presented in this study . Recently , several studies found support for a non-lytic role for CD8+ T cells in viral infections [19] , [46]–[49] . We assume that the anti-viral control we have quantified is largely lytic as both BLV and HTLV-1 spread mainly by mitotic replication and are therefore unlikely to be susceptible to non-lytic mechanisms . In conclusion , the estimates we found for the CTL killing rate in BLV and HTLV-1 infection are at the lower end of the range described in the literature . This enables us to put current estimates into perspective and suggests that a CTL killing rate of the order of 1 per day or less , as found in HIV-1 and SIV , is not atypically low . The higher estimates are based on the loss of peptide-pulsed targets in LCMV and expanded , activated CD8+ T cells in HIV-1 , both of which may overestimate the physiological rate of CTL killing . These estimates may represent the potential rather than the realised CTL efficiency . If this interpretation is correct there is considerable scope for improvement of the CTL response . Further studies in additional systems are urgently needed to test this suggestion .
The animal protocol and experimental procedures in the BLV-study were approved by the Commission d'Ethique Animale affiliated to the Université de Liège ( permit number 996 ) and were conducted in accordance with institutional and the European guidelines for animal care and use . The HTLV-1 study was approved by the Local and Regional Research Ethics Committee . All participants provided written informed consent and all procedures were carried out in accordance with the Declaration of Helsinki . Blood was collected from six BLV-infected and three control sheep . Half of the volume was incubated at 37°C for 2 hours and labelled with CFSE , the other half was incubated at 4°C and labelled with PKH26 . The two fractions were then pooled and intravenously re-injected . Blood was collected at different time points post-injection . The experiment was repeated with 3 BLV-infected sheep injected intravenously for 3 weeks with cyclosporin A at 5 mg/kg/day . The percentage of CFSE and PKH26-positive B lymphocytes and the mean fluorescence intensity ( MFI ) of the positive and negative population were determined by flow cytometry . For each animal total number of B lymphocytes per µl of blood was determined . Proviral load ( pvl ) was measured in copies per number of B lymphocytes by real-time PCR . Further details are provided in [21] . Proviral load of HTLV-1 infected HAM/TSP patients was determined before and after one and three months of VPA treatment by real-time TaqMan PCR-method [50] . VPA was administered orally ( 20 mg/kg per day ) . Further details are provided in [30] . Proviral load quantifies the number of proviral copies rather than the number of infected cells . Due to the possibility of multiple infection these numbers are not necessarily equal . To estimate the fraction of infected cells from the proviral load we used an in silico approach . We assumed equal infection probability for each cell , whether already infected or uninfected , and set the total number of infection events ( N ) equal to pvl of each animal . For each animal we estimated the infected fraction by randomly “infecting” N cells in the total pool , allowing multiple infection , and counted the number of unique cells that were infected . We repeated this 50 , 000 times to acquire the mean and standard error of the infected fraction ( Table S2 ) . Ganusov and de Boer [4] have convincingly argued that , in the absence of a detailed knowledge of the form of the CTL killing term it is preferable to estimate the overall rate of killing attributable to the CTL response rather than the per capita CTL killing rate as the latter is not robust to model assumptions . In LCMV-infection there are indications that the killing follows the law of mass action but it is unclear whether this applies to other infections and other hosts [51]; we therefore estimated the overall rate of killing . The experiment is conducted over 2 weeks so we do not model recirculation of cells from blood via the lymphoid organs as we assume the system reaches equilibrium on the timescale studied . Short-term incubation at 37°C of blood from BLV-infected sheep triggers viral expression [21] . We therefore assume that the CFSE-labelled B lymphocyte population can be divided into two subsets , one containing infected cells that express the viral protein ( ag+ , T ) and one containing cells that are not infected ( H ) . The PKH26-labelled population will consist of three different populations , one population of infected cells that do express the viral protein ( T ) , one population of infected cells that do not express the virus ( ag− , S ) and one population of cells that are not infected ( H ) ; cells can move from the ag− to the ag+ population at rate u . The initial ratio between the ag+ ( T ) and ag− ( S ) BLV –infected B lymphocytes in the PKH26-labelled population is a free parameter in the model . We thus defined a model system describing the dynamics of each population: ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) ( 7 ) ( 8 ) ( 9 ) Where n , is the number of divisions that an average CFSE- or PKH26-positive cell would have to undergo to become label-negative , i indicates the number of divisions a cell has already undergone , ranging from 1 to n-1 , u is the rate at which infected ag− cells , up-regulate viral protein , ps is the proliferation rate of ag− cells and uninfected cells , pt is the proliferation rate of ag+ cells , d is the disappearance rate , and k is the rate at which ag+ cells are killed by CTL ( all rates in d−1 ) . To determine n we used the method in reference [52]: ( 10 ) and found that on average after 6 divisions the label becomes undetectable ( Table S9 ) . The predicted fraction of labelled cells is calculated by summing the cells in Si , Ti and Hi and dividing this by the total number of B lymphocytes for each sheep . The predicted MFI was: ( 11 ) Where B is T+S+H and I0 is the MFI of the label-positive population at the start of the experiment . In a previous publication [21] , we used a simple descriptive model to analyze the disappearance rate ( as opposed to CTL killing rate ) of the total population of B cells ( as opposed to the ag+ population of BLV infected B cells ) . Here we use mechanistic models to estimate the rate of CD8+ T cell killing . The data ( fraction of labelled cells and the MFI ) was scaled to obtain equal means . The model was fitted using the function modFit in the package FME in R [53] . The confidence interval on the estimates was obtained by bootstrapping the cases and trimming the extremes [54] , [55] . To estimate CD8+ T cell killing in HTLV-1 infection from the decline in number of infected cells we used a model describing the change in number of ag− infected cells ( S ) and ag+ infected cells ( T ) , together representing the change in number of infected cells ( N ) : ( 12 ) ( 13 ) ( 14 ) where ps and pt are proliferation rates of the ag− and ag+ population of infected cells respectively , d is the CTL-independent , intrinsic death rate , u is the upregulation rate of viral proteins and k is CTL killing rate of the total HTLV-1 specific CD8+ T cell population . In the literature pt has been estimated as 0 . 07–0 . 13 d−1 [56] and ps as 0 . 027 d−1 [45] , [57] . We set d equal to ps and estimated k from these values over a range of values for u . We repeated this procedure using proviral load instead of the fraction of infected cells and found similar k estimates ( Table S8 ) .
|
Virus replication is countered by a range of innate and adaptive host defences . One important and widely studied adaptive defence is the CD8+ cytotoxic T lymphocyte ( CTL ) response . Quantification of the in vivo lytic capability of CTLs is essential for a detailed understanding of the immune response . This includes understanding the balance between viral replication and viral clearance , understanding the rate limiting steps in CTL killing and thus how killing can be increased and understanding the failure of CTL vaccines . However , the typical rate at which virus-infected cells are killed by the CTL response in vivo is poorly understood . Current estimates differ considerably and are especially low for HIV-1-infection . We estimated the rate of killing of infected cells by CD8+ T cells in two distinct persistent virus infections which enables us to put current estimates into perspective . We show that CTL killing of HIV-infected cells may not be atypically low . The estimates at the higher end of the range are obtained in more manipulated systems and may thus represent the potential rather than the realised CTL efficiency .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"blood",
"cells",
"white",
"blood",
"cells",
"immune",
"cells",
"cell",
"biology",
"animal",
"cells",
"t",
"cells",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"immunology",
"immune",
"response"
] |
2014
|
Rates of CTL Killing in Persistent Viral Infection In Vivo
|
Corneal astigmatism refers to refractive abnormalities and irregularities in the curvature of the cornea , and this interferes with light being accurately focused at a single point in the eye . This ametropic condition is highly prevalent , influences visual acuity , and is a highly heritable trait . There is currently a paucity of research in the genetic etiology of corneal astigmatism . Here we report the results from five genome-wide association studies of corneal astigmatism across three Asian populations , with an initial discovery set of 4 , 254 Chinese and Malay individuals consisting of 2 , 249 cases and 2 , 005 controls . Replication was obtained from three surveys comprising of 2 , 139 Indians , an additional 929 Chinese children , and an independent 397 Chinese family trios . Variants in PDGFRA on chromosome 4q12 ( lead SNP: rs7677751 , allelic odds ratio = 1 . 26 ( 95% CI: 1 . 16–1 . 36 ) , Pmeta = 7 . 87×10−9 ) were identified to be significantly associated with corneal astigmatism , exhibiting consistent effect sizes across all five cohorts . This highlights the potential role of variants in PDGFRA in the genetic etiology of corneal astigmatism across diverse Asian populations .
Astigmatism is a condition where light rays are prevented from focusing at a single point in the eye , resulting in blurred vision at any near or far distance . While astigmatism comprises cornea and non-corneal components , it typically results from the unequal curvature of two principle meridians in the anterior surface of the cornea known as corneal astigmatism [1] , [2] . The presence of a high degree of astigmatism during early development is believed to be associated with refractive amblyopia [3] , [4] , [5] , as evidenced by decreased best-corrected visual acuity which cannot be remedied by external corrective lenses . Early abnormal visual input caused by uncorrected astigmatism can lead to orientation-dependent visual deficits , despite optical correction of visual acuity later in life [6] . In addition , it has been suggested that optical blurring by astigmatism may predispose the development of myopia , commonly known as nearsightedness [7] , [8] , [9] , [10] . Astigmatism is highly prevalent across most populations and poses a significant burden to global public health with at least 1 in 3 adults above 30 years of age suffering from astigmatism of −0 . 5 diopters ( D ) or less [11] . The reported age-adjusted prevalence of astigmatism was 37 . 8% for Chinese adults [12] , 54 . 8% in rural Asian Indians [13] , 37% ( ≤−0 . 75D ) for Caucasian in Australia [14] and 36 . 2% in the US [15] . The prevalence of astigmatism in children varies considerably across different studies and ethnic groups . For instance , the prevalence of astigmatism ( ≤−0 . 75D ) in school-children ranges from 13 . 6% in Australia [16] , 20% in Northern Ireland [17] , 28 . 4% for Singapore school children [8] , to 42 . 7% for Chinese children in urban China [18] . Although the precise cause of astigmatism is unknown , genetic factors have been implicated in the etiology of corneal astigmatism . Studies have reported a higher risk of developing astigmatism in individuals whose sibling or parents have astigmatism [11] . Evidence from twin studies suggests a genetic etiology in astigmatism development , with the estimated heritability ranging from 30% to 60% [19] , [20] , [21] , [22] , [23] . For instance , Hammond and colleagues [21] investigated the inheritance of astigmatism for 226 monzygotic ( MZ ) and 280 dizygotic ( DZ ) twins in the United Kingdom and found genetic effects accounted for 42% to 61% of the variation in corneal astigmatism . While most of the twin studies have been conducted in Caucasian populations , a study on Chinese twins in Taiwan reported a heritability estimate of 46% for corneal astigmatism , suggesting that genetic factors account for a similar extent in the etiology of astigmatism for Asian populations [22] . However , no genetic loci have been systematically and consistently identified to be implicated in the development of corneal astigmatism . Here we report the findings from the meta-analyses of five genome-wide association studies ( GWAS ) performed in 8 , 513 individuals from three Asian populations . The discovery phase of our study comprises 4 , 254 individuals from two population-based GWAS performed in adults of Chinese and Malay ethnicities from the Singapore Prospective Study Program ( SP2 ) and the Singapore Malay Eye Study ( SiMES ) respectively . The replication phase comprises of data from three other genome-wide surveys of: ( i ) 2 , 139 Indian adults from the Singapore Indian Eye Study ( SINDI ) ; ( ii ) 929 Chinese school children from the Singapore Cohort Study of the Risk Factors for Myopia ( SCORM ) ; and ( iii ) 397 Chinese trios of parents and astigmatic offsprings from the Singaporean Chinese in the Strabismus , Amblyopia and Refractive Error Study ( STARS ) .
The characteristics of the post-QC samples from the five studies are summarized in Table 1 . The post-QC SP2 dataset comprised 2 , 016 adults , of which 1 , 231 individuals had corneal astigmatism ( ≤−0 . 75 D ) and 785 subjects were defined as non-astigmatic controls . The post-QC SiMES dataset comprised 2 , 238 adults ( 1 , 018 cases and 1 , 220 controls ) . In total , 462 , 518 and 515 , 712 autosomal genotyped SNPs passed stringent quality control criteria for SP2 and SiMES respectively and the genome-wide meta-analysis was conducted on 460 , 528 SNPs present in both studies . There was no evidence of over-inflation of statistical significances due to population structure in either of the discovery cohorts ( SP2 λGC = 1 . 006 , SiMES λGC = 1 . 007 ) or in the meta-analysis of both studies ( overall λGC = 1 . 007 ) . Suggestive evidence of association ( defined as 10−6<P-value<10−5 ) were seen in each of SP2 and SiMES ( Figure S1A and S1B ) , as well as in the meta-analysis of SP2 and SiMES where a collection of SNPs deviated from their expected distributions in the quantile-quantile plots of the P-values ( Figure S1C ) . None of the SNPs in the discovery meta-analysis attained genome-wide significance of P-value<5×10−8 . Seven SNPs exhibited evidence stronger than P-value<1 . 0×10−5 and these were found to cluster in the platelet-derived growth factor receptor alpha ( PDGFRA ) gene on chromosome 4q12 ( lowest P = 9 . 44×10−7 at rs7677751; Table 2; Figure S2 ) . Interestingly , these SNPs are located within the MYP9 region identified previously as a candidate locus for myopia through linkage scans [24] . In the replication phase with the three additional GWAS cohorts , three SNPs in PDGFRA ( rs7677751 , rs2307049 and rs7660560 ) attained genome-wide significance in the combined analysis ( Table 2 ) with the lead SNP rs7677751 from the discovery phase remaining as the strongest signal in the combined analysis ( P = 7 . 87×10−9; Figure 1 ) . All seven SNPs from the discovery phase exhibited P-values<0 . 05 in SINDI but not in SCORM or STARS . However the direction and magnitude of the effect sizes at these seven SNPs in all three replication cohorts were highly similar to those seen in the discovery populations of SP2 and SiMES ( Table 2 , Figure 2 ) . No significant evidence of effect size heterogeneity was detected across the SNPs ( heterogeneity I2 P-value≥0 . 246 ) , and the minor allele frequencies of these SNPs are consistently similar across all five studies ( Table S1 ) . A genome-wide meta-analysis of the combined five cohorts did not yield any additional locus with genome-wide significance ( see Figure S3 for QQ and Manhattan plots , λGC = 1 . 002; Table S2 ) . At the lead SNP rs7677751 in PDGFRA , the frequency of the risk T-allele ranged from 0 . 19 to 0 . 26 in the five cohorts and conferred a 26% higher risk of corneal astigmatism than the C allele ( OR = 1 . 26 , 95% CI = 1 . 16–1 . 36 ) in the meta-analysis across all five studies . This SNP alone explains 0 . 41% of the variation in corneal cylinder power . In addition , a general genetic model identified that the 5 . 5% of the individuals in the combined cohorts that carry the TT genotype at rs7677751 had a 1 . 65-fold ( 95%CI = 1 . 33–2 . 06 , P-value = 6 . 23×10−6 ) increase in the risk of developing corneal astigmatism compared to those that are not carrying any copies of the risk allele ( Figure S4 ) . All of the associated SNPs spanned 10 kb within PDGFRA at 4q12 ( Figure 2 ) , and a high degree of linkage disequilibrium is seen at this locus in all three Asian populations ( Chinese , Malays and Asian Indians; Figure S5 ) .
We have performed a genome-wide survey for corneal astigmatism across 8 , 513 individuals , where the discovery phase combines the data from two GWAS performed in Chinese and Malay adults , and the replication phase included Asian Indian adults , Chinese children and family trios . We observed a strong and consistent association with the onset of corneal astigmatism at the PDGFRA gene locus on chromosome 4q12 across all five Asian cohorts , with three SNPs in this locus exhibiting evidence stronger than genome-wide significance in the meta-analysis . To the best of our knowledge , this is the first GWAS to investigate the genetic etiology of corneal astigmatism in a genome-wide fashion . The PDGFRA gene spans 69 kb with 23 coding exons and resides on chromosome 4q12 . The receptor for platelet-derived growth factor ( PDGF ) contains two types of subunit , a- and β- PDGFRA , which are differentially expressed on the cell surface [25] . PDGFR-a binds to three forms of PDGF ( PDGF-AA , AB and BB ) and mediates many biological process including embryonic development , angiogenesis , cell proliferation and differentiation . The role of PDGFRA in cellular growth and proliferation is underlined by its contribution to the pathogenesis of gastrointestinal stromal tumours [26] . A large body of evidence has shown that both PDGF and its receptors are expressed in corneal epithelium , stromal fibroblasts and endothelium [27] , [28] as well as proliferative retinal tissues in eyes [29] , [30] , [31] . Along with other cytokines ( epidermal growth factor , transforming growth factor-a , -β etc ) , studies have further suggested that PDGF and its receptors can mediate corneal fibroblast migration , matrix remodeling and play an important role in corneal wound healing [28] , [32] , [33] , [34] . The corneal stroma comprises a large portion of the cornea; the sensitivity of stromal tissue to various growth factors is well described [35] . The administration of PDGF resulted in keratinocyte elongation using rabbit corneal stroma tissue [36] . In light of this , a role for PDGFRA in the regulation of ocular development and parameters cannot be excluded , and our study suggests that genetic polymorphisms within PDGFRA may be involved in the regulation of corneal biometrics resulting in the occurrence of corneal astigmatism . In addition , Hammond et al . reported that 4q12 ( MYP9; LOD 3 . 3 ) was significant linked with myopia from a genome-wide linkage study of 221 dizygotic twin pairs [24] , and subsequent replication revealed nominal significance of 4q12 ( P = 0 . 065 ) for refractive error in African-American families [37] . We thus undertook a candidate SNP approach with the identified SNPs to investigate the possible association between PDGFRA and ( i ) the onset of high myopia; ( ii ) the refractive error as a quantitative trait . We did not observe any striking association between the identified variants with either outcomes , suggesting that the association of PDGFRA with corneal astigmatism is probably not through any shared etiology with myopia . The lead SNP in our analyses rs7677751 is located in the intro 1 of PDGFRA . Interestingly , among the SNPs identified , rs2228230 is coding-synonymous ( valine:GTC>valine:GTT ) and resides in exon 18 , while rs3690 is within the untranslated-3′ region . These three SNPs ( rs7677751 , rs2228230 and rs3690 ) are strongly correlated with each other ( pair-wise Pearson correlation coefficient r ranging from 0 . 77 to 0 . 81 ) , although the association evidence at the latter two SNPs did not reach genome-wide significance . As the next closest gene ( GSX2 ) from the 5′ end of PDGFRA is 127 kb away and is not within the LD block with our identified SNPs ( Figure 1 ) , it is unlikely that the signals observed in our study are attributed to functional variants located beyond PDGFRA . Our group recently reported a strong association between variants in PDGFRA with corneal curvature [38] . Corneal curvature is an ocular dimension defined as the average of the radius of corneal curvature at the horizontal and vertical meridians . Myopic eyes have been found to have steeper corneas ( reduced radius of curvature ) , but the significant correlation between corneal curvature and refractive error was not consistently observed [39] , [40] , [41] . Excessively flatter cornea is associated with cornea plana , producing high hyprotropia and likely resulting in angle-closure glaucoma [42] , [43] . On the other hand , corneal astigmatism is an eye-disorder , where the cornea is more curved in one meridian direction compared to the other . This fragmentizes the light rays entering the eye , leading to the inability to focus onto a single point in the eye [1] . It is thus interesting that the same PDGFRA gene has been identified in two ocular outcomes that are biologically different , given the presence of a weak correlation between corneal astigmatism and corneal curvature ( Spearman correlation coefficient r between 0 . 088 and 0 . 192 in our cohorts; Figure S6 ) , pointing to a possible pleiotropic contribution of PDGFRA . Our study has adopted a binary definition of corneal astigmatism ( affected and unaffected ) that is commonly adopted in clinical practice and eye-trait epidemiology [16] , [44] . One caveat of this definition is the potential for misclassifying the affected status , particularly for samples with cylinder power around the cutoff threshold of −0 . 75D . To evaluate the robustness of our findings to the choice of threshold used , we additionally performed the association analysis at the identified SNPs with four different combinations of the thresholds used to define cases and controls . We observed that the odds ratios were highly similar across all four scenarios ( Table S3 ) , with the combined evidence at rs7677751 ranging from Pmeta of 1 . 5×10−4 to 6 . 7×10−8 . Unsurprisingly , the association evidence was weakest in the scenario with the most stringent thresholding ( ≤−1 . 5D for cases and >−0 . 5 for controls ) , given this stringency comes at the expense of decreasing the number of individuals in each study . We additionally performed a secondary analysis treating corneal cylinder power as a quantitative trait . Strong statistical evidence was consistently observed at the three leading SNPs ( rs7677751 , P = 1 . 76×10−7; rs2307049 , P = 3 . 41×10−7 and rs7660560 , P = 4 . 41×10−7; Table S4 ) , indicating that our findings are robust to the definition of the phenotype . Owing to the relatively small sample sizes within each of the five GWAS studies , we have chosen to prioritize our survey to identify genetic variants that contribute to the etiology of corneal astigmatism in multiple Asian populations . While Malays have been observed to be genetically closer to the Chinese , the Asian Indians tend to be genetically closer to the Caucasians [45] . Our discovery at PDGFRA thus suggests that part of the underlying biological pathway responsible for astigmatism development is common to multiple populations , although there may be population-specific genetic variants that our current study is not sufficiently powered to identify . Our study has included two pediatric Chinese populations ( SCORM and STARS ) with school or pre-school children who are still progressing to their final phenotype . It was documented that a high degree of astigmatism occurs during infancy and the early childhood [46] . The prevalence rates remain stable during young adulthood ( 18 to 40 years ) , but increase consistently during late adulthood at aged 40 years or older [1] , [12] . Studies have also indicated that the age-related change in astigmatism is associated with meridians changes in the cornea [11] . Children and adolescents have a predominance of “within-the-rule” corneal astigmatism in general , where the vertical curve is greater than the horizontal ( axis of 1° to 15° ) ; while in older adults , it shifts to “against-the-rule” astigmatism ( axis of 75°–105° ) [47] , [48] . However , our study considers corneal astigmatism without reference to the axis nor the longitudinal changes from children to adults . Whether PDGFRA plays the same role in pediatric and adults populations will however need further investigation .
This study adhere to the Declaration of Helsinki . Ethics approvals have been obtained from the Institutional Review Boards of the Singapore Eye Research Institute , Singapore General hospital , National University of Singapore and National Healthcare Group , Singapore . In all cohorts , participants provided written , informed consent at the recruitment into the studies . For studies involving children who were still minors ( SCORM and STARS ) , written informed consent was obtained from the children's parents . All studies used a similar protocol to measure ocular phenotypes including corneal curvature , autorefraction and cylinder power by a team of eye care professionals . Participants in SP2 , SIMES and SINDI underwent non-cycloplegic automated refractive assessments using the autorefractor ( Canon RK-5 , Tokyo , Japan ) . For SCORM and STARS , cycloplegic measurements ( Canon RK-F1 , Tokyo , Japan ) were performed 30 minutes after three drops of 1% cyclopentolate which were administered 5 minutes apart . Corneal curvature radii in the horizontal and vertical meridian were determined with keratometry in millimeters [60] . The keratometer measured the anterior corneal surface and used a refractive index of 1 . 3375 to account for the contribution from the posterior corneal surface to derive the corneal refractive power in diopters . Corneal cylinder power was calculated as the difference between the flattest and steepest meridian of the keratometry readings in diopters of power . As the corneal cylinder power between the right and left eyes are strongly correlated across all five cohorts ( Pearson's correlation coefficient r ranging from 0 . 51 to 0 . 79; P<2 . 2×10−16 ) , the mean corneal cylinder power over both eyes was used to define corneal astigmatism . Averaging ocular measurements between two eyes in genetic studies has been suggested to be statistically more powerful than using the information from only one eye [61] , and this approach has been consistently adopted in genome-wide studies of myopia [62] , [63] . As with previous studies [16] , [44] , we have defined individuals with average corneal cylinder power ≤−0 . 75D as cases , and those with average corneal power between −0 . 75D and 0D as controls . For SP2 , a total of 2 , 867 blood-derived DNA samples were genotyped using the Illumina Human610 Quad and 1Mduov3 Beadchips . For the samples that were genotyped on the two platforms , the genotypes from the denser platform were used in our study . For SiMES ( n = 3 , 072 ) , SINDI ( n = 2 , 593 ) and STARS ( n = 1 , 451 ) , the Illumina Human610 Quad Beadchips was used for genotyping all DNA samples . For the 1 , 116 SCORM children , DNA samples were genotyped on the Illumina HumanHap 550 Duo Beadchips . Detailed data quality control ( QC ) procedures for each study were provided in the supplementary information ( Text S1 ) . In brief , for case-control study design , QC criteria included a first round for autosomes SNP QC to obtain a cleaned set of genotypes for sample QC , by excluding SNPs with: ( i ) missingness ( per-SNP call rate ) >5%; ( ii ) minor allele frequency ( MAF ) <1%; and ( iii ) HWE p-value<10−7 . Using the subset of SNPs passing the first round QC , samples were then excluded based on the following conditions: ( i ) per-sample call rates of less than 95%; ( ii ) excessive heterozygosity ( defined as the sample heterozygosity to be beyond 3 standard deviations from the mean sample heterozygosity ) ; ( iii ) cryptic relatedness; ( iv ) gender discrepancies; and ( v ) deviation in population membership from population structure analysis . A second round of SNP QC was then applied to the remaining samples passing quality checks . We excluded SNPs with missingness >5% , gross departure from HWE ( P value<10−6 ) , MAF<1% and low concordance between duplicate samples on different genotype platforms ( relevant to SP2 samples only ) . Population structure was ascertained using principal components analyses ( PCA ) with the EIGENSTRAT program [64] . Population substructure of SP2 and SiMES was examined by PCA with respect to three population panels in the HapMap samples ( Figure S7 ) . Due to the presence of population structure within the Malay and Indian samples ( Figures S8 and S9 respectively ) , we adjusted for the top 5 principal components in the association analyses for the SiMES and SINDI datasets . For the STARS trios , we additionally excluded samples and trio-sets on the basis of excessive Mendelian inconsistencies defined as having >1% of the post-QC SNPs exhibiting Mendelian errors . SNPs with more than 10% Mendelian errors are excluded from the association analyses , and the genotypes leading to Mendelian errors in all other remaining SNPs are coded as missing . As family trios are more robust to the presence of population structure , we did not exclude any samples due to population structure . The genome-wide association tests were performed using PLlNK ( version 1 . 07; http://pngu . mgh . harvard . edu/~purcell/plink/ ) as the primary analysis tool . A logistic regression adjusted for age and gender is used to model the association of genetic markers with corneal astigmatism . For each of SiMES and SINDI , the top 5 principal components of genetic ancestry from the EIGENSTART PCA were also included as covariates to adjust for population stratification in these populations . We assumed an additive genetic model where the genotypes of each SNP is coded as 0 , 1 , and 2 for the number of minor alleles carried , in keeping with increments in allelic dosage . For family GWAS association tests in STARS , a transmission disequilibrium test ( TDT ) is used to measure significant distortions in transmission of an allele from heterozygous parents to the affected offspring under the condition of Mendel's law [65] . We also performed a quantitative trait analysis with the average corneal cylinder power as the outcome . This is performed in PLINK for the unrelated samples , and in FBAT ( http://www . hsph . harvard . edu/~fbat/fbat . htm ) for the family trios . As the distribution of the quantitative trait of corneal cylinder power is skewed ( Figure S10 ) , we performed a normal quantile transformation [66] prior to the association analysis for unrelated samples . For family-based data , no transformation was conducted since the FBAT does not require normal trait [67] Meta-analyses are performed using weighted-inverse variance estimated from fixed-effect modeling in METAL ( http://www . sph . umich . edu/csg/abecasis/metal/ ) . We adopt the method by Kazeem and Farrall [65] to pool the evidence from the case-control analyses and the family trio TDT . For the quantitative trait analysis , the overall z statistics is calculated as a weighted sum of the z-statistics from the linear regressions in the non-familial data and FBAT analysis for the family-based data , weighted by number of unrelated individuals or trios in the respective studies [68] . Results from a genome-wide meta-analysis of the SNPs common to SP2 and SiMES are used in the discovery phase to identify putative variants that are associated with the onset of corneal astigmatism , defined as a P-value<10−5 . The remaining three cohorts ( SINDI , SCORM and STARS ) are used to validate the putative findings . In addition , a genome-wide meta-analysis of all five datasets is also performed . Genotyping quality of all reported SNPs in this paper have been visually assessed by checking the intensity clusterplots .
|
Corneal astigmatism is associated with reduced visual acuity and an increased risk of developing refractive amblyopia . Although it is highly heritable , there is no prior study on the genetic etiology of corneal astigmatism . Our genome-wide meta-analysis across 8 , 513 individuals in five genome-wide surveys from three genetically diverse populations in Asia reveals that genetic variants in the PDGFRA gene on chromosome 4q12 is significantly associated with corneal astigmatism . These polymorphisms in the PDGFRA gene exhibit strong and consistent effects over all five Asian cohorts . PDGFRA is a receptor for platelet-derived growth factor , which is expressed in many retinal tissues in the eyes and appears to contribute to ocular development . Results from our study further suggest the potential role of PDGFRA in the regulation of corneal biometrics .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"ophthalmology",
"genetics",
"biology",
"genetics",
"and",
"genomics"
] |
2011
|
Genome-Wide Meta-Analysis of Five Asian Cohorts Identifies PDGFRA as a Susceptibility Locus for Corneal Astigmatism
|
Failure in detecting naturally occurring breeding sites of Aedes mosquitoes can bias the conclusions drawn from field studies , and hence , negatively affect intervention outcomes . We characterized the habitats of immature Aedes mosquitoes and explored species dynamics along a rural-to-urban gradient in a West Africa setting where yellow fever and dengue co-exist . Between January 2013 and October 2014 , we collected immature Aedes mosquitoes in water containers in rural , suburban , and urban areas of south-eastern Côte d’Ivoire , using standardized sampling procedures . Immature mosquitoes were reared in the laboratory and adult specimens identified at species level . We collected 6 , 159 , 14 , 347 , and 22 , 974 Aedes mosquitoes belonging to 17 , 8 , and 3 different species in rural , suburban , and urban environments , respectively . Ae . aegypti was the predominant species throughout , with a particularly high abundance in urban areas ( 99 . 374% ) . Eleven Aedes larval species not previously sampled in similar settings of Côte d’Ivoire were identified: Ae . albopictus , Ae . angustus , Ae . apicoargenteus , Ae . argenteopunctatus , Ae . haworthi , Ae . lilii , Ae . longipalpis , Ae . opok , Ae . palpalis , Ae . stokesi , and Ae . unilineatus . Aedes breeding site positivity was associated with study area , container type , shade , detritus , water turbidity , geographic location , season , and the presence of predators . We found proportionally more positive breeding sites in urban ( 2 , 136/3 , 374 , 63 . 3% ) , compared to suburban ( 1 , 428/3 , 069 , 46 . 5% ) and rural areas ( 738/2 , 423 , 30 . 5% ) . In the urban setting , the predominant breeding sites were industrial containers ( e . g . , tires and discarded containers ) . In suburban areas , containers made of traditional materials ( e . g . , clay pots ) were most frequently encountered . In rural areas , natural containers ( e . g . , tree holes and bamboos ) were common and represented 22 . 1% ( 163/738 ) of all Aedes-positive containers , hosting 18 . 7% of the Aedes fauna . The predatory mosquito species Culex tigripes was commonly sampled , while Toxorhynchites and Eretmapodites were mostly collected in rural areas . In Côte d’Ivoire , urbanization is associated with high abundance of Aedes larvae and a predominance of artificial containers as breeding sites , mostly colonized by Ae . aegypti in urban areas . Natural containers are still common in rural areas harboring several Aedes species and , therefore , limiting the impact of systematic removal of discarded containers on the control of arbovirus diseases .
Several Aedes species act as vectors of arboviral diseases , such as yellow fever , dengue , chikungunya , Rift Valley fever , and Zika virus infections that are of considerable public health relevance [1] . The transmission patterns of these arboviruses and their geographic expansion are expected to change due to environmental transformation , including urbanization [2 , 3] . Besides yellow fever , other arboviruses are likely underestimated and underreported in Africa because of low awareness by health care providers , other prevalent non-malarial febrile illnesses , lack of diagnostic tests , and absence of systematic surveillance [4] . Nevertheless , yellow fever , dengue ( DENV1-4 ) , chikungunya , and Zika viruses are currently circulating in West Africa through the sylvatic , rural , and epidemic cycles maintained by wild and urban vectors [5 , 6] . Côte d’Ivoire has been repeatedly facing yellow fever and dengue outbreaks involving several vectors such as Aedes africanus , Ae . furcifer , Ae . luteocephalus , Ae . opok , and Ae . vittatus in rural , and Ae . aegypti in urban areas [7 , 8] . These outbreaks have often occurred in foci characterized by high rate of urbanization due to economic development supported by palm oil and rubber farming , trade , and traffic [7] . Arboviral disease transmission is influenced by community-level effects of container-dwelling Aedes mosquito larvae by regulating the production and fitness of adult vectors [9] . Aedes mosquito larvae are highly sensitive to environmental changes , including urbanization [10] . Some Aedes species ( e . g . , Ae . aegypti ) inhabit a wide variety of containers ranging from natural containers ( e . g . , tree holes ) to artificial containers ( e . g . , tires , discarded items , and other water containers ) due to their ecologic plasticity [11] , while others are restricted to specific breeding sites because of the higher sensibility of their offspring to environmental changes [12] . The ecologic plasticity allows Ae . aegypti and Ae . albopictus to spread worldwide by sea , air , and land transportation networks , and to adapt to new and changing environments [10] . The choice of breeding sites is governed by competition and predation among immature stages of Aedes and other mosquitoes that co-exist in the same breeding site [11 , 12] . For example , intra- and interspecific competition between Ae . aegypti and Ae . albopictus [13] and among several Aedes species [12] has been reported . Moreover , mosquito species such as Toxorhynchites spp . , Eretmapodites spp . , and Culex tigripes predate on the larvae of Aedes [12 , 13] . The biotic factors may also interact with abiotic factors , such as the climate [13] . As larvae directly depend on water , precipitation is the most important physical factor . The complex patterns of flooding and drying of larval breeding sites govern arboviral transmission [14] . In Côte d’Ivoire , yellow fever has been a key factor that forced the transfer of the colonial capital from Grand-Bassam to Bingerville near Abidjan in 1899 [15] . However , more than a century later , yellow fever and dengue outbreaks still remain an unresolved public health issue [7 , 8 , 15] . During arbovirus epidemics , vector controls are mostly based on the systematic removal of artificial Aedes breeding sites in urban areas . The most effective vector control strategy is the control of immature stages in their aquatic habitats [12] . Hence , effective larval control requires a deep understanding of larval ecology . Our study aimed to characterize the dynamics of Aedes larval breeding sites , species composition , and biological associations in terms of geographic and seasonal variations along a rural-to-urban gradient in south-eastern Côte d’Ivoire . As Aedes mosquito larvae are highly sensitive to environmental changes [10] , we hypothesized that larval breeding sites differ in species composition between urban and rural areas .
The study protocol received approval from the local health and other administrative authorities . All entomologic surveys and sample collections carried out on private lands or private residential areas were done with the permission and written informed consent of the residents . This study did not involve endangered or protected species . The study was conducted in three areas located within a traditional arbovirus focus in south-eastern Côte d’Ivoire: Ehania-V1 ( geographic coordinates 5° 18’ N latitude , 3° 4’ W longitude ) , Blockhauss ( 5° 19 N , 4° 0’ W ) , and Treichville ( 5° 18 N , 4° 0’ W ) , representing an increasing urbanization gradient ( Fig 1 ) . The degree of urbanization is characterized by land use , vegetation coverage , human population density , state of roads , and public services , as described in Zahouli et al . [16] . Natural and artificial containers such as tree holes , bamboo , fruit husks , tires , discarded items , and water storage receptacles that may serve as potential breeding sites for Aedes mosquitoes vary according to human habitation and activities . The rural area is surrounded by farms of palm oil trees ( Eleasis guineensis ) covering 11 , 444 ha and a preserved rainforest of 100 ha , while the suburban area is located about 2 km away from the Banco National Park with over 3 , 750 ha of rainforest . The rainforest is inhabited by a diverse fauna ( e . g . , primates and birds ) that serve as hosts for Aedes mosquitoes . The climate is characterized by high temperature and precipitation with two rainy seasons . The seasons are distinguished by rainfall rather than temperature . The main rainy season extends from May to July , while the shorter rainy season occurs from October to November , with distinct dry seasons in between . The average annual precipitation ranges from 1 , 200 to 2 , 400 mm . The annual average temperature and relative humidity are around 26 . 5°C and 80–90% , respectively . Aedes larval breeding sites were sampled quarterly in domestic ( space inhabited by humans ) and peri-domestic ( surrounding vegetated environment within a 600 m radius from the domestic areas ) sites in rural , suburban , and urban areas from January 2013 to October 2014 . While water-holding containers , tree holes , and bamboo were repeatedly sampled , other potential breeding sites were sampled for the presence of immature stages of Aedes mosquitoes . All accessible properties were surveyed simultaneously in the three settings . Some properties could not be sampled because the residents refused to provide access or because there were physical barriers of access . Potential larval breeding sites of Aedes mosquitoes were sampled in all three study sites by teams consisting of four trained mosquito collectors in each study area . Each mosquito collector team was composed of the same persons during all surveys . The number of experienced mosquito collectors was constant on any one day in each study area , whereas the teams made rotations from one study area to another in order to ensure similar sampling efforts and efficiency in the three study areas , and minimize potential biases . The collectors worked from 08:00 to 16:00 hours , and spent proportionally equal time periods searching for potential mosquito breeding sites in the study areas . Readily visible and accessible containers in the selected households and surrounding premises were examined for the presence of water and mosquito larvae . In a preliminary survey , existing larval breeding sites , such as natural and artificial cavities or containers with a potential to contain water were kept in an inventory and assigned a unique label . Based on this preliminary survey , potential breeding sites were classified into two categories , three sub-categories , and 16 types , depending on their location , origin , material , and container type ( Table 1 and S1 Fig ) . The breeding sites were assessed for abiotic and biotic characteristics , including geographic location ( domestic and peri-domestic sites ) , color , exposure to sunlight ( full shade , no exposure to sunlight; partial shade , partial exposure to sunlight; no shade , permanent exposure to sunlight ) , turbidity ( transparent/clear , colored , opaque ) , substrate type ( no substrate , foliage , moss , soil ) , surface of water , depth , presence of mosquito larvae , and predators ( larvae of Cx . tigripes , Eretmapodites spp . , and Toxorhynchites spp . mosquitoes , toad tadpoles , and arachnids ) . Larvae and pupae of Aedes mosquitoes were sampled using the World Health Organization ( WHO ) standard equipment adapted to the aperture and the depth of larval habitats . A flexible collection tube connected to a manual suction pump was used to sample water from bromeliads and bamboo holes . Scoops of 350 ml capacity were used to collect immature mosquitoes from larger breeding sites ( e . g . , tree holes , discarded containers , tires , and puddles ) . The collected Aedes mosquito were counted using a pipette and classified as young larvae ( 1–2 instar ) , old larvae ( 3–4 instar ) , and pupae . Non-Aedes mosquito larvae such as Anopheles spp . , Coquelitidia spp . , Culex spp . , Eretmapodites spp . , Filcabia spp . , Toxorhynchites spp . , and Uranotenia spp . were also recorded . The predacious larvae of mosquitoes , such as Cx . tigripes , Eretmapodites spp . , and Toxorhynchites spp . were removed from the samples to avoid predation on the other species and preserved separately . All mosquito samples were stored separately in plastic boxes and transported in a coolbox to a field laboratory . In the laboratory , mosquito larvae were reared until they reached the adult stage . In order to minimize mortality , a maximum of 20 larvae were placed in 200 ml plastic cups , filled with 150 ml distilled water and covered with netting . Larvae of Aedes and other mosquitoes were fed each morning between 07:00 and 08:00 hours with Tetramin Baby Fish Food . Predacious larvae of Toxorhynchites spp . and Cx . tigripes were fed with larvae from colonies sampled from the study areas . Emerging adult mosquitoes were identified to species level using a morphological key [17] . As larval mortalities were low , the proportion of mosquito species was estimated on the basis of emerging adults . Adult specimens were stored by species and recorded in an entomology collection database . The frequency of Aedes-positive breeding sites ( FP ) was calculated as the percentage of water holding containers with at least one larva or pupa ( numerator ) among the wet containers ( denominator ) . The proportion of Aedes-positive breeding site types among the Aedes-positive breeding sites ( PP ) was expressed as the percentage of each Aedes-positive container type ( numerator ) among the total Aedes-positive containers ( denominator ) in each study area . To test whether there was a difference in the number of positive breeding sites and the number of available wet containers in each category , we used Fisher’s exact test and χ2 , as appropriate , to test for differences in the frequency of Aedes-positive breeding sites across the three study areas , between the domestic and peri-domestic sites , and between dry and rainy seasons . Aedes species proportions were calculated as the percentage of specimens belonging to the genus Aedes for each study area and then compared between breeding sites as above . Larval abundances of Aedes mosquitoes were standardized as the mean numbers of larvae per liter of water , expressed as the geometric mean , known as Williams’ mean ( i . e . , log[number of mosquito larvae + 1] ) [18] , and compared using the Kruskal-Wallis test , followed by Mann-Whitney . The Mann-Whitney U test was also performed to compare pairs of study areas when the Kruskal-Wallis H test showed a significant difference or only two habitats . Aedes species richness was defined as the number of collected species in each study area and compared using a one-way analysis of variance ( ANOVA ) , followed by the Tukey post-hoc test for post-hoc pairwise comparisons [19] . Aedes species diversity and dominance were estimated using the Shannon-Weaver index [20] and Simpson index [21] and analyzed using a Kruskal-Wallis test . Kruskal-Wallis test was performed because a test for normality showed a significant difference in the variances after log-transforming the data . A significance level of 5% was set for statistical testing . All statistical analyses were conducted using Stata version 14 . 0 ( Stata Corporation; College Station , TX , United States of America ) .
Table 2 shows the species composition of adult mosquitoes that emerged from the larvae and pupae sampled from the breeding sites along the rural-to-urban gradient in south-eastern Côte d’Ivoire and reared after transfer to the laboratory . In total , 7 , 661 , 16 , 931 , and 26 , 968 adult mosquitoes emerged from the collected larvae in rural , suburban , and urban areas , respectively . The rural setting had the highest mosquito species diversity ( eight genera and 37 species ) , followed by the suburban setting ( four genera and 14 species ) , and the urban setting ( three genera and nine species ) . The genus Aedes predominated throughout , with proportions of 80 . 40% ( n = 7 , 661 ) in rural , 84 . 75% ( n = 16 , 931 ) in suburban , and 85 . 19% ( n = 26 , 968 ) in urban settings . The rural setting had the largest number of Aedes species ( 17 species ) , followed by the suburban ( eight species ) and urban settings ( three species ) . The predacious mosquito species Cx . tigripes was sampled in each of the three study settings , while the predators Eretmapodites chrysogaster , Er . inornatus , and Toxorhynchites brevipalpis were primarily collected in rural settings . Moreover , several other vector competent mosquito species , namely Anopheles coustani , An . gambiae , Coquelettidia fuscopennata , Cx . quinquefasciatus , and Cx . poicilipes were sampled . Table 3 summarizes the species composition of Aedes mosquitoes collected as larvae among different types of breeding sites in the rural , suburban , and urban areas . Ae . aegypti and Ae . vittatus were commonly encountered in the three settings . Ae . aegypti was the most prevalent species in the all study areas , and exhibited rising abundance from rural ( n = 6 , 159; 75 . 12% ) to suburban ( n = 14 , 347 , 93 . 94% ) , and urban ( n = 22 , 974 , 99 . 37% ) areas . The highest prevalence of Ae . vittatus ( 5 . 18% ) was found in suburban areas . In rural areas , Ae . furcifer ( 4 . 53% ) , Ae . palpalis ( 3 . 96% ) , Ae . dendrophilus ( 3 . 83% ) , Ae . vittatus ( 2 . 83% ) , Ae . africanus ( 2 . 31% ) , Ae . luteocephalus ( 1 . 49% ) , Ae . metallicus ( 1 . 28% ) , Ae . lilii ( 1 . 22% ) , and Ae . unilineatus ( 1 . 20% ) were collected at frequencies above 1% . We also found two specimens of Ae . albopictus ( 0 . 01% ) in the urban settings . The presence of Aedes mosquito larvae in breeding sites significantly varied by species ( Table 3 ) . For example , Ae . aegypti were found in all types of Aedes-positive breeding sites sampled in all the three study areas . Moreover , Ae . dendrophilus , Ae . furcifer , and Ae . luteocephalus were found in all container types in the rural areas , while Ae . vittatus and Ae . metallicus were collected from both natural and artificial containers in the suburban areas . Ae . africanus , Ae . lilii , Ae . unilineatus , and Ae . usambara were mostly present in natural containers such as tree holes , bamboo , and fruit husks in rural settings . Several species were found together in the same breeding sites . For example , Ae . aegypti , Ae . dendrophilus , Ae . furcifer , and Ae . africanus shared the same breeding sites in the rural areas , whereas Ae . aegypti co-existed with Ae . vittatus in suburban settings ( n = 1 , 295 , 12 . 8% ) . These two species co-occurred , albeit at low frequency ( n = 57 , 0 . 3% ) in urban breeding sites . Additionally , Cx . quinquefasciatus and An . gambiae were often collected together with Ae . aegypti in tires and discarded containers in peri-domestic environments in the three study areas . Mosquito predators , such as Cx . tigripes , Er . chrysogaster , and Tx . brevipalpis were found in the same breeding sites as Ae . aegypti , Ae . dendrophilus , Ae . furcifer , and Ae . africanus in rural settings . These ecologic associations were most present in tree holes , discarded containers , and tires in the rural areas and in peri-domestic breeding sites during the rainy season . Among 3 , 569 , 4 , 882 , and 5 , 783 containers inspected in rural , suburban , and urban settings , 2 , 423 , 3 , 069 , and 3 , 374 were wet , respectively . The urban setting had a significantly higher Aedes-positive breeding site rate ( 2 , 136/3 , 374 , FP = 63 . 3% ) , as compared to suburban ( 1 , 428/3 , 069 , FP = 46 . 5% ) and rural settings ( 738/2 , 423 , FP = 30 . 5% ) ( χ2 = 478 . 9 , df = 2 , p < 0 . 05 ) ( S1 Table ) . The Mann-Whitney U test indicated that the abundance of immature Aedes mosquitoes in one study area was significantly different compared to another . A significantly higher abundance of immature Aedes mosquitoes was found in urban areas with larval densities of 1 . 26 ± 0 . 01 larvae/l , followed by the suburban areas with 0 . 77 ± 0 . 01 larvae/l and rural areas with 0 . 42 ± 0 . 01 larvae/l ( χ2 = 663 . 3 , df = 2 , p < 0 . 001 ) ( Table 4 ) . Urban settings showed significantly higher proportions of pupae ( n = 23 , 126 , 14 . 9% ) and 3–4 instar larvae compared to rural setting with 9 . 6% ( n = 6 , 212 ) of pupae and 47 . 8% of 3–4 instar larvae ( p < 0 . 05 ) . The presence of immature Aedes mosquitoes was significantly associated with the sites , seasons , breeding site types and categories , substrates , color , vegetal detritus , shade , water turbidity , and predators ( p < 0 . 05 ) . Fig 2 shows that the Aedes-positive microhabitat rate varied widely from one breeding site type to another in all three areas . The rural area showed the largest variability in Aedes breeding sites grouped into 16 types , followed by the suburban and urban areas presenting 15 and 12 microhabitat types , respectively . S1 Table indicates that immature Aedes mosquitoes were found in both natural ( 163/738 , PP = 22 . 1% ) and artificial ( 575/738 , PP = 77 . 9% ) breeding sites in the rural , and mostly in artificial breeding sites in the suburban ( 1 , 405/1 , 428 , PP = 98 . 4% ) and urban ( 2 , 129/2 , 136 , PP = 99 . 7% ) areas , including higher proportions of industrial containers in the urban areas ( 2 , 066/2 , 136 , PP = 96 . 7% ) . In the rural areas , the main Aedes-positive breeding sites represented natural types , such as three holes ( 62/69 , FP = 89 . 9% ) , bamboo ( 17/45 , FP = 37 . 8% ) , and fruit husks ( 59/195 , FP = 30 . 3% ) , traditional containers such as metallic ( 27/44 , FP = 61 . 4% ) and clay pots ( 44/101 , FP = 43 . 6% ) and wood-containers ( 24/69 , FP = 34 . 8% ) ; and industrial containers such as tarps ( 41/66 , FP = 62 . 1% ) , tires ( 183/324 , FP = 56 . 5% ) , vehicle tanks ( 41/84 , FP = 48 . 8% ) , discarded containers ( 104/254 , FP = 40 . 9% ) , and vehicle carcasses ( 68/171 , FP = 52 . 0% ) . In the urban setting , the most common Aedes breeding sites comprised of industrial containers such as tires ( 1 , 087/1 , 236 , FP = 87 . 9% ) , discarded containers ( 601/767 , FP = 78 . 4% ) , vehicle tanks ( 77/94 , FP = 81 . 9% ) , vehicle carcasses ( 91/131 , FP = 69 . 5% ) , and water storage containers ( 141/896 , FP = 15 . 7% ) . Water storage containers were found to be more frequently infested with immature stages of Aedes mosquitoes in the urban than in the suburban ( χ2 = 17 . 3 , df = 1 , p < 0 . 001 ) or rural settings ( χ2 = 57 . 3 , df = 1 , p < 0 . 001 ) . Furthermore , there was a statistically significant difference in Aedes mosquito positivity rate in water storage container between the suburban and rural settings ( χ2 = 15 . 8 , df = 1 , p < 0 . 001 ) . Besides the variations in the frequency in the colonization of Aedes breeding sites , the most abundant Aedes breeding sites were tires and discarded containers in all the study areas ( all p < 0 . 05 ) ( Fig 3 ) . Also frequently positive were natural breeding sites such as tree holes ( 62/738 , PP = 8 . 4% ) , fruit husks ( 59/738 , PP = 8 . 0% ) , industrial containers such as tarps ( 41/738 , PP = 5 . 6% ) , vehicle tanks ( 41/738 , PP = 5 . 6% ) , and vehicle carcasses ( 68/738 , PP = 9 . 2% ) in the rural area , and water storage containers ( 141/2 , 136 , PP = 6 . 6% ) in the urban area ( Fig 3 ) . Table 4 summarizes the abundance , richness , diversity , and dominance of Aedes mosquito species according to the breeding site types among sites and study areas . The Shannon’s diversity and Simpson’s dominance indices highly varied between the study areas and breeding sites , showing higher overall values in peri-domestic environments . The highest larval abundances of Aedes mosquitoes were recorded in tires in all study areas ( p < 0 . 05 ) . In addition , tree holes and metallic pots in the rural , vehicle tanks and building tools in the suburban , and discarded containers , vehicle tanks , and vehicle carcasses in the urban areas were also highly productive breeding sites for Aedes mosquito ( S2 Fig ) . Aedes species richness was significantly different among the microhabitats in the rural ( F = 4 . 3 , df = 16 , p < 0 . 001 ) , suburban ( F = 9 . 2 , df = 7 , p < 0 . 001 ) , and urban settings ( F = 11 . 1 , df = 2 , p < 0 . 001 ) . Significantly higher numbers of species ( 13 species ) were found in tree holes in the rural areas . The rural areas showed the highest species diversity , as demonstrated by a Shannon’s diversity index of 1 . 64 , followed by 0 . 38 for the suburban and 0 . 06 for the urban areas . Among the breeding sites , the highest Shannon’s diversity index was found in the rural areas for the tree holes with a value of 3 . 13 . Conversely , Simpson’s dominance index of Aedes species significantly decreased from the urban ( 0 . 99 ) to suburban ( 0 . 89 ) and rural ( 0 . 57 ) areas ( F = 16 . 2 , df = 3 , p < 0 . 001 ) . Table 5 shows that the proportion of breeding sites positive for Aedes larvae significantly varied across the peri-domestic and domestic sites in all study areas . Overall , compared to domestic environment , peri-domestic sites showed a higher proportion of significantly Aedes-positive breeding sites , with FP of 84 . 8% ( 1 , 753/2 , 066 ) in urban ( χ2 = 1 , 100 , df = 1 , p < 0 . 001 ) , 70 . 2% ( 1 , 176/1 , 676 ) in suburban ( χ2 = 829 . 2 , df = 1 , p < 0 . 001 ) , and 42 . 6% ( 636/1 , 492 ) in rural ( χ2 = 271 . 5 , df = 1 , p < 0 . 001 ) areas . In rural areas , 87 . 7% ( 143/163 ) of the natural breeding sites that hosted Aedes larvae were located in the peri-domestic sites . High numbers of tires were found infested in the domestic site , with FP of 66 . 5% ( 151/227 ) Aedes-positive breeding sites in the urban , and 35 . 8% ( 63/176 ) in the suburban area . In all study areas , the proportion of Aedes-positive breeding sites and the number of larvae varied significantly over time with more breeding sites being positive during the rainy season ( Fig 4 and S3 Fig ) . During the rainy season , proportionally more breeding sites were positive . The frequencies of Aedes-positive breeding sites were 69 . 6% ( 1 , 650/2 , 369 ) in the urban ( χ2 = 137 . 7 , df = 1 , p < 0 . 001 ) , 52 . 9% ( 1 , 196/2 , 263 ) in the suburban ( χ2 = 138 . 4 , df = 1 , p < 0 . 001 ) , and 34 . 6% ( 642/1 , 857 ) in the rural ( χ2 = 63 . 5 , df = 1 , p < 0 . 001 ) areas ( S2 Table ) . Significantly more Aedes-positive breeding sites were observed during the rainy season in the rural , urban , and suburban areas , with FP of 40 . 0% ( 187/468 ) and 72 . 0% ( 521/724 ) in July 2013 , and 56 . 6% ( 327/578 ) in October 2013 , respectively ( S3 Fig ) . Moreover , higher densities of immature Aedes mosquitoes were recorded in July 2013 with 0 . 62 ± 0 . 03 and 1 . 70 ± 0 . 03 larvae/l in the rural , urban and suburban areas , respectively , and in October 2013 with 1 . 02 ± 0 . 02 larvae/l ( Fig 5 ) . There were significant differences in the highest Aedes microhabitat rates ( χ2 = 121 . 2 , df = 2 , p < 0 . 001 ) and the highest abundance ( χ2 = 156 . 5 , df = 2 , p < 0 . 001 ) between the three study areas . The highest frequency ( i . e . , 352/393 , FP = 89 . 6% ) of Aedes-positive breeding sites was observed in the peri-domestic sites in the urban areas during the rainy season in October 2013 .
When designing strategies to monitor and control Aedes arbovirus vectors in their breeding sites , failure to identify the broad spectrum of potentially available breeding sites will bias the results from field sampling and will thus negatively affect the impact of larval control interventions . Our study pertaining to larval habitats of Aedes mosquitoes alongside a rural-to-urban gradient within yellow fever and dengue co-endemic areas in the south-eastern part of Côte d’Ivoire provided strong evidence for influence on species structure , breeding sites , and biological interactions among the immature forms ( Fig 6 ) . Compared to a previous study conducted in the same area of Côte d’Ivoire [16] , the current study identified 11 additional Aedes species ( i . e . , Ae . albopictus , Ae . angustus , Ae . apicoargentus , Ae . argenteopunctatus , Ae . haworthi , Ae . lilii , Ae . longipalpis , Ae . opok , Ae . palpalis , Ae . stokesi , and Ae . unilineatus ) and 16 additional non-Aedes species that may influence arbovirus transmission patterns . To our knowledge , Aedes mosquito species such as Ae . lilii , Ae . stokesi , and Ae . unilineatus , and others such as Cq . fuscopennata and Tx . brevipalpis appear to be reported for the first time in Côte d’Ivoire . Ae . albopictus is not native to Côte d’Ivoire , but has previously been reported [22] . Presumably this species has been introduced through the seaport bordering the urban municipality of Treichville . The higher numbers of Aedes species is likely due to abundant presence of natural and artificial breeding sites , and their potentials to provide suitable microenvironments . Gravid Aedes females select oviposition sites according to their physical , chemical , and biological characteristics [11 , 12] and these may change in space and time over the year [16] . The public health relevance of Aedes mosquitoes results from their invasiveness and ecologic plasticity , competence for multiple pathogens , potential as bridge vectors due to their opportunistic feeding behavior and adaptation to urban , rural , and forest areas [23] . Almost all of the container-specialist Aedes mosquitoes collected as larvae such as Stegomyia subgenus , including Ae . aegypti , Ae . africanus , Ae . albopictus , Ae . angustus , Ae . apicoargenteus , Ae . luteocephalus , Ae . metallicus , Ae . opok , Ae . vittatus , Ae . unilineatus , and Ae . usambara species , and Diceromyia and Aedimorphus subgenera comprising respectively Ae . furcifer and Ae . stokesi species have been shown to carry and/or to transmit in nature over 24 viruses , including yellow fever , dengue , Zika , chikungunya , and Rift Valley in tropical regions [5 , 6] . In addition , Ae . ( Aedimorphus ) argenteopunctatus in South Africa [24] and Ae . ( Neomelaniconion ) palpalis [25] which show vector competence for Rift Valley fever virus in vitro and the other Aedes species like Ae . ( Stegomyia ) dendrophilus , Ae . ( Stegomyia ) lilii and Ae . ( Aedimorphus ) haworthi which belong to the same subgenera of species involved in the transmission of the arboviruses thus could be suspected as potential vectors of diseases . Still , Ae . ( Finlaya ) longipalpis belonging to the same Finlaya subgenus with Ae . niveus which has been the principal vector of dengue virus in Malaysia [26] may potentially transmit arboviruses in Côte d’Ivoire . Among non-Aedes mosquitoes , Er . chrysogaster , Er . inornatus and Cq . fuscopennata have been found to have natural infection , while Er . quinquevittatus has exhibited laboratory competence with yellow fever virus in Africa [6] . Moreover , An . coustani has been found to be infested with Zika virus [27] , while O’nyong-nyong and chikungunya viruses have been isolated from An . gambiae [28] . Cx . quinquefasciatus [25] and Cx . poicilipes [26] have been shown susceptible to transmit Rift Valley fever virus . In conclusion , as in Senegal [12] , the collections of immature stages of non-anthropophagic , unexpected and new potential vectors in rural areas suggest the co-existence of several still unidentified arbovirus cycles in south-eastern Côte d’Ivoire . Our results also revealed that , urban areas showed higher capacity to support Aedes breeding sites and larvae than suburban and rural areas . The higher numbers of positive breeding sites and higher abundance of Aedes mosquito larvae may be due to the destruction of natural vegetation coverage for infrastructure buildings in the urbanized areas that may affect biological factors ( e . g . , fauna and flora ) , and increase the radiation budget thus modifying the microenvironments within and around the microhabitats [29] . Increased exposure to sunlight probably accelerates Aedes mosquito larval development and thus increases the size of adult vectors that possibly find more opportunities of blood feeding sources from larger human populations in urban areas [16 , 29] . Still , urban Aedes populations are probably less exposed to the pressures from agricultural insecticide and predators ( e . g . , Eretmapodites spp . and Toxorhynchites spp . ) compared to rural communities . We also found that less than two-thirds of breeding sites were infested with Aedes larvae thus suggesting that not all available containers filled with water were occupied by at least one larva or pupa of Aedes mosquitoes and the immature Aedes mosquitoes were not randomly distributed [12] . The presence of empty containers might imply that the gravid females of Aedes mosquitoes select their egg-laying sites carefully according to their physical characteristics ( e . g . , depth , color , clearance , surface , location , height , shade , sun exposure , and food sources ) [12 , 29] , and biological interactions ( e . g . , competition and predation ) [10 , 11 , 30] at play within the water-holding container systems . In our larval surveys , we documented distinct geographic and seasonal variations in terms of the proportions of positive breeding sites and abundance of Aedes mosquitoes in all areas . Indeed , the highest proportions and relative abundance of Aedes mosquitoes were observed among vegetated peri-domestic breeding sites and during the rainy seasons in all areas . The shade of the vegetation reduces the water temperature [12] , thus protecting breeding sites from drying out . Moreover , leaves supply organic detritus and associated microorganisms that may serve as food sources for the mosquito larvae [10] . The geographic and seasonal patterns in Aedes breeding sites are important from an epidemiologic perspective and suggest that the rainy season is the best period of time to identify breeding sites , while during the dry season it would be an ideal period of time to control immature Aedes mosquitoes , with particular attention for peri-domestic environments . Our data revealed that the pattern of Aedes mosquito breeding sites changes substantially from natural containers to artificial containers along a rural-to-urban gradient . Although artificial breeding sites dominate in all areas , there is a higher proportion of natural containers ( e . g . , rock holes , animal detritus , leaf axils , fruit husks , bamboo , and tree holes ) in rural areas , traditional containers ( e . g . , clay pots , wood-containers , and metallic pots ) in suburban areas . However , in the urban areas , the most productive breeding sites for Aedes mosquito were industrial containers ( e . g . , tarps , discarded tires , vehicle tanks , carcasses , building tools , and water storage containers ) . The availability of , and the segregation among , Aedes breeding sites probably result from the strong impacts of human activities on the environment , while the natural breeding sites are provided by the natural landscape and agriculture [12] . We observed that tree holes , tires , and water storage containers showed higher Aedes species richness in rural , higher Aedes abundances in all areas , and high Ae . aegypti infestation rates in urban areas , respectively . Tree holes , found in the preserved rainforest , seem to provide ideal larval habitats for several species due to their greater stability , various trophic inputs , and retention of rainwater for longer periods of time [12] . Used tires are mostly associated with the palm oil industry in rural areas , production of the local dish “Attiéké” in suburban areas , and selling of tires and car repairs in urban areas . Tree holes and tires have bigger volumes and are expected to better protect the immature forms of Aedes mosquitoes against flushing during heavy rains [12 , 14] . Moreover , tires are black-colored containers that are highly attractive to the gravid Aedes females searching for oviposition sites [11 , 31] . The high number of water barrels infested with Aedes larvae might be due to the water being held for longer periods in uncovered receptacles [32] . Taken together , Aedes species diversity , richness , abundance , and dominance significantly changed from rural to urban settings . The variations in Aedes mosquito species may be explained by the sensitivity of their larvae to environmental changes induced by urbanization [10 , 12] . Native species such as Ae . africanus , Ae . argenteopunctatus , Ae . longipalpis , Ae . stokesi and Ae . usambara were restricted to natural breeding sites in the rural areas . However , other wild species , such as Ae . furcifer , Ae . dendrophilus , Ae . palpalis , Ae . vittatus , Ae . luteocephalus , and Ae . metallicus were also surprisingly frequent in artificial containers . In contrast , our surveillance failed to sample Ae . fraseri that have been collected by ovitraps in the rural areas previously [16] , probably due to its possible cryptic breeding sites or potential height-dependent oviposition behavior . The existence of multiple types of behavior in the same Aedes mosquito species may indicate the existence of generalist species or sibling strains of individuals from various origins [6 , 11] that have experienced different selective urbanization pressures . Lastly , our study showed that urbanization acts as a series of ecological filters for Aedes mosquitoes by advantaging Ae . aegypti , the primary vector of yellow fever , dengue , chikunguya , and Zika viruses [1–3] . Ae . aegypti was the most prevalent species in all study areas , exhibiting an increasing abundance along rural-to-urban gradient towards an higher abundance in urban areas where larvae mostly inhabit in anthropogenic containers ( e . g . , tires , discarded containers ) . Ae . aegypti displayed behavioral plasticity in that the females lay eggs in a vast array of containers ranging from natural containers such as rock holes , tree holes , and bamboo to a wide range of man-made containers [11] , including water storage containers in urban areas [32] . The ecologic variations in oviposition behavior of Ae . aegypti and other Aedes mosquitoes may be discussed in ecologic , evolutionary , and epidemiologic approaches [11] , and suggest possible overlaps of sylvan and urban vector distributions thus linking several potential mixed arbovirus transmission cycles [5 , 6 , 12 , 16] . In addition , if highly infested microhabitats are targeted for removal , Aedes mosquito females may possibly adapt to changes in breeding habitats and alternatively oviposit in other containers previously unoccupied [33] . The ability of Ae . aegypti to adapt ovipositional behaviors to changing environments possibly enabling to overcome ecological constraints ( e . g . , instability and disturbance of the breeding sites ) imposed by urbanization [10 , 11] . Ae . aegypti-transmitted yellow fever outbreaks are historically well known in Côte d’Ivoire to have forced the transfer of the capital from Grand-Bassam to Abidjan in 1899 [15] . Since then , several unpredictable resurgences of yellow fever and dengue have been occurring in rural and urban areas causing many suspected , confirmed and fatal cases , and remain presently an unresolved major public health concern [7 , 15 , 34] , with the current outbreak of dengue DENV-3 resulting in one confirmed and 17 suspected cases recorded in Abidjan in May 2017 . Our study suggests that the unique removal of artificial containers that is a common practice in arbovirus control programs in Côte d’Ivoire might not effectively control diseases in the south-eastern part of the country . Vector control measures should combine removals of artificial containers [6] and autocidal gravid ovitrap-based on mass trapping [35] , and insecticide auto-dissemination approaches [36] .
In south-eastern Côte d’Ivoire , urbanization is associated with larval habitats of Aedes species at a finer scale by driving their breeding sites from natural to artificial containers , and at the larger scale by transforming rural to urban areas . Ae . aegypti is most prevalent in urban areas , suggesting that urbanization is a driver for producing suitable breeding sites for this mosquito species , and hence related disease outbreaks . However , rural settings still support irremovable containers such as natural breeding sites ( e . g . , tree holes ) that host several wild Aedes species and Ae . aegypti . Therefore , even effective removal of discarded containers in urban areas ( a common practice in arbovirus control programs ) might not be sufficient to control arboviral diseases . Instead , vector control strategies should embrace a more holistic approach , combining different tools and methods of proven efficacy [6 , 35 , 36] .
|
Outbreaks of yellow fever and dengue caused by Aedes mosquitoes have been repeatedly reported in rural and urban areas in humid tropical Africa , including Côte d’Ivoire . Although controlling immature stages of Aedes mosquitoes in their aquatic habitats before they become adult vectors remains the best method to fight arboviral diseases , failure to identify the larval habitats can compromise intervention success . We studied the larval ecology of Aedes mosquitoes in different settings ( rural , suburban , and urban ) in Côte d’Ivoire . We found that the degree of urbanization was significantly associated with Aedes breeding sites . Compared with rural areas , urban and suburban areas were characterized by high numbers of Aedes mosquito breeding sites; mostly artificial containers ( e . g . , tires and discarded containers ) that were inhabited by the larvae of Ae . aegypti . In rural areas , natural containers ( e . g . , tree holes and bamboos ) harbored several other Aedes species not found elsewhere . Our results suggest that removal of discarded containers–a common practice in arbovirus control programs–in urban areas does not suffice for controlling arboviral diseases because urban areas remain exposed to ( re ) infestation due to natural containers that host several Aedes species in rural areas . Additional vector control strategies are required .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion",
"Conclusions"
] |
[
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"ecology",
"and",
"environmental",
"sciences",
"animals",
"developmental",
"biology",
"animal",
"behavior",
"rural",
"areas",
"insect",
"vectors",
"zoology",
"plants",
"infectious",
"diseases",
"geography",
"grasses",
"aedes",
"aegypti",
"behavior",
"life",
"cycles",
"disease",
"vectors",
"insects",
"arthropoda",
"mosquitoes",
"urban",
"ecology",
"urban",
"areas",
"ecology",
"animal",
"sexual",
"behavior",
"earth",
"sciences",
"geographic",
"areas",
"biology",
"and",
"life",
"sciences",
"species",
"interactions",
"bamboo",
"larvae",
"organisms"
] |
2017
|
Urbanization is a main driver for the larval ecology of Aedes mosquitoes in arbovirus-endemic settings in south-eastern Côte d'Ivoire
|
Candida spp . can cause severe and chronic mucocutaneous and systemic infections in immunocompromised individuals . Protection from mucocutaneous candidiasis depends on T helper cells , in particular those secreting IL-17 . The events regulating T cell activation and differentiation toward effector fates in response to fungal invasion in different tissues are poorly understood . Here we generated a Candida-specific TCR transgenic mouse reactive to a novel endogenous antigen that is conserved in multiple distant species of Candida , including the clinically highly relevant C . albicans and C . glabrata . Using TCR transgenic T cells in combination with an experimental model of oropharyngeal candidiasis ( OPC ) we investigated antigen presentation and Th17 priming by different subsets of dendritic cells ( DCs ) present in the infected oral mucosa . Candida-derived endogenous antigen accesses the draining lymph nodes and is directly presented by migratory DCs . Tissue-resident Flt3L-dependent DCs and CCR2-dependent monocyte-derived DCs collaborate in antigen presentation and T cell priming during OPC . In contrast , Langerhans cells , which are also present in the oral mucosa and have been shown to prime Th17 cells in the skin , are not required for induction of the Candida-specific T cell response upon oral challenge . This highlights the functional compartmentalization of specific DC subsets in different tissues . These data provide important new insights to our understanding of tissue-specific antifungal immunity .
Opportunistic fungal infections cause an increasing medical problem due to the progression in immunosuppression worldwide [1] . Candida spp . present in the normal human microbiota can cause mucocutaneous infections when cellular immune barriers of the host are breached . As such , HIV+ individuals with low T cells counts are often affected by oropharyngeal candidiasis ( OPC ) [2] , indicating that CD4+ T cells play a critical role in preventing disease symptoms . Candida-specific memory T helper cells are found in all healthy individuals that have been exposed to the fungus in the normal human microflora and interestingly , they belong predominantly to the subset of Interleukin 17 ( IL-17 ) -secreting Th17 cells [3] . The notion that IL-17 plays a key role in protection from fungal infections is further supported by the identification of rare families of patients , in which inborn errors in genes linked to the IL-17 pathway are associated with chronic and recurrent forms of mucocutaneous candidiasis [4] . Although the relevance of Th17 cells in protection from Candida is well-documented , the regulation of these cells remains ill-defined . This gap in knowledge is entailed ( among other things ) by the limited information available about Candida-derived T cell epitopes . Out of the >1015 different T cell receptors ( TCRs ) that are theoretically generated by gene segment rearrangement , only a minute proportion recognizes Candida-derived antigens . The difficulty to identify these few antigen-specific T cells within the entire polyclonal repertoire hampers the study of their activation and differentiation process . The use of TCR transgenic T cells proved useful to elucidate diverse aspects of adaptive immunity in experimental systems of infectious and non-infectious diseases . However , only few TCR transgenic mouse lines specific for clinically relevant pathogens including fungi [5 , 6] exist to date . To circumvent this limitation , well-established TCR transgenic T cells specific for model antigens such as ovalbumin or the I-Eα chain have been used in combination with infectious agents engineered to express these model antigens . Although such systems are useful to interrogate the activation of antigen-specific T cells in the context of an infectious setting , they have important limitations , such as restricted availability , processing and presentation of model antigens during T cell priming that result in the inefficient generation of effector and memory T cells [7] . TCR transgenic T cells which recognize endogenous antigen thus represent an important advantage to functionally analyze the pathogen-specific T cell response at high resolution and in a physiological context . No TCR transgenic mouse specific for Candida spp . exists to date . Differentiation of naive T cells into effector T cells depends on antigen presentation , co-stimulation and polarizing cytokines provided in cis by antigen presenting cells ( APCs ) [8] . In the context of Candida infection , Syk- and Card9-coupled C-type lectin receptors including Dectin-1 and Dectin-2 are relevant for the induction of Th17-inducing cytokines in response to fungal recognition [9 , 10] . Dectin-1 and Dectin-2 are broadly expressed by diverse subsets of mononuclear phagocytes ( MNPs ) , many of which can potentially serve as APCs for Th17 induction . MNPs comprise monocytes , macrophages and dendritic cells ( DCs ) . Although they are all derived from a common macrophage and DC progenitor ( MDP ) , MNPs comprise developmentally and functionally distinct cellular subsets in different tissues , which are difficult to unambiguously distinguish on the basis of their phenotype [11] . Ly6Chi monocytes differentiate from MDPs and egress from the bone marrow in a CCR2-dependent manner [12] . After entering tissues they can give rise to monocyte-derived DCs expressing high levels of CD11c and MHC II under the influence of M-CSF in inflammatory conditions [13] . MDP can also give rise to common DC progenitors ( CDPs ) , which develop in response to Flt3 signaling and give rise to two distinct subsets of DCs: Batf3-independent and Batf3-dependent DCs , the latter of which comprises lymphoid tissue CD8α+ DCs and non-lymphoid-tissue CD103+ CD11b- DCs [14] . In the skin , Langerhans cells ( LCs ) constitute a special case . Seeded before birth from fetal liver monocytes [15] they maintain themselves under steady state conditions by self-renewing from local precursors [16] . Non-lymphoid tissue DCs migrate from the periphery and carry antigens for presentation to T cells in the draining lymph nodes and when activated by inflammatory or infectious stimuli promote the generation of antigen-specific effector T cells . The oral mucosa shares features with other mucosal tissues and the skin , but it constitutes a unique tissue with its own cellular composition and function [17] . LCs , CD103+ CD11b- DCs and CD11b+ CD103- DCs have all been identified , but their role in immune activation is not well understood . In addition , inflammatory monocytes that give rise to monocyte-derived DCs infiltrate the oral mucosa upon infection and inflammation . Using an experimental model of OPC we show here how these different DC subsets orchestrate the T cell response during oral infection . We made use of a novel TCR-transgenic mouse , whose T cells specifically recognize an endogenous Candida-derived antigen , to functionally determine the presentation capacity of individual APC subsets . We found that both monocyte-derived and Flt3L-dependent conventional DCs carry fungal antigen from the site of infection to the draining cervical lymph nodes where they directly present it to T cells . In a partially redundant manner they instruct the activation and differentiation of Candida-specific T cells into cytokine-producing effector cells in vivo . This indicates that the initiation of an antifungal Th17 response depends on an intricate interplay of different APC subsets in the oral mucosa allowing the generation of a robust response .
All mouse experiments described in this study were conducted in strict accordance with the guidelines of the Swiss and Austrian Animal Protection Law and were performed under protocols approved by the Veterinary office of the Canton Zürich , Switzerland ( license number 184/2009 and 201/2012 ) and by the institutional ethics and animal welfare committee of the University of Veterinary Medicine Vienna ( license number 68 . 205/0258-II/3b/2011 ) . All efforts were made to minimize suffering and ensure the highest ethical and humane standards . C57BL/6J mice ( B6 ) were purchased from Janvier Elevage . Ccr7-/- [18] , Batf3-/- [19] , Ccr2-/- [20] and Flt3l-/- [21] were bred at the Laboratory Animal Service Center ( University of Zürich , Switzerland ) . Langerin-DTR mice [22] were a kind gift from Björn Claussen and Dr . Kordula Kautz-Neu ( Mainz , Germany ) . All mice were on the C57BL/6 background , kept in specific pathogen-free conditions and used at 6–15 weeks of age . In some experiments , mice were treated with diphtheria toxin via the intraperitoneal route ( 10 ng per gram body weight , daily starting from 1 day prior to infection to day +2 post-infection ) . For blocking of CSF1R , 2 mg anti-CSF1R antibody ( clone AFS98 , kindly provided by Melanie Greter , Zürich ) was administered intraperitoneally one day prior infection , followed by a second dose of 1 mg on day 1 post-infection . Splenocytes were isolated from systemically infected B6 mice ( infection on day -24 and day -10 ) and re-activated with GM-CSF-induced bone marrow-derived DCs at a ratio 10:1 in the presence of 2 x 104 heat-killed C . albicans yeast cells . After 3 days cells were fused with BW5147 lymphoma cells ( ATCC #TIB48 ) using polyethylene glycol 1500 ( AppliChem ) [23] selected in hypoxanthine , aminopterin and thymidine ( HAT ) medium ( Invitrogen ) . Specificity of the CD4+ T cell hybridoma for C . albicans was assessed by co-culturing 5 x 104 hybridoma cells with 5 x 104 DC1940 cells that were pulsed with 5 x 104 heat-killed fungi . After 24h supernatants were transferred to 1x104 CTLL-2 and their viability was assessed by the alamar blue cell viability test ( Invitrogen ) following the manufacturer’s instructions . C . albicans-specific hybridoma were subcloned by serial dilution to generate the monoclonal hybridoma cell line 59 . 8 , which was re-screened for specificity . TCR Vα2 and Vβ4 expression was determined by flow cytometry . RNA was isolated form the hybridoma using TRI reagent ( Sigma ) according to the manufacturer's instructions , and cDNA was generated using M-MLV Reverse Transcriptase RNase , H- ( Promega ) . cDNA was amplified with a TCRα-specific primer set [24] and a TCRβ-specific primer set [25] . Sequencing of the PCR products was done by Microsynth and then aligned to the mouse genome using Ensemble database ( http://www . ensembl . org/Mus_musculus ) and analyzed with Immunogenetics Information System ( www . imgt . org ) . The identified Vα2Jα53 and Vβ4Dβ1Jβ1 gene segments were amplified from the genomic DNA using the following primers: Vα2 fwd , 5'-tgacccgggagcttcagtctaggaggaatg-3'; Vα2 rev , 5'-atatcggccgctcctgtaatacttacttg-3'; Vβ4 fwd , 5'-tgtctcgagagagatcctatcctgtgtgacactgctatg-3'; Vβ4 rev , 5'-tgcccgcggcatcccacacccaaagaccctcaggccttaccta-3'; digested with XmaI and NotI or XhoI and SacII , respectively , and cloned into previously described TCR expression vectors [26] . The resulting pTαVα2 and pTβVβ4 were digested with SalI respectively KpnI to excise the transgenes from prokaryotic vector DNA . The isolated linearized fragments were co-injected in equimolar ratios into fertilized C57BL/6N oocytes according to the standard method [27] . The resulting TCR transgenic mouse line selected for experimental use was designated according to the standardized genetic nomenclature for mice: C57BL/6N-Tg ( TcraTcrb ) 603Biat ( Hector ) [28] . It was backcrossed to express the congenic marker Thy1 . 1 and bred at our animal facility Rodent Center HCI . The C . albicans laboratory strain SC5314 was used throughout unless stated otherwise . Clinical isolates of C . albicans , C . dubliniensis , C . krusei , and C . glabrata were obtained from Cristina Fragoso and Orlando Petrini ( Bellinzona , Switzerland ) . All fungal strains were grown in YPD medium at 30°C for 15–18 hours . Mice were infected with 2 . 5 x 106 cfu C . albicans sublingually as described [29] without immunosuppression . In some experiments , mice were treated with 400 μg Fluconazole ( Ratiopharm ) intraperitoneally on day 2 post-infection and 0 . 2 mg/ml Fluconazole ( Sigma-Adrich ) in the drinking water from day 2 post-infection until the mice were sacrificed to prevent fungal overgrowth , which may affect the degree of the T cell response . Mice were monitored for morbidity and weight throughout the course of all experiments . Determining the body weight of infected mice represents a sensitive method for monitoring the control of infection . While all mice sublingually infected with C . albicans strain SC5314 lose 10–15% of their body weight within the first 2 days post-infection , their recovery of the original weight within 5–7 days post-infection correlates with rapid fungal elimination [30] . For determination of fungal burden , the tongue of euthanized animals was removed , homogenized in sterile 0 . 05% NP40 in H2O for 3 minutes at 25 Hz using a Tissue Lyzer ( Qiagen ) and serial dilutions were plated on YPD agar containing 100 μg/ml Ampicillin . For systemic infection , mice were injected intravenously with 5 x 104 cfu C . albicans . Mice were immunized subcutaneously with 50 μg pADH1126-140 ( EMC microcollection ) in Incomplete Freund's Adjuvant ( IFA , Sigma ) mixed with 25 μg CpG ( Microsynth ) . C . albicans strain ATCC14053 was grown YPD medium at 30°C for 16 hours , washed extensively and resuspended in 20mM Na citrate buffer . Samples were autoclaved for 1 . 5h at 121°C and spun at max speed for 15’ . The supernatant , containing highly soluble mannoproteins , was harvested and stored at -20°C . A mix of equal volumes of Fehling solution I ( 7% hydrate copper ( II ) sulfate in 100ml H2O ) and Fehling solution II ( 35% potassium tartrate + 10% NaOH in 100 ml H2O ) was prepared and added to the thawed supernatant in a 1:1 ratio for 30’ . After centrifugation for 15’ at max speed a pellet was obtained that derived from precipitation of mannoproteins . The pellet was dissolved in 3N HCl . Proteins were precipitated upon addition of 8:1 MetOH + Acetic acid , incubation for 1h on a rotating wheal at 4°C and centrifugation ( step repeated twice ) . Finally , two steps of wash/dehydration were performed with MetOH and Ether , respectively . Pellets were dried with a vacuum pump and stored at -80C . Samples were resuspended in water and quantified using Bradford reagent ( Biorad ) prior to use . The C . albicans-specific T cell hybridoma 59 . 8 was maintained in DMEM medium supplemented with 10% FCS , Penicillin/Streptomycin , 2mM Glutamine and 50 μM 2-Mercaptoethanol . The DC cell line DC1940 [31] was kept in IMDM medium , supplemented with 10% FCS , Penicillin/Streptomycin , 2 mM Glutamine and 50 μM 2-Mercaptoethanol . CTLL-2 cells , which are dependent on IL-2 for growth [32] , were maintained in RPMI 1640 medium supplemented with 10% FCS , Penicillin/Streptomycin and 2mM Glutamine and recombinant IL-2 . Mice were anaesthetized with a sublethal dose of Ketamine ( 100 mg/kg ) , Xylazin ( 20 mg/kg ) and Acepromazin ( 2 . 9 mg/kg ) , and perfused by injection of PBS into the right heart ventricle . Tongues were removed , cut into fine pieces and digested with DNase I ( 2 . 4 mg/ml , Roche ) and Collagenase IV ( 4 . 8 mg/ml , Invitrogen ) in PBS for 45 min at 37°C . Single cell suspensions were enriched for leukocytes using 30% Percoll and analyzed by flow cytometry . Cervical lymph nodes were removed from infected mice on day 2 post-infection or from naïve controls and digested with DNase I ( 2 . 4 mg/ml , Roche ) and Collagenase I ( 2 . 4 mg/ml , Invitrogen ) in PBS for 15 min at 37°C . CD11b+ cells were enriched with the help of biotinylated anti-CD11b antibody and Streptavidin microbeads ( Miltenyi ) according to the manufacturer's recommendations . 5 x 104 C . albicans-specific T cell hybridoma 59 . 8 was co-cultured with 5 x 104 DC1940 cells that were pulsed with 5 x 104 heat-killed C . albicans , mannoprotein extract , 1 μg/ml of a pool of overlapping 15-mer peptides covering the entire ADH1 protein sequence or with 1 μg/ml of individual peptides ( A&A , La Jolla , CA ) . Alternatively , 105 59 . 8 hybridoma cells were stimulated with 105 cervical lymph node cells from sublingually infected mice , whichwere enriched for CD11b+ cells , without addition of exogenous antigen . After 24h of co-culture at 37°C , IL-2 production by the hybridoma cells was quantified with the CTLL-2 bioassay . For this , 1 x 104 CTLL-2 cells were incubated with supernatant from the hybridoma overnight and their viability was assessed by the alamar blue cell viability test ( Invitrogen ) following the manufacturer’s instructions . As a control , CTLL-2 cells were incubated with recombinant IL-2 or with medium alone . CD4+ T cells were purified from spleen ( and in some cases from spleen and lymph nodes ) of TCR transgenic Hector mice with anti-CD4 microbeads ( Miltenyi ) following the manufacturer's recommendations . In some cases , they were labeled with 1 μM carboxyfluorescein succinimidyl ester ( CFSE , Invitrogen ) for 5 minutes at room temperature . 6 x 104 T cells were then co-cultured with 1 x 104 CD11b+-enriched or FACS-sorted cervical lymph node cells from sublingually infected mice without addition of exogenous antigen . Alternatively , 6 x 104 Hector T cells were co-cultured with 1 x 104 splenocytes that had been pulsed with 100ng/ml of a pool of overlapping 15-mer peptides covering the entire ADH1 protein sequence or with 100ng/ml pADH1126-140 . Expression of CD69 as a marker of T cell activation was analyzed by flow cytometry after 24 hours of co-culture . T cell proliferation was determined by measuring dilution of the CFSE signal by flow cytometry after 3 to 4 days . In some experiments , 1 x 106 CD4+ Hector T cells were prepared as described above and adoptively transferred into recipients one day prior to infection . On day 7 post-infection , cervical lymph nodes were removed and single cell suspensions were prepared by digested with DNase I ( 2 . 4 mg/ml , Roche ) and Collagenase I ( 2 . 4 mg/ml , Invitrogen ) in PBS for 15 min at 37°C . For inducing cytokine secretion by primed T cells , 106 cervical lymph node cells were then re-stimulated for 6 hours with 1 x 105 DC1940 cells pulsed with pADH1126-140 peptide ( 100 ng/ml ) , 2 . 5x105/ml heat-killed C . albicans or left unpulsed . Brefeldin A ( 10 μg/ml , AppliChem ) was added for the last 5 hours . IL-17 production by endogenous CD3+ CD4+ T cells and/or CD3+ CD4+ Thy1 . 1+ Vα2+ Hector T cells was then analyzed by flow cytometry . All antibodies were from Biolegend , if not stated otherwise . For flow cytometry analysis of APCs , single cell suspensions of cervical lymph nodes and tongue were prepared as described before and stained in PBS with LIVE/DEAD Fixable Near-IR Stain ( Life Technologies ) , CD45 . 2 ( clone 104 , ) , CD11b ( clone M1/70 ) , Ly6C ( clone AL-21 , BD Biosciences ) , Ly6G ( clone 1A8 ) , I-A/I-E ( clone M5/114 . 15 . 2 ) , CD11c ( clone N418 ) , CCR2 ( clone 475301 , R&D Biosystems ) , Langerin ( clone eBioL31 , eBiosciences ) , CD103 ( clone 2E7 ) , CD64 ( clone X54-5/7 . ) , CD24 ( clone M1/69 ) or SIRPα ( clone P84 ) . For flow cytometry analysis of T cells , single cell suspensions were stained in ice-cold PBS with LIVE/DEAD Fixable Near-IR Stain , CD4 ( clone RM4-5 ) , CD3ε ( clone 145-2C11 ) , and in some cases CD44 ( clone IM7 ) , CD62L ( clone MEL-14 ) and/or CD69 ( clone H1 . 2F3 ) . Thy1 . 1 ( clone OX-7 ) and TCRVα2 ( clone B20 . 1 ) were added for identification of Hector TCR transgenic T cells . For intracellular cytokine staining , T cells were first incubated in ice-cold PBS containing LIVE/DEAD Fixable Near-IR Stain and surface marker antibodies . After fixation and permeabilization using BD Cytofix/Cytoperm ( BD Biosciences ) the cells were then incubated in in Perm/Wash buffer ( BD Biosciences ) containing anti-IL-17A ( clone TC11-18H10 . 1 ) and anti-IFNγ ( clone XMG1 . 2 ) antibodies . Data were acquired on a LSRII ( BD Biosciences ) and analyzed with FlowJo software ( Tristar ) . For all experiments , the data were pre-gated on live single cells . For isolating APC subsets by FACS sorting , cervical lymph nodes single cell suspensions were depleted of B and T cells with the help of biotinylated anti-CD19 ( clone 6D5 ) and anti-CD3ε and Streptavidin microbeads ( Miltenyi ) according to the manufacturer's recommendations , stained in ice-cold PBS with LIVE/DEAD Fixable Near-IR Stain , Ly6G , I-A/I-E , CD11c and CCR2 , and sorted on a FACSAriaII ( BD Biosciences ) . Statistical significance was determined by student's t-test using GraphPad Prism ( GraphPad Software ) with * , p < 0 . 05; ** , p < 0 . 01; *** , p < 0 . 001 . For data plotted on a logarithmic scale the geometric mean is indicated .
To study the immune response to Candida antigen , we first developed a Candida-specific TCR transgenic mouse . In this mouse , dubbed 'Hector' , 40–60% of all peripheral CD4+ T cells expressed a transgenic TCR consisting of Vα2 and Vβ4 genes sequenced from the T cell hybridoma 59 . 8 , which was generated from T cells that were isolated from a C . albicans-infected C57BL/6J ( B6 ) mouse ( S1 Fig ) . The antigenic specificity of Hector T cells was determined using the T cell hybridoma 59 . 8 and different C . albicans antigenic preparations presented by the DC1940 cell line [31] . As positive control , the T cell hybridoma 59 . 8 exposed to C . albicans-loaded DC1940 cells produced IL-2 , which was quantified using the CTLL-2 bioassay ( Fig 1A ) . The hybridoma was found to react against DC1940 cells pulsed with a mannoprotein-enriched fraction ( Fig 1A ) , indicating that the antigenic determinant was present in the C . albicans cell wall . Mass spectrum analysis of the mannoprotein extract revealed the presence of five abundant proteins ( yeast wall protein1 , YWP1; enolase , ENO1; glyceraldehyde-3-phosphate dehydrogenase , G3PDH; alcohol dehydrogenase , ADH1; fructose bisphosphate 1 , FBA1 ) . A peptide pool , consisting of 15-mers overlapping by 10 amino acids covering the entire ADH1 sequence , stimulated IL-2 production from the hybridoma 59 . 8 in a dose-dependent manner ( Fig 1B ) , while no response was detected against peptide pools covering the sequences of YWP1 , ENO1 , G3PDH or FBA1 . By screening the individual peptides of ADH1 , we identified 3 peptides that triggered IL-2 production by the hybridoma ( S2 Fig ) . Of these , peptide C2 induced the strongest when tested for their capacity to induce proliferation of Hector T cells , while peptide C3 induced a much weaker response and peptide D1 failed to induce a response in this assay ( S2 Fig ) . Peptide C2 stimulated proliferation of Hector T cells to an extent that was comparable to that induced by the peptide pool covering the entire ADH1 sequence ( Fig 1C ) . Finally , the specificity of Hector T cells for the ADH1 peptide C2 was confirmed in vivo in mice adoptively transferred with CD4+ Hector T cells and immunized with the peptide admixed with CpG adjuvant ( Fig 1D ) . Peptide C2 mapped to residues 126–140 of ADH1 ( pADH1126-140 ) , corresponding to sequence GSFEQYATADAVQAA , and was predicted to have a good binding affinity to I-Ab ( IC50 = 605 , SMM align method ) [33] . In line with the high degree of conservation of the ADH1 protein and in particular of the pADH1126-140 peptide sequence across Candida spp . , we found a comparable dose-dependent response of hybridoma 59 . 8 to different strains of C . albicans , C . dubliniensis , C . tropicalis , C . glabrata and C . krusei ( S2 Fig ) . Moreover , the epitope was also conserved in S . cerevisiae ( S2 Fig ) , but not in other ascomycetes such as Aspergillus or the dimorphic fungi . Thus , the Hector mouse is a source of T cells highly enriched in CD4+ T cells specific for a novel ADH1-derived antigen and they provide a valuable tool for studying in detail and in an antigen-specific manner the adaptive immune response to C . albicans . With the availability of C . albicans-specific TCR transgenic T cells , we set out to study the immune response to C . albicans antigen in a model of oral mucosal infection . CFSE-labeled CD4+ Hector T cells were adoptively transferred into B6 mice that were then infected sublingually with 2 . 5 x 106 cfu C . albicans , as previously described [29] . As shown in Fig 2 , Hector T cells proliferated extensively in the draining cervical lymph nodes upon infection ( Fig 2A and 2B ) , displayed an activated CD44hi CD62lo phenotype ( Fig 2C ) , and differentiated to Th17 cells ( Fig 2D and 2E ) . Cytokine production by Hector T cells was comparable whether the cells were re-stimulated with heat-killed fungus or with the cognate antigen ( Fig 2D and 2E ) . Moreover , similar results were obtained irrespective of whether 105 or the standard dose of 106 Hector T cells were adoptively transferred ( S3 Fig ) . A sizable proportion of endogenous polyclonal T cells ( 0 . 2 to 0 . 4% of the CD3+CD4+ cells ) also differentiated to Th17 cells , most of which co-produced IL-17A , IL-17F and IL-22 , while IFN-γ-producing cells could barely be detected . ( Fig 2D and 2E ) . In contrast to the oral infection , systemic infection with C . albicans induced differentiation of both Hector T cells and endogenous T cells into IFN-γ-producing Th1 cells ( Fig 2F and 2G ) . We next asked how in OPC C . albicans-derived antigen can reach the draining lymph node from the site of infection , since the fungus is normally restricted to the keratinized layer of the oral epithelium and does not invade deeper tissues or drain to lymphoid organs in immunocompetent mice [30] . Indeed , we were unable to culture C . albicans from the cervical lymph nodes of infected B6 mice . A likely possibility is that C . albicans-derived antigen accesses draining lymph nodes transported by migratory cells that arrive from the site of infection to the lymph nodes through afferent lymphatics . To test this possibility , we first enriched CD11b+ cells from cervical lymph nodes of C . albicans infected and naïve mice and found that only CD11b+ cells from infected mice could directly and rapidly stimulate the T cell hybridoma 59 . 8 ( Fig 3A ) and induce activation and proliferation of Hector T cells in vitro ( Fig 3B ) . Kinetic studies showed that maximal presentation was achieved on day 2 post-infection ( Fig 3C ) . We then enriched CD11b+ cells from cervical lymph nodes of infected B6 and Ccr7–/–mice , in which migration of cells from the periphery to draining lymph nodes is severely impaired [34] . Strikingly , activation of Hector T cells was strongly reduced ( Fig 3D and 3E ) . To further corroborate these data , we adoptively transferred CCR7-sufficient Hector T cells into Ccr7–/–or B6 mice prior to sublingual infection with C . albicans . Expression of CCR7 on Hector T cells allows their normal entry into cervical lymph nodes via high endothelial venules . When lymph nodes were analyzed on day 7 post-infection , we found that Hector T cells differentiated into IL-17A-secreting effector cells in response to C . albicans in B6 mice , but their expansion and differentiation was strongly reduced in CCR7-deficient mice ( Fig 3F ) , indicating that the delivery of C . albicans-derived antigen to the cervical lymph nodes was cell-associated and dependent on CCR7-mediated cell trafficking in vivo . Attempts to visualize the cells that take up C . albicans ( either labeled with a fluorescent dye or engineered to express GFP ) failed both in the tongue or in the cervical lymph nodes of infected mice , presumably due to the fact that the majority of C . albicans hyphae , the predominant morphotype in infected oral tissue , remained extracellular and the ingested material was degraded rapidly by the phagocytosing cells . Three major populations of DCs could be identified in cervical lymph nodes according to the expression of MHC II and CD11c: Population I ( MHC IIhigh CD11c+ ) , population II ( CD11chigh MCH II+ ) , and population III ( CD11cint MHC IIint ) ( Fig 4A ) . Population I and II expressed CD11b , CD24 and SIRP1α but not CD64 , while population III was more heterogeneous for expression of some of these markers ( Fig 4B ) . When compared to the populations found in lymph nodes of Ccr7–/–mice , population II could be identified as lymph node resident DCs and population I as migratory DCs . Population III could also be identified as composed mainly of lymph node resident cells , since the number of CD11cint MHC IIint cells was not affected in Ccr7–/–mice ( Fig 4A ) . During the course of C . albicans infection , populations I and III increased , while population II remained unchanged ( Fig 4C ) . To define the antigen-presenting capacity in OPC , we FACS-sorted to high purity the three DC populations from cervical lymph nodes of C . albicans infected mice and directly tested their capacity to activate Hector T cells in vitro without addition of exogenous antigen . We also tested Ly6G+ neutrophils isolated from the cervical lymph nodes of the same mice in the assay , since previous studies have shown that these cells can present antigen in certain conditions [35] . Hector T cells responded only very weekly , or not at all , to CD11chi MHC IIint DCs ( population II ) , CD11cint MHC IIint cells ( population III ) and Ly6G+ neutrophils . In contrast , they rapidly upregulated CD69 and proliferated strongly when co-cultured with MHC IIhi CD11c+ migratory DCs ( population I ) ( Fig 4D and 4E ) . In the assay , the T cell stimulatory capacity was strictly dependent on C . albicans-derived antigen , since DCs isolated from naïve animals were unable to induce proliferation of Hector T cells . Together with the previous findings , these data indicate that cells within the MHC IIhi CD11c+ migratory population can capture C . albicans-derived antigen from the peripheral tissue and transport them to the cervical lymph nodes where they are presented to CD4+ T cells . Consistent with a role of migratory DCs in the induction of Th17 differentiation , cells within population I produced higher levels of IL-6 compared to the other two DC populations in the cervical lymph nodes of OPC-infected mice ( S4 Fig ) . To further dissect the heterogeneity of the MHC IIhi CD11c+ population in the cervical lymph nodes we performed staining with antibodies to Langerin and CD103 . We detected two small populations consisting of Langerin+ CD103+ cells , most likely belonging to the Batf3-dependent DC subset and Langerin+ CD103– Langerhans cells ( Fig 5A ) . A third major subset consisted of Langerin−CD103– cells . We therefore set out to determine the role of these different cell types in the induction of antifungal T cell response during OPC . We used Langerin-DTR mice to deplete Langerin-expressing cells . Administration of diphtheria toxin prior to infection with C . albicans did not alter the extent of C . albicans-induced Th17 priming in the cervical lymph nodes , which was assessed by measuring IL-17 production after re-stimulation of effector T cells with cognate antigen in vitro ( Fig 5B ) . The C . albicans-induced Th17 cell response was also not affected in Batf3-deficient animals ( Fig 5C ) , indicating that both CD103+ and CD103– Langerin-expressing DCs were not essential for the induction of antifungal Th17 immunity in the oral mucosa . The large Langerin−CD103– subset of migratory DCs in the cervical lymph node appeared to be phenotypically homogeneous for all markers analyzed ( Fig 4B ) . However , the subset may still comprise phylogenetically and functionally distinct cell types , including Flt3-dependent conventional DCs and monocyte-derived DCs that are Flt3-independent but dependent on Csf1R signaling for differentiation from inflammatory monocyte [13] . Consistent with this notion , we found that in Flt3l-/- mice , migratory MHC IIhi CD11c+ DCs , which include the large population of Langerin−CD103– cells , were strongly reduced in Flt3l-/- mice compared to B6 mice ( Fig 6A ) . Furthermore , the CD11b+ cervical lymph node cells isolated from infected Flt3l-/- mice were strongly impaired in their ability to induce CD69 upregulation and proliferation of Hector T cells in vitro ( Fig 6B and 6C ) . This became also clear when the APC fraction was purified by FACS sorting: antigen presentation by Flt3L-dependent migratory DCs strongly promoted the activation of CD4+ Hector T cells in vitro , it was however not essential for the response ( Fig 6D ) , indicating that Flt3L-independent migratory DCs are also involved . Thus , Flt3L-dependent migratory DCs appear to be an important source of antigen in cervical lymph nodes for T cell activation in response to C . albicans oral infection . To assess the role of the Flt3L-independent monocyte-derived DCs we took advantage of the notion that these cells express CCR2 [12] . First , we noticed that CCR2+ monocytes , which are also Ly6Chi and CD11bhi , rapidly accumulated in the infected tongue ( S5 Fig ) . From day 2 post-infection , a proportion of CCR2+ monocytes up-regulated CD11c and MHC II and down-regulated Ly6C , indicating that they differentiated to DCs ( S5 Fig ) . Second , CCR2+ monocytes were also found in the cervical lymph nodes of infected mice ( Fig 6E ) , and some of them differentiated to DCs , as observed in the peripheral tissue ( Fig 6E and 6F ) . We also observed that monocyte-derived DCs with the strongest expression of MHC II and CD11c had reduced levels of CCR2 ( Fig 6F ) . Because of these characteristic markers , these cells fell in the gate of migratory MHChi CD11chi DCs ( population I ) and were hardly distinguishable from other migratory DCs such as Flt3L-dependent DCs . To directly assess the antigen presentation capacity of the CCR2+-derived migratory DCs during OPC , we separated MHC IIhi CD11c+ DCs from the cervical lymph nodes of infected mice into three fractions according to their CCR2 expression and exposed them to CD4+ Hector T cells in vitro . We observed T cell activation with all the subsets , irrespective of their degree of CCR2 expression , suggesting that both monocyte-derived DCs ( included mainly in the CCR2hi and CCR2int fractions ) as well as CCR2lo DCs , most likely reflecting Flt3L-dependent DCs , could directly present C . albicans-derived antigen ( Fig 6G ) . To evaluate the relative contribution of CCR2-dependent and Flt3L-dependent migratory DC subsets in T cell priming during OPC in vivo , we examined the induction of C . albicans-specific Th17 cell in the cervical lymph nodes of OPC infected Flt3l-/- and Ccr2-/- mice on day 7 post-infection . While the response was not affected by the absence of Flt3L-dependent DCs ( Fig 7A ) , the frequency of IL-17-secreting C . albicans-specific CD4+ T was strongly reduced in Ccr2-/- mice compared to B6 controls ( Fig 7B ) . Similarly to the endogenous response , adoptively transferred Hector T cells were also strongly impaired in their capacity to differentiate into IL-17-producing effector cells in Ccr2-/- mice ( S6 Fig ) . Alterations in DC and monocyte populations may impair the innate control of the fungus; and an increased fungal burden can augment the extent of the T cell response . Therefore , to exclude the possibility that the results were influenced by differences in the available amount of antigen between the experimental groups , we treated the mice with Fluconazole from day 2 post-infection , a time point when the fungal burden was high and similar in all groups of mice ( S7 Fig ) . This resulted in comparable weight recovery ( S7 Fig ) and clearance of the fungus to undetectable levels within the period of the experiment , which is indicative of comparable infection control in all experimental groups . To further support the critical role of monocyte-derived DCs in Th17 priming during OPC , we made use of a CSF1R-specific antibody , which was shown to block the differentiation of monocytes into monocyte-derived DCs [13] and led to a significant reduction of CCR2+ cells within the migratory DC population ( S8 Fig ) . Th17 induction in response to OPC was considerably lower in mice that were treated with this antibody prior to and during infection than in untreated controls ( Fig 7C ) . However , similarly to what we observed in the Ccr2-/- mice , the reduction in Th17 response in mice treated with anti-CSF1R antibody was only about 50% compared to the response in controls . This could be explained by inefficient/incomplete blockade with the antibody , while in Ccr2-/- mice , monocyte trafficking may be partially compensated by CCR2-independent mechanisms . The impact of monocyte-derived DCs on Th17 priming during OPC may thus be underestimated in our system . An explanation for the partial effect could also be the involvement of other subsets of APCs that might even compensate to a certain extent for the absence of monocyte-derived DCs to promote Th17 priming in anti-CSF1R treated or CCR2-deficient mice . Because Flt3L-dependent DCs contribute to the presentation of C . albicans-derived antigen , at least when presentation was assessed in our in vitro assay ( Fig 6B and 6C ) , we tested the hypothesis that they may play a relevant role in Th17 induction in absence of monocyte-derived DCs . For this we analyzed again the Th17 response to C . albicans in Flt3L-deficient animals and we included a group , in which we blocked the CSF1R prior to infection . All animals were again treated with Fluconazole from day 2 post-infection to minimalize differences in fungal burden between the experimental groups that could affect T cell priming ( S7 Fig ) . This resulted in a nearly complete blockade of the C . albicans-specific Th17 response ( Fig 7D ) . Together , our results demonstrate a contribution of different subsets of APCs to the Th17 response during OPC and highlight the critical role of monocyte-derived DCs and to a lesser extent Flt3L-dependent DCs in this process . While Flt3L-dependent DCs were not essential under normal conditions when monocyte-derived DCs were present , they did critically contribute if these primary APCs were absent .
Th17 cells have emerged as a key component of protective immunity against mucocutaneous candidiasis . In recent years , Candida-specific T cells have been studied in detail in humans and in mice leading to the identification of Th17-inducing innate pathways [9 , 10 , 36–40] , revealing Candida-derived antigenic epitopes [31 , 41–43] , and providing detailed insights into their clonal diversity [44] . Here we provide new insights in the mechanism that regulates fungus-specific T cell immunity . We generated a Candida-specific TCR transgenic mouse model to provide a tool for the detection , enumeration and characterization of antigen-specific T cells during infection in vivo . Using this new tool we identified the cellular players that present fungal antigen and instruct the Th17 response against C . albicans during oropharyngeal infection . CCR2-dependent monocytes-derived DCs rapidly accumulate in the oral mucosa during infection and directly present Candida-derived antigen in the draining lymph nodes . However , they are partially redundant for Th17 induction during OPC and complemented by tissue-resident Flt3-dependent migratory DCs for antifungal T cell priming in vivo . Together , this reveals a complex regulation of the antifungal T cell response by a multifaceted network MNPs in the oral mucosa . T cells of the newly established Hector mouse model are responsive to a native epitope of C . albicans derived from the metabolic enzyme ADH1 that localizes to the fungal cell wall and is also abundant in biofilms [45] . ADH1 was reported previously to have immunogenic properties because it bears some structural homology to integrins and can mediate adherence to extracellular matrix [46] , and ADH1-specific antibodies were detected in Candida-exposed individuals and mice [47 , 48] . Here we show that ADH1 determines T cell specificity ADH1 is highly conserved in diverse species of the genus Candida and in other Saccharomycotina . The short stretch of amino acids that define the T cell epitope identified here are 100% conserved in multiple clinically relevant species of Candida including C . albicans , C . dubliniensis and C . glabrata . The Hector mouse model thus offers new opportunities for investigating T cell responses against distinct species of Candida . Here we explored the regulation of C . albicans-specific T cells during oropharyngeal infection . The oral cavity is the entry port and the first site of contact with the host for a multitude of microbes . Diverse DC subsets including tissue resident DCs as well as inflammatory DCs accumulating in the oral mucosa in response to infection and inflammation orchestrate T cell outcomes in response to these microbes . DC subsets in the oral mucosa are related to those described in other barrier tissues . However , their relative abundance and distribution as well as their putative function partially differs . Similarly to the skin , Langerhans cells are present in the oral mucosa , but in contrast to what was reported for an epicutaneous model of candidiasis , in which Langerhans cells were shown to be necessary and sufficient for antifungal Th17 priming [49] , they are not essential for Th17 induction during OPC . Moreover , while Batf3-dependent DCs promote Th1 and inhibit Th17 cell differentiation during epicutaneous candidiasis [49] , absence of Batf3-dependent cells does not affect the T cell response during OPC . Whether monocyte-derived DCs are involved in the T cell response against C . albicans in the skin had not been examined to our knowledge . The discrepancies observed between the two distinct tissues are likely explained by site-specific differences , including the important difference in abundance of Langerhans cells between the skin and the tongue , but may also be influenced by the different mouse models of Langerhans cell deficiency used in the two studies . In the oral mucosa , Langerhans cells seem to be mainly tolerogenic [17] . The observed differences in regulation of T cell priming at different sites underline the compartmentalization of the immune response against one and the same fungal organism in different tissue environments . This effect is even more pronounced when antifungal immunity in barrier tissues is compared with the response elicited during systemic candidiasis , which is dominated by type 1 immunity [50] . Whether the quality of the Th17 response primed in the oral mucosa versus the skin may differ , remains to be established . In the oral mucosa , C . albicans-specific T cells form a stable long-lived population of memory Th17 cells that efficiently responds to secondary infection [51] . Similarly , Th17 cells primed in the skin have recently been shown to enhance protection from epicutaneous fungal challenge with C . albicans [50] . The DCs presenting C . albicans-derived antigen to T cells in the cervical lymph nodes of OPC-infected mice belong to the CCR7-dependent migratory population . Distinguishing monocyte-derived DCs from other migratory DC subsets in the cervical lymph nodes of C . albicans-infected mice on the basis of their phenotype is difficult as they gradually lose their characteristic expression of Ly6C and CCR2 . We therefore used an approach to separate them on the basis of their phylogenetic origin . Direct assessment of antigen presentation by migratory subsets revealed that both monocyte-derived DCs and Flt3L-dependent DCs presented C . albicans-derived antigen in the cervical lymph nodes of infected mice . They were however redundant for Th17 differentiation in vivo . Interrupting the differentiation of monocyte into DCs in absence of Flt3-dependent DCs almost abolished T cell priming , supporting the notion that other Flt3-independent DCs such as Langerhans cells are not required and not sufficient for Th17 induction during OPC . Monocyte-derived DCs gain increasing attention for their role as professional APC to promote T cell responses [52–54] including those elicited by fungi such as Aspergillus fumigatus or Blastomyces dermatitidis [55 , 56] . In addition to priming adaptive immunity , CCR2-dependent cells also contribute to innate immunity against fungi including C . albicans and A . fumigatus [57 , 58] . The mechanism by which these cells contribute to acute protection has not been fully established . During OPC , innate lymphoid cells and innate lymphocytes provide important sources of IL-17 during the early phase of infection [59 , 60] . Whether monocytes and/or monocyte-derived DCs impact on the regulation of innate IL-17 secretion has not yet been established . Here we show that monocyte-dependent DCs , together with Flt3L-dependent DCs , orchestrate the antigen-specific T cell response to a clinically highly relevant fungal pathogen , and may thus have implications for potential future immunotherapeutic approaches and vaccine development against mucosal candidiasis .
|
Candida spp . are present in the normal microbiota without causing damage to the host . They can become pathogenic and bear a serious health hazard for individuals with a weakened immune system . The continuous incidence of fungal infections and the increase in resistance against available antifungal drugs urge the development of novel preventive and therapeutic strategies . Knowledge gained from understanding how immunocompetent mammals control Candida will help develop new immunotherapeutic and-prophylactic approaches suitable to improve patient prognosis . It is well known that T helper cells , and in particular the Th17 subset , provide resistance against mucocutaneous infections with Candida . However , the mechanisms through which T cell-mediated antifungal immunity is induced in such context are not well understood . Here we developed a new experimental system to study the regulation of antigen-specific T cells with high resolution . Our results reveal the interplay of different dendritic cell subsets associated to the oral mucosa of infected mice that directly present fungal antigen to Candida-specific T cells and orchestrate a protective Th17 response in a tissue specific manner . Thus , our data highlight important features of immune regulation in the oral mucosa , a tissue that is immunologically not well characterized .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Antigen-Specific Th17 Cells Are Primed by Distinct and Complementary Dendritic Cell Subsets in Oropharyngeal Candidiasis
|
Whether functional magnetic resonance imaging ( fMRI ) allows the identification of neural drivers remains an open question of particular importance to refine physiological and neuropsychological models of the brain , and/or to understand neurophysiopathology . Here , in a rat model of absence epilepsy showing spontaneous spike-and-wave discharges originating from the first somatosensory cortex ( S1BF ) , we performed simultaneous electroencephalographic ( EEG ) and fMRI measurements , and subsequent intracerebral EEG ( iEEG ) recordings in regions strongly activated in fMRI ( S1BF , thalamus , and striatum ) . fMRI connectivity was determined from fMRI time series directly and from hidden state variables using a measure of Granger causality and Dynamic Causal Modelling that relates synaptic activity to fMRI . fMRI connectivity was compared to directed functional coupling estimated from iEEG using asymmetry in generalised synchronisation metrics . The neural driver of spike-and-wave discharges was estimated in S1BF from iEEG , and from fMRI only when hemodynamic effects were explicitly removed . Functional connectivity analysis applied directly on fMRI signals failed because hemodynamics varied between regions , rendering temporal precedence irrelevant . This paper provides the first experimental substantiation of the theoretical possibility to improve interregional coupling estimation from hidden neural states of fMRI . As such , it has important implications for future studies on brain connectivity using functional neuroimaging .
Distinguishing efferent from afferent connections in distributed networks is critical to construct formal theories of brain function [1] . In cognitive neuroscience , the distinction between forward and backward connections is essential in network models [2 , 3] . This is also important when describing how information is exchanged between different brain systems [4] and how neural coding is embedded in biological networks [5] . Such hierarchical structure is biologically grounded in the asymmetry of connections between neuronal ensembles , as suggested by computational neuroanatomy studies [6–9] . In clinical neuroscience , distinguishing neural drivers ( i . e . , the source of driving or forward connections in the brain—usually from deep pyramidal cells ) from other brain regions is essential when trying to identify structures involved in the origin or in the control of pathological activities . Epileptic seizures are illuminating in that sense . They are characterised by paroxysmal activities which , in the case of focal seizures , originate from the “epileptic focus” , i . e . , a neural network restricted to a particular cortical structure , and eventually spread to other structures of the brain [10] . The epileptic focus can thus be interpreted as a neural driver of the pathological activity . In relation to the existence of distributed networks , theories of brain function have recently promoted the concept of functional integration [11] . Functional integration specifies that brain functions are mediated by transient changes of interactions between certain brain regions , instantiated either by autonomous mechanisms ( dynamical systems operating at the limit of stability ) or by the action of neural drivers reinforced by the experimental context . In integrated neuroscience , these formal ideas have initiated a search for neural networks using sophisticated signal analysis techniques to estimate the connectivity between distant regions [4 , 12–18] . At the brain level , connectivity analyses were initiated in electrophysiology ( electroencephalography [EEG] and magnetoencephalography [MEG] ) because electrical brain signals have an excellent temporal resolution that makes them particularly amenable to such analyses . Connectivity measures in EEG and MEG [13 , 16] rely on the estimation of metrics of interaction that are more or less related to the notion of temporal precedence ( because of propagation and synaptic delays ) of the activity in the driving structure with respect to that in the driven ones . Despite their attractive neurodynamical features , EEG and MEG studies in healthy subjects are limited by their poor spatial resolution . Functional magnetic resonance imaging ( fMRI ) , in contrast , exhibits excellent spatial resolution and has become the method of choice for mapping brain functions . During neuronal activation , fMRI is sensitive mainly to changes of local perfusion and oxygen uptake by neurones [19] . FMRI therefore provides an indirect measure of neuronal activity . The dynamical properties of the technique highly depend on the neurovascular coupling that relates vascular changes to neural activity [20–22] . However , this physiological limitation , which compromises the temporal resolution ( ∼2 s ) of metabolic neuroimaging techniques , has not prevented careful analyses of connectivity using fMRI . Connectivity measurements with fMRI quantify either functional connectivity , i . e . , the correlation of fMRI time series between different regions [23–25] , or effective connectivity , i . e . , coupling parameters in generative models of fMRI time series [14 , 15 , 26] . Although numerous fMRI studies have shown exciting results about brain connectivity , it remains uncertain whether fMRI can be used to identify neural drivers . This is what we propose to evaluate here , in a genetic animal model of absence epilepsy using intracerebral EEG and simultaneous EEG/fMRI recordings . We use the Genetic Absence Epilepsy Rats from Strasbourg ( GAERS ) [27] . This animal model has been validated in terms of isomorphism , homology , and pharmacological predictability to be reminiscent of typical absence epilepsy , a form of generalised nonconvulsive epilepsy occurring during childhood in humans [28] . GAERS result from genetic selection of Wistar rats over 80 generations . Animals show spontaneous spike-and-wave discharges ( SWDs ) associated with behavioural arrest and slight perioral automatisms . These nonconvulsive seizures last 20 s on average and are repeated every minute when the rat is at rest . Intracerebral EEG recordings have shown that the frontoparietal cortex and ventrolateral nuclei of the thalamus play an important role in the generation and/or maintenance of these seizures [27 , 29] . Using local field potential and intracellular recordings , we have shown recently that SWDs originate from the perioral region of the first somatosensory cortex [30] . A similar finding had earlier been obtained in another genetic model of absence epilepsy [31 , 32] . We assess in this study whether fMRI can show evidence of the first somatosensory cortex being a neuronal driver during SWDs . We provide a comparative evaluation of vector regression models ( Granger causality ) [33] and Dynamic Causal Modelling ( DCM ) [14] . A key distinction between these models is that Granger causality tests for statistical dependencies among observed ( time-lagged ) physiological responses , irrespective of how they are caused . In contrast , dynamic causal models represent hidden states that cause the observed data and are therefore causal models in a true sense . If the mapping between the hidden brain states and observed responses is not causal , Granger causality estimated directly from fMRI time series can be very misleading . An example of a noncausal mapping is regional variations in the hemodynamic response function ( HRF ) that delay hemodynamic responses in fMRI , relative to their hidden neuronal causes ( see Protocol S1 for further explanation ) . Minimising the blurring effects of hemodynamic variability using explicit [34] or implicit ( such as in DCM [14] ) deconvolution techniques is thus the key aspect of any functional connectivity analysis using fMRI . This paper provides the first , to our knowledge , experimental substantiation of the theoretical possibility to estimate , in fMRI , functional connectivity from hidden neural variables and therefore demonstrates the raison d'être for DCM and other deconvolution techniques .
EEG recorded during fMRI was of sufficient quality to easily visualise periods of SWDs ( Figure 1 ) . Quantification of SWDs was performed by extracting EEG power between 4 and 20 Hz . On average , SWDs showed an increase of power by a factor 2 . 34 as compared to interictal activity , which corresponded to 2 . 57 times the standard deviation of interictal power . FMRI regressors were obtained by convolving such EEG power with a canonical HRF [34] . Note that this convolution smoothes and introduces a delay in the SWD time series on the order of several seconds ( corresponding approximately to the time to peak of the HRF ) . FMRI regressors were used to construct statistical parametric maps ( SPMs ) of regional effects in cerebral blood volume ( CBV ) related to the occurrence of SWDs . Highly significant and reproducible seizure-related activations ( CBV increases ) and deactivations ( CBV decreases ) were found at the animal level ( p < 0 . 001 , Familywise Error [FWE] corrected ) and at the group level ( n = 6 , p < 0 . 05 , FWE corrected ) ( Figure 2 and Table 1 ) . At the group level , activations were found in the barrel field of the primary somatosensory cortex ( S1BF ) , the centromedial , mediodorsal , and ventrolateral parts of the thalamus ( CM/MDL/MDC/CL/PC/VL/Po ) , the retrosplenial cortex ( RSA/RSGb ) , and the reticular part of the substantia nigra ( SNR ) . These structures are known to be involved in the generation or control of absence seizures . The cerebellum and nuclei of the pons ( Mo5 ) and of the medulla oblongata ( MdV ) were also found activated . In addition , several areas were found deactivated , such as the striatum ( CPu ) , the limb representation of the primary somatosensory cortex ( S1HL/S1FL ) , the visual cortex ( V1M/V1B/V2L ) , and the secondary motor cortex ( M2 ) . The HRF was found to last significantly longer in S1BF than in other ROIs ( Figure 3A ) . A similar effect was observed in the striatum , to a much lesser extent . These HRFs are kernels of a hemodynamic model , the parameters of which were estimated for every fMRI session . The estimated distribution of hemodynamic parameters in S1BF was found to be significantly different from one of the other ROIs in almost all possible pairs tested ( Wilcoxon test , p < 0 . 01 uncorrected; see Table 2 ) . To determine which parameter underlies predominantly the slowness of the HRF in S1BF , we generated different HRFs using prior values of the hemodynamic parameters , with the exception of one parameter , which was set to the value estimated in S1BF ( Figure 3B ) . This allowed us to conclude that the strong decrease of the autoregulation constant γ , instantiating a stable feedback of changes in cerebral blood flow ( CBF ) on vasodilatatory effects ( see Equation 1 ) , is the main cause of the pathological hemodynamics observed . These results show a large heterogeneity of HRF waveforms , in particular in S1BF and in the striatum , which has a significant impact on the estimation of connectivity . Estimation of temporal precedence , or of information transfer , and prediction between time series will be affected much by the variability in time to peak of the HRFs . Therefore , these results call for cautious interpretation of causality results directly obtained from hemodynamic measures ( see Protocol S1 for a conceptual schematic ) .
In this study , we used a well-recognised animal model of absence epilepsy ( GAERS ) [27 , 28] to assess whether fMRI can be used to determine directionality of interactions between remote brain regions . In epilepsy research , estimating neuronal drivers ( i . e . , epileptogenic zone ) within epileptic networks is one of the major issues . In drug-resistant patients with focal epilepsy , for instance , the precise determination of neuronal drivers should have a major surgical impact [35] . This is also true in cognitive neuroscience , in which the possibility to estimate oriented interregional connectivity should permit the refinement of network theories of brain function [3] . Although it is well established that SWDs in absence epilepsy result from paroxysmal oscillations within corticothalamic networks , the respective contributions of the neocortex and of the thalamic relay nuclei in the initiation of such activity are still debated [31 , 36] . It was first suggested that SWDs originate from a subcortical pacemaker with widespread and diffuse cortical projections [37–41] or from an interaction between cortical and thalamic neurons . However , data from a pharmacological model of SWDs in the cat [42–44] and from a genetic model of absence epilepsy , the Wistar Albino Glaxo/Rijswijk ( WAG/Rij ) rat [31 , 32 , 45] , provided evidence for a leading role of the cerebral cortex . In the GAERS , it was found that SWDs are initiated in the facial region of the somatosensory cortex before propagating , or not ( for brief SWDs ) , to the ventrolateral thalamus and to the primary motor cortex [30] . In addition , inhibition of this part of the first somatosensory cortex by local application of tetrodotoxin was shown recently to suppress SWDs ( P . O . Pollack , S . Mahon , M . Chavez , and S . Charpier , unpublished data ) . In human patients with absence epilepsy , fMRI [46] and positron emission tomography ( PET ) [47] studies showed the involvement of the thalamocortical system during SWDs , but without any clear evidence for the site of initiation of such activity . Here , using concurrent fMRI and EEG measurements , we obtained SWD-correlated changes in CBV beyond the thalamus and S1 . Significant activations or deactivations were also found in the brainstem , cerebellum , SNR , striatum , and different cortices ( retrosplenial , visual , limb region of S1 , and motor and sensory secondary ) . Interestingly , all these structures were activated bilaterally , resulting in a symmetrical network . Whereas , to our knowledge , the role of the cerebellum in the generation or control of SWDs has hitherto not been addressed , the spreading of discharges to different cortices was described [48] . CBV changes in striatum and substantia nigra pars reticulata are particularly noteworthy , as these structures , respectively the input and output of the basal ganglia , were suggested to control epileptic seizures in different animal models [49] . For instance , activation of dopaminergic transmission in the striatum suppresses seizures , whereas its inhibition by dopaminergic antagonists aggravates SWDs [50] . Similarly , inhibition of the substantia nigra pars reticulata by pharmacological manipulation is well known to block epileptic seizures in different models , including the GAERS [51] . Our EEG/fMRI results are thus in line with the view that SWDs propagate to different cortical regions , and to subcortical regions as well . Activation of basal ganglia circuits would allow endogenous regulation of SWDs , which can be artificially enhanced by neuromodulation techniques [49] . Two EEG/fMRI studies were performed in the WAG/Rij rat [52 , 53] . Bilateral activations were also found in the frontoparietal cortex , the thalamus , and brainstem nuclei . No deactivations were reported , however . GAERS and WAG/Rij rats , though similar in many aspects , show also some differences , in particular in the features of spontaneous SWDs [28] . These differences may explain why fMRI activations only partly overlap . Importantly , a strong activation in S1BF is observed in both models . This finding supports the important role of this part of the cortex in the initiation of SWDs , as demonstrated by electrophysiology in GAERS and in Wag/Rij rats [30–32] . In the present study , in addition to revealing the spatial organisation of the epileptic network , we estimated the HRF to SWDs in the different regions involved . We thereby used a truncated hemodynamic model [20] characterised by various parameters directly related to underlying biophysical processes . In the model used , it is assumed that changes in synaptic activity trigger vasodilatatory effects described by the lumped time constant called “signal decay κ . ” Vasodilatation induces changes in cerebral blood flow ( CBF ) , which in return have an autoregulation effect on changes in vasodilatation ( constant γ in the model ) . Changes in CBV are then obtained from changes in CBF using a state equation with two parameters ( a transit time τ and an exponent α for nonlinear effects ) . Our main finding here was an abnormally slow HRF in S1BF , due to near suppression of the autoregulation mechanisms of CBF on vasodilatation . The autoregulation constant γ is a lumped parameter that summarises , in dynamical terms , the effects of many different physiological processes involved in the feedback autoregulatory mechanisms occurring during functional hyperemia . Functional hyperemia , which matches the delivery of blood flow to the activity level of each brain region , requires coordinated cellular events that involve neurons , astrocytes , and vascular cells [54] . Deregulation of the function of any of these cell types in S1BF thus appears as a plausible physiological mechanism to explain the abnormally long time constant of CBF feedback that we found . Additional experiments in the future are needed to reveal which processes involved in regulation of vasodilatation by blood flow are exactly altered in the first somatosensory cortex of the GAERS . Such differences in hemodynamic properties allowed us to challenge the face validity of functional connectivity analyses in fMRI . For simplicity and reproducibility among animals of this validation study of functional connectivity in fMRI , we selected three regions of interest that ( 1 ) were the most consistently activated over sessions and animals , ( 2 ) exhibited different hemodynamics , and ( 3 ) were easily integrated in our current understanding of SWDs . We selected first S1BF because of recent evidence indicating its role as a cortical driver , second the ventrobasal thalamus because it is known that the thalamocortical loop is implicated in SWDs , and third the striatum because of various studies suggesting its role in the control of SWDs . Other structures also showing significant CBV changes at the group level were ignored , either because the signal-to-noise ratio was too low at the session level ( because estimated connectivity is related to effect size and highly depends on signal-to-noise ratio , this would have entailed a significant loss of results reproducibility between animals and sessions ) , or because no experimental evidence was available for validating connectivity results ( for instance , it would have been difficult to interpret fMRI connectivity results for cerebellum that has never been explored in GAERS ) . The Granger causality measure tested [25 , 33] , heavily based on the concepts of temporal precedence , information transfer , and prediction between time series , estimated the striatum as being the neural driver of SWDs when applied directly to fMRI signals . This result strongly contradicts the evidence from the literature [49] . We then evaluated whether the very same Granger causality measure , but applied to hidden neural states estimated after deconvolution of hemodynamic effects in fMRI time series , would be more compelling . It was indeed the case since S1BF was identified as the neural driver at the group level . Comparison of the results of both analyses demonstrates that the failure of connectivity analysis from original fMRI time series to identify S1BF as the neural driver is due to regional variability of the HRFs . Finally , connectivity analyses at the neuronal level using DCM were also able to reconstruct a meaningful connectivity pattern . Bayesian model comparison showed a clear preference for the models specifying S1BF as the neuronal driver , with consistent reproducibility among animals . At the animal level , results obtained with DCM were more reproducible than with the linear implementation of Granger causality . It is probable that more sophisticated approaches , including multivariate , nonlinear , parametric , or nonparametric implementation of Granger causality [55–57] , would have allowed a significant improvement in result reproducibility between animals . fMRI connectivity analyses were validated using iEEG data obtained in freely moving rats . The directionality of interactions , estimated from the asymmetry of a measure of generalised synchronisation , clearly indicated S1BF as being the driver . The generalised synchronisation measure relies on time-embedding of iEEG signals ( Takens' theorem ) . This manipulation depends upon some parameters that are sometimes difficult to optimise [58] , and moreover , its theoretical underpinnings [59] might not be totally fulfilled by brain signals . In view of these potential difficulties , for construct validation in terms of spike propagation , the averaged SWD complex was computed , and a temporal precedence of the activity in S1BF was demonstrated , as anticipated from iEEG generalised synchronisation and from fMRI connectivity . Because fMRI does not provide sufficient information to reconstruct accurate electrical activity , the neuronal model used in DCM remains necessarily simple , allowing the generation of caricatures of neural states . Nevertheless , DCM distinguished different functional hypotheses in a meaningful way . To our knowledge , this study provides the first experimental validation of DCM for fMRI using invasive EEG recordings . The so-called “synaptic activity” estimated by DCM remains difficult to interpret . First-order electrical kernels ( see Figure 5 ) do not allow the generation of EEG-like signals if convoluted with a random input ( as classically done when modelling EEG with neural mass models [60] ) because their time constant ( ∼2 s ) is too large to generate the 7–9-Hz oscillations that characterise SWDs in GAERS . Their dynamic properties are more compatible with the rate of change of EEG power often observed at the beginning of seizures ( see Figure 1 in [30] ) . The coupling parameters of DCM might then be interpreted as indications of how changes in EEG power are transferred between regions . Because DCM parameters in fMRI are estimated from several minutes of recordings , the significant difference that was found between models implies that the information transfer is more or less stable during seizures—in other words , that one direction of information transfer dominates . This is indeed what we observed in iEEG , as far as connectivity from S1BF was concerned ( Figure 7 ) . Finally , it is important to note that , like any model-based approach , results depend on the assumptions of the generative model used . In particular , current implementation of DCM [14] does not take into account time lags between neural populations due to conduction velocities and propagation through dendritic trees . Elaborating and validating a more realistic neural model for DCM in fMRI taking time dependencies into consideration would be interesting , but goes well beyond the scope of this work . This study is , to our knowledge , the first electrophysiological validation of fMRI connectivity analyses based on Granger causality and Dynamic Causal Modelling using a well-characterised animal model of functional coupling . As such , it has important implications for such studies that are starting to predominate in the functional neuroimaging literature on connectivity . Our results clearly indicate that one must minimise spurious interactions due to hemodynamic variability between brain regions using explicit or implicit ( such as in DCM ) deconvolution of hemodynamic effects in fMRI time series . Otherwise , directed functional connectivity results should be taken cautiously , particularly if one cannot demonstrate that hemodynamic properties are the same in every region analysed .
Experimental procedures and animal care were carried out in accordance with the European Community Council Directive of 24 November 1986 ( 86/609/EEC ) . They were approved by the Ethical Committee in charge of animal experimentation at the Université Joseph Fourier , Grenoble ( protocol number 88–06 ) . Six male adult GAERS ( 281 ± 56 g ) were used for the fMRI/EEG study , and five adult GAERS ( two males , three females; 232 ± 70 g ) were recorded in iEEG . Spontaneous seizures were measured during magnetic resonance ( MR ) experiments using EEG . Animals were equipped with three carbon electrodes located on the skull near the midline ( frontal , parietal , and occipital ) , several hours prior to the MR experiments . Two additional carbon electrodes were used to monitor cardiac activity ( electrocardiography [ECG] ) . Because absence epilepsy is suppressed by anaesthesia , animals were maintained conscious under neuroleptanalgesia . Anaesthesia was induced under 5% isoflurane , maintained under 2% isoflurane during animal preparation , and stopped during MR acquisition . The femoral artery was catheterised to allow administration of an iron-based superparamagnetic contrast agent ( injected as a bolus just before MR preparatory settings , 8 mg Fe/kg , i . e . , 145 mmol Fe/kg , Sinerem ) and infusion of curare and analgesics . Just before inducing neuroleptanalgesia , a tracheotomy was performed , and animals were ventilated at 90 breaths/min throughout the rest of the experiment . Neuroleptanalgesia was induced using an intravenous bolus of d-tubocurarine ( 1 ml/kg ) . Animals were then maintained under intravenous infusion of a mixture of d-tubocurarine ( 1 . 2 mg/kg/h ) , Fentanyl ( 3 μg/kg/h ) , and haloperidol ( 150 μg/kg/h ) [30] . Animals were secured in an MR-compatible , customised , stereotaxic headset with ear and tooth bars . They were positioned in the magnet , maintained in position between 3 and 4 h for data acquisition , and then sacrificed . Rectal temperature was monitored and kept at 37 °C using a heating pad positioned under the animal . MR imaging was performed in a horizontal-bore 2 . 35 T magnet ( Bruker Spectrospin ) , equipped with actively shielded magnetic field gradient coils ( Magnex Scientific ) and interfaced to a SMIS console ( SMIS ) . A linear volume coil was used for excitation ( internal diameter 79 mm ) , and a surface coil was used for detection ( Rapid Biomedical ) . Both coils were actively decoupled . T1-weighted anatomical images were acquired using a 3D-MDEFT sequence with parameters optimised following the procedure described in [61]: voxel size = 0 . 333 × 0 . 333 × 0 . 333 mm3 , TI = 605 ms , quot = 0 . 45 , alpha = 22° , TR/TE = 15/5 ms , and BW = 20 kHz . CBV-weighted measurements were made with gradient-echo echo-planar imaging ( EPI ) acquisition ( two shots , data matrix = 48 × 48 , FOV = 35 × 35 mm2 , 15 contiguous 1 . 5-mm-thick slices covering the whole brain , alpha = 90° , TE = 20 ms , TR = 3 s ) . Functional volumes were acquired over about 2 h , in several 30-min sessions to prevent overheating of the gradient hardware . 3D-MDEFT and EPI images were centred to facilitate superimposition . EEG and ECG signals were sampled simultaneously with fMRI at 1 , 024 Hz ( SD32 , Micromed ) . ECG was merely used to monitor the physiological state of animals . When ECG revealed a heart frequency below 250 beats/min , the experiment was terminated , and the animal was sacrificed . EEG and fMRI temporal coregistration was ensured by the EEG acquisition software recording a TTL signal from the MR system at each volume acquisition . For the iEEG recordings , GAERS were implanted with intracerebral electrodes under general anaesthesia ( diazepam 4 mg/kg intraperitoneally [i . p . ] , ketamine 100 mg/kg i . p . ) . Pairs of electrodes formed of stainless steel wires ( 0 . 175 mm ) separated by 2 mm on the longitudinal axis were stereotaxically placed in each structure targeted . Stereotactic coordinates were as follows , with the bregma as reference [62]: ( 1 ) first somatosensory cortex S1BF ( anteroposterior [AP]: −1 and −3 mm; mediolateral [ML]: +5 mm; and dorsoventral [DV]: −3 mm ) , ( 2 ) ventrobasal thalamus ( AP: −2 . 3 and −4 . 2 mm; ML: +2 . 4 mm; and DV: −6 . 2 mm ) , and ( 3 ) striatum ( AP: +3 and −0 . 8 mm; ML: +3 mm; and DV: −6 mm ) . Two additional electrodes ( stainless steel screws ) were fixed in the nasal and occipital bones to serve as reference and/or ground . All electrodes were connected to a female microconnector that was fixed to the skull by acrylic cement . Animals were allowed to recover for a week , during which they were handled daily for habituation . Once implanted , the rats were kept alive 2 mo at maximum . They were killed by an overdose of pentobarbital , and their brains were then removed and cut into 20-μm coronal sections . These sections were stained with cresyl violet , and each site was localised with reference to the atlas of Paxinos and Watson [62 , 63] . Electrode implantation was considered correct if the centre of gravity of the pair of electrodes was located within the targeted structure . Electroencephalograms were recorded in awake , freely moving animals , using a digital acquisition system ( Cambridge Electronic Design ) with a sampling rate of 2 kHz and analog filters ( high-pass filter 1 Hz/low-pass filter 90 Hz ) . During the recording sessions , rats were continuously watched to detect abnormal posture or behaviour . Sessions did not exceed 2 h and were performed between 9:00 am and 5:00 pm . fMRI data analysis was done using SPM5 ( Statistical Parametric Mapping , Wellcome Department of Imaging Neuroscience , Functional Imaging Laboratory , London , UK ) . Some routines of this software were adapted to rat imaging in accordance with [63] . For each session , EPI volumes were first realigned to account for motion correction . All images were then normalised to a 3D-MDEFT template with coordinates chosen according to the rat atlas of Paxinos and Watson , with the origin at the bregma [62] . Normalised images were resampled to reach an isotropic spatial resolution of 0 . 4 mm . Finally , normalised EPI images were smoothed with a Gaussian kernel of 0 . 5-mm width . Statistical analysis was done on smoothed , normalised , and realigned EPI images . Statistical maps of regional CBV changes in relation to SWDs were obtained using the standard procedure applied in EEG/fMRI studies of epilepsy [64 , 65] . It consists of the detection of epileptic events in the EEG . A regressor of interest for fMRI data is then obtained by convolving EEG epileptic events with a model of the hemodynamic impulse response function [66] . If the impulse response is causal ( which is usually the case ) , it is assumed that electrical activity precedes and causes hemodynamic changes . SWDs were extracted from the EEG using a moving average ( time window length = 2 s; sampling rate = 5 Hz ) of EEG power between 4 Hz and 20 Hz . SWD power was then scaled such as to be about zero between SWDs and about one during SWDs . Note that it was not necessary to correct imaging or cardiac artefacts in our data because they were not significant at frequencies of SWDs . The SWD regressor used for fMRI statistical analysis was obtained by convolving the normalised SWD power with the canonical HRF provided in SPM5 . For each animal , SPMs of the t-statistic of SWD-related activations were obtained by correlating the high-pass filtered ( cutoff = 0 . 97 mHz ) time series of each voxel with the SWD regressor using a standard first-level multisession statistical design [67] . Activations at the group level were obtained using a fixed-effect analysis following guidelines provided in [68] . The decision to perform a fixed-effect analysis was based on ( 1 ) the reduced number of animals ( n = 6 ) being too small to perform a random-effect analysis and ( 2 ) the excellent reproducibility between animals of the activation patterns . Activation maps were obtained under the conventional hypothesis of identical hemodynamics all over the brain . Although this assumption is particularly convenient to obtain statistical maps , significant hemodynamic variability is to be expected [69–71] . Taking into account this spatial variability is critical in identifying neuronal drivers from fMRI signals . We therefore estimated the HRFs in the different structures activated . A biophysical model of brain hemodynamics was used to biologically constrain the estimation of the HRFs . We therefore adapted the hemodynamic model used in [14 , 20] to the measurement of CBV-weighted signals ( due to the use of an iron contrast agent ) . Briefly , we removed from the distributed version of DCM ( SPM5 , http://www . fil . ion . ucl . ac . uk/spm/ ) the state equation corresponding to the definition of deoxyhemoglobin content , and we changed the output equation that was developed for BOLD signals ( assuming BOLD signals as arising from a mixture of CBV and blood oxygenation effects ) . The model thus described below is a truncated version of the hemodynamic model developed in [20] . For the ith region , neuronal activity zi causes an increase in a vasodilatory signal si ( time constant κi ) that is subject to autoregulatory feedback ( autoregulation constant γi ) . Inflow fi responds in proportion to this signal with changes in blood volume vi ( time constant τi and nonlinear constant αi ) : Variations in CBV-weighted signals y were assumed to be proportional and of opposite sign to variations of blood volume v—a CBV increase shortens the transverse relaxation time [72] . The four hemodynamic parameters for each region i ( κi , γi , τi , and αi ) were estimated from the time series of each ROI using a maximisation-expectation algorithm [73] , similar to the one used in the standard DCM procedure [14] . Most methods to infer the direction of information transfer between two time series are based on identifying temporal precedence . If past activity of a given region X helps predicting current activity of another region Y , then it is assumed that the activity of X causes to some extent the activity of Y . Although compelling , temporal precedence in fMRI time series may be biased by regional variability of hemodynamics ( see Protocol S1 for an intuitive explanation ) . Consequences of hemodynamic variability can be minimised by deconvolving fMRI time series with a hemodynamic impulse response function . Output time series represent then hidden state variables that are more closely related to neuronal activity . Instead of original fMRI time series , such a state-space model can be used to infer functional connectivity . Hemodynamic deconvolution of each ROI time series was performed as described in [34] . Under linear assumption , fMRI signals m ( t ) can be modelled as the result of the convolution of neural states s ( t ) with a hemodynamic response function h ( t ) : where t is the time and ⊗ denotes convolution . ε ( t ) is the noise in the measurement , assumed here to be white and therefore defined by its constant power spectrum . The estimation of the neural states s ( t ) was obtained using the following formula [34]: where FT−1 denotes the inverse Fourier transform , and H ( ε ) , M ( ε ) are the Fourier transform of h ( t ) , m ( t ) , respectively . For each ROI , the hemodynamic response function h ( t ) was obtained after optimising the parameters of the biophysical model described in Equation 1 . The expectation-maximisation algorithm used for this parameter optimisation also provided the value of the noise power spectrum ε02 . Granger causality measures have been proposed recently to identify the direction of information transfer between remote brain regions recorded in fMRI [25 , 33] . In its simplest version , Granger causality is computed using linear multivariate autoregressive models of fMRI time series . For each pair of brain regions X and Y , the linear influence from X to Y ( Fx→y ) and from Y to X ( Fy→x ) is defined as follows [25]: where x and y are the time series of regions X and Y . In Results , they correspond either to hemodynamic activity ( fMRI signals entered directly into the analysis ) or to hidden neural states ( obtained from fMRI signals using Equation 3 ) . x[n] corresponds to the nth time bin of x . The three first lines of Equation 2 define autoregressive models for time series of regions X and Y , the three lines below quantify the residual variances , i . e . , how well autoregressive models predict time series , and the two last lines show how interdependency measures are defined from the residual variances . Autoregressive models were estimated using the Matlab package ARfit ( http://www . gps . caltech . edu/~tapio/arfit/ ) [74 , 75] . The model order p was defined according to the Schwarz Bayesian criterion [76] . It measures the efficiency of the parameterised model in terms of predicting the data and penalises the complexity of the model , where complexity refers to the number of model parameters . For each pair of regions ( x , y ) , statistics on the asymmetry of the interaction measure Fx→y − Fy→x were obtained using 999 surrogate datasets [77] that were constructed for each session by translating , independent of each another , ROI time series by a random number of time samples . Surrogates thus destroyed local time interdependencies and preserved the properties of each signal taken separately . They allowed one to estimate distributions of Granger causality under the null hypothesis that ROI time series were locally uncorrelated and were not time-locked over sessions and animals . Null distributions were drawn at the animal and group levels by averaging Fx→y − Fy→x over sessions and animals for each surrogate realisation . p-Values on the direction of interactions were obtained by comparing the value computed from original data to the null distribution constructed from surrogates ( see [77] for a review on surrogates ) . We refer here to the simplest implementation of Granger causality because it is the most popular in fMRI [25 , 33] . However , there are many other possibilities , including parametric and nonparametric nonlinear approaches that have been applied to the brain , mainly in electrophysiology [55–57] . DCM [14] relies on a biophysical model that connects the neuronal states z , called “synaptic activity , ” to fMRI signals . A bilinear neuronal state equation specifies the connectivity between n brain regions: where A , B , and C are connectivity matrices , and u are inputs to the neural system . The synaptic activity is then transformed into fMRI signals using the hemodynamic model described in Equation 1 . Using a maximisation-expectation algorithm , DCM proceeds to a conjoint estimation , from the measured CBV time series , of the neuronal parameters ( connectivity matrices A , B , and C ) and of the four hemodynamic parameters for each region i ( κi , γi , τi , and αi ) . In other words , it performs in one step the hemodynamic deconvolution and connectivity estimation between hidden neural variables . This implies a certain degree of interactions between both processes that potentially results in more robust results than when deconvolution and connectivity analyses are taken separately . For the present study , we identified neural drivers within a small network composed of three regions . To prevent introducing any bias in the estimation of functional connectivity , we did not take into account prior anatomical information about probable missing connections . We thus chose to specify all possible unidirectional networks comprising direct and/or indirect connections ( 15 models; S1BF driver: models 1–5; thalamus driver: models 6–10; striatum driver: models 11–15 ) ( Figure 8 ) . Because DCM necessitates knowledge of the inputs u , we defined u as being equal to the SWD regressor—shifted backwards in time ( 400 ms , which corresponds approximately to the time constant of the driver's DCM neuronal kernel , see results in Figure 5 ) to account for neuronal filtering ( u is a presynaptic input whereas the EEG reflects multi-postsynaptic activity [60] ) . In each model , an input u ( non-zero C matrix ) was applied to the assumed neural driver . Here , input u must be thought of as a practical way to model unstable dynamics intrinsically generated by an epileptic focus , using simple dissipative neural models used by DCM as described in Equation 5 . In the models shown in Figure 8 , the region receiving input u transfers the information to other regions with forward connections ( matrix A ) . For parsimony , we did not allow a modulation of the interregional connection strength by u ( by the means of the modulatory matrix B ) . We thereby assumed that the connection strength did not vary between ictal and interictal states . To conform to standard practice in DCM studies , only self modulation ( first diagonal of B ) of the region receiving the exogenous input was allowed . Actually , because it appears that inputs u were very close to zero during interictal states , assumptions about connectivity modulation had little effect on the parameters estimated . Identification of the neural driver in the 15 competing models ( Figure 8 ) was done using Bayesian model comparison based on model evidence [78] . Practically , the model log-evidence was approximated by the model negative free energy , the criterion used for optimising the model parameters [14] , which is a tight lower bound on the log-evidence . The most plausible model is the one with the largest negative free energy , i . e . , the best fit to the data . A difference in log-evidence of approximately three is usually taken as strong evidence for one model over the other ( i . e . , the marginal likelihood of one model is ∼20 times the other ) [14] . Assuming each dataset is independent of the others , the log-evidence at the group level ( or at the animal level when different sessions have been acquired ) is simply obtained by adding the log-evidence of each session [79] . iEEG data analysis was done using a SPM5 Toolbox for intracerebral EEG developed in our laboratory . iEEG signals were first band-pass filtered between 5 and 100 Hz to capture the main frequencies of SWDs and to remove motion artefacts in low EEG frequencies . Seizures were visually detected . Only those showing ( 1 ) no movement artefact , ( 2 ) preictal and postictal periods of at least 4 s , and ( 3 ) a duration of at least 10 s were kept for further analysis ( n = 72 ) . As a first estimation of the sequence of “activation” within the three implanted structures , spike averaging over time was performed . An ad hoc algorithm , based on EEG amplitude thresholding and local maxima identification , was implemented in which the first peak of the SWD complex was detected in signals originating from S1BF . The mean activation pattern was then obtained by averaging each SWD complex over time , seizures and animals using a time window covering from 50 ms before up to 80 ms after detected spikes . The delay between the peaks in the signals from the different structures was finally measured on the averaged waveforms . Further functional connectivity analyses in iEEG were performed using a nonlinear measure based on the concept of generalised synchronisation [13 , 16 , 80 , 81] . By definition , generalised synchronisation exists between two dynamical systems X and Y when the state of the response system Y is a function of the state of the driving system X:Y = F ( X ) . If F is continuous , two close points on the attractor of X should correspond to two close points on the attractor of Y . An important feature of generalised synchronisation is that synchronised time series can look very dissimilar , which is critical for analysing highly nonlinear signals such as those measured with EEG in epilepsy . Details of these methods can be found in Protocol S2 . Briefly here , we used the normalised measure of generalised synchrony Δ between regions X and Y as described elsewhere [13 , 16] . There are two ways to compute Δ , which we denote Δ ( X | Y ) and Δ ( Y | X ) . Δ ( X | Y ) and Δ ( Y | X ) are not identical for asymmetrical systems . This property can be used to dissociate the driver and the driven systems , and we defined the direction of information transfer between X and Y using Δ ( Y | X ) − Δ ( X | Y ) . For each seizure , the normalised measure of generalised synchronisation Δ was computed on a time window ( duration of 4 s to get sufficient number of time points for robust estimation of generalised synchronisation ) , which was translated every 200 ms between −2 s up to 8 s according to seizure onset . By using a sliding window , we were able to compute the evidence for directed connectivity as a function of peristimulus time , after SWDs onset .
|
Our understanding of how the brain works relies on the development of neuropsychological models , which describe how brain activity is coordinated among different regions during the execution of a given task . Knowing the directionality of information transfer between connected regions , and in particular distinguishing neural drivers , or the source of forward connections in the brain , from other brain regions , is critical to refine models of the brain . However , whether functional magnetic resonance imaging ( fMRI ) , the most common technique for imaging brain function , allows one to identify neural drivers remains an open question . Here , we used a rat model of absence epilepsy , a form of nonconvulsive epilepsy that occurs during childhood in humans , showing spontaneous spike-and-wave discharges ( nonconvulsive seizures ) originating from the first somatosensory cortex , to validate several functional connectivity measures derived from fMRI . Standard techniques estimating interactions directly from fMRI data failed because blood flow dynamics varied between regions . However , we were able to identify the neural driver of spike-and-wave discharges when hemodynamic effects were explicitly removed using appropriate modelling . This study thus provides the first experimental substantiation of the theoretical possibility to improve interregional coupling estimation from hidden neural states of fMRI . As such , it has important implications for future studies on connectivity in the functional neuroimaging literature .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"neurological",
"disorders",
"computational",
"biology",
"radiology",
"and",
"medical",
"imaging",
"neuroscience"
] |
2008
|
Identifying Neural Drivers with Functional MRI: An Electrophysiological Validation
|
Natural soil is characterized as a complex habitat with patchy hydrated islands and spatially variable nutrients that is in a constant state of change due to wetting-drying dynamics . Soil microbial activity is often concentrated in sparsely distributed hotspots that contribute disproportionally to macroscopic biogeochemical nutrient cycling and greenhouse gas emissions . The mechanistic representation of such dynamic hotspots requires new modeling approaches capable of representing the interplay between dynamic local conditions and the versatile microbial metabolic adaptations . We have developed IndiMeSH ( Individual-based Metabolic network model for Soil Habitats ) as a spatially explicit model for the physical and chemical microenvironments of soil , combined with an individual-based representation of bacterial motility and growth using adaptive metabolic networks . The model uses angular pore networks and a physically based description of the aqueous phase as a backbone for nutrient diffusion and bacterial dispersal combined with dynamic flux balance analysis to calculate growth rates depending on local nutrient conditions . To maximize computational efficiency , reduced scale metabolic networks are used for the simulation scenarios and evaluated strategically to the genome scale model . IndiMeSH was compared to a well-established population-based spatiotemporal metabolic network model ( COMETS ) and to experimental data of bacterial spatial organization in pore networks mimicking soil aggregates . IndiMeSH was then used to strategically study dynamic response of a bacterial community to abrupt environmental perturbations and the influence of habitat geometry and hydration conditions . Results illustrate that IndiMeSH is capable of representing trophic interactions among bacterial species , predicting the spatial organization and segregation of bacterial populations due to oxygen and carbon gradients , and provides insights into dynamic community responses as a consequence of environmental changes . The modular design of IndiMeSH and its implementation are adaptable , allowing it to represent a wide variety of experimental and in silico microbial systems .
Soil hosts the greatest diversity and abundance of microbial life in all of the biosphere , with over 109 cells per gram of soil [1] expressing thousands to millions of different operational taxonomic units in small volumes [2 , 3] . Nonetheless , considering the vast surface area per volume of a typical soil , bacterial cells are sparsely distributed within the soil matrix , covering less than 1% of the total available soil surface [4 , 5] . This highly non-uniform distribution is further reinforced by localized resource distribution giving rise to microbial hotspots , such as in soil aggregates , the rhizosphere , biopores or the detritusphere [6] . These hotspots are important for large-scale biogeochemical cycles of carbon and nitrogen and for the emission of greenhouse gases [6–9] . Improved prediction of soil ecological functioning mediated by bacterial processes requires better understanding of how these bacterial hotspots function and their response to dynamic conditions and integrated influence on large-scale processes . In addition to its inherent complexity , soil opacity limits direct visualization of bacterial distribution within soil [10 , 11] . Although the use of soil thin sections and artificial micrometric pore networks offers insights into spatial quantification of bacterial distributions and self-organization [12 , 13] , the simultaneous acquisition of the dynamic chemical landscape is not yet resolvable . Mechanistic models are capable of bridging some of the experimental gaps and facilitate systematic studies of the interplay of physical , chemical and biological factors and their spatiotemporal interactions . Individual-based models ( IBM ) have gained in popularity owing to their local scale and cell-level representation of interactions , which offer a useful platform for prediction of emergent spatial patterns and population abundances arising from microscale interactions . IBMs have been used to investigate a range of questions in microbial ecology such as altruism in biofilms [14] , the influence of metabolic switching on biofilm structure [15] , bacterial coexistence due to hydration dynamics [16] , bacterial spatial organization in soil aggregates [17] , and the dynamics of bacterial community diversity in desert soils [18] . A common feature of IBM models is the estimation of individual bacterial growth rates and nutrient consumption based on simple growth kinetics ( e . g . Monod kinetics ) with empirically prescribed stoichiometric parameters . A flexible approach to calculating bacterial cell response to local conditions and potential switching of metabolic strategies became available with the solution of metabolic networks that make no prior assumptions about growth rates [19] . New approaches using spatiotemporal metabolic modeling with genome-scale metabolic networks enable quantification of cell growth rates , and the associated uptake of nutrients and excretion of intermediate ( by-product ) metabolites . Such approaches have been used to create biofilm models through an extension of Matlab with NetLogo named MatNet [20] , a population-based model of trophically interacting bacterial communities [21] , an extension of the MatNet approach to include multispecies communities [22] , as well as to investigate bacterial intra-colony nutrient heterogeneities [23] . Common to these modeling approaches is their ability to describe trophic dependencies of microbial species that automatically adapt to spatially and temporally varying nutrient conditions . However , all of the above-mentioned spatiotemporal metabolic models employ basic and unresolved representations of the physical microbial habitats that are considerably simpler than conditions in soil . To realistically simulate bacterial life in complex and partially saturated habitats , such as those found in soil , a description of the physical ( pore spaces and surfaces ) and characteristics of the aqueous habitats with the resulting diffusion fields is essential [5] . In this study , we developed and tested a new type of spatiotemporal metabolic model that assigns metabolic networks to individual agents capable of chemotactic motion within spatially resolved soil habitats . The physical domain is comprised of angular pore networks representing soil pores . The hydration conditions are imposed by a prescribed matric potential that interacts with the physical domain and results in aqueous phase configurations similar to those expected in soil ( with dual occupancy of the liquid and gas phases in angular pores ) . The matric potential is a component of the total soil water potential attributed to capillary and adsorptive forces between water and the soil solid matrix , with values ranging from 0 for saturated soil to large negative as the soil dries ( often expressed as negative pressure ) . The physical domain including the water phase serve as a backbone for numerical diffusion of nutrients and intermediate metabolites , with explicit account of diffusive bottlenecks for oxygen when pores are saturated or for nutrient diffusion in unsaturated soil , whilst maintaining connectivity of the aqueous phase throughout the simulated domain . Finally , bacterial cells are represented as individual agents with the ability to autonomously move and disperse , intercept nutrients and grow at rates determined by local ( often spatially variable ) nutrient conditions owing to the integration of metabolic networks . The model is compared to predictions obtained from a previously published population-based spatiotemporal metabolic network [21] and to bacterial spatial segregation observations from experimental data and simulation results from a previous IBM [13] . Additionally , the model was used to mimic experiments of glucose perfusion in soil aggregates [24] and strategically to highlight the influence of pore size distributions under wet and dry conditions with contrasting results–a primary distinction between IndiMeSH and other spatiotemporal metabolic network models .
A pseudo two-dimensional pore network composed of interlinked pores connected by nodes forming a lattice defines the spatial component of the model . The coordination number expresses the number of pores connected to each node within the lattice ( e . g . 4 for a chessboard , 6 for a honeycomb ) . Each pore has an angular cross-sectional area represented by either isosceles triangles [17] or rectangles ( when used to represent microfluidic networks [13] ) . Angular pores were selected due to their ability to simultaneously have liquid and gas phases under unsaturated conditions , an attribute not possible within cylindrical pores [26] . Different pore sizes are included by varying the inscribed circle radius of each individual pore ( for example taken from a lognormal distribution to represent soil pores [17] ) . The geometry of the triangle was determined by varying the central angle of the isosceles triangle . For rectangular pores , the width and height can be defined for each individual pore ( more detail is provided in [17] and [13] ) . A visual representation of parameters determining pore geometry is provided in S1 Fig . An important aspect of the model is its ability to represent different hydration conditions using the energy state of water or the matric potential . The matric potential determines the aqueous phase configuration ( amount retained in the pores and connectivity ) , hence it also shapes the nutrient diffusional landscape and constrains bacterial dispersal ranges . The water-filled cross-sectional area in each pore is calculated from the matric potential and geometrical considerations [27] as shown in Eq 1 . Here Aw is the water filled cross-sectional area ( m2 ) , At the total cross-section area of the pore ( m2 ) , Ψ the matric potential ( Pa ) , σ the water surface tension ( Nm-1 ) and γi the angle i within the corner of the angular pore . Pores are considered unsaturated whenever an air phase is present , specifically , when a continuous connection of unsaturated pores is available from a liquid-air interface at the boundary to the pore under consideration . We note that the water-filled cross-sectional area of a pore defines available pathways for nutrient diffusion and for the calculation of total nutrient storage in each pore ( depending on pore size , length , and local nutrient concentration ) . An important parameter of the aqueous phase in each pore is the effective water film thickness defined as the diameter of the largest sphere fully inscribed between the liquid-air interface and the pore wall [28] and calculated using Eq 2 . Here , WFT is the water film thickness defined as the smallest radius of an inscribed circle between the liquid-air interface and the pore wall ( m ) , Ψ the matric potential ( Pa ) , σ the water surface tension ( Nm-1 ) and γi the angle within the corner under consideration of the angular pore . The water film thickness affects the speed and range of bacterial motility , as further described below . Of particular importance is the fact that different angular pore geometries ( e . g . , pores with obtuse angles ) may retain water films that are too thin to support bacterial flagellated motion , hence , disconnect this bond from other parts of the network in terms of cell dispersion [29] . Despite considerable simplifications compared to natural pore networks , this formulation of physical pore network architecture and associated water configurations retains the salient features of the soil aqueous phase , such as continuity of liquid pathways for nutrient diffusion and cell dispersion and dual occupancy of the liquid and gas phases within angular pores . Diffusive fluxes of dissolved substrates within the aqueous phase are described by Fick’s law and are evaluated between nodes subject to mass balance at the nodes , as given by: Vw∂C∂t=AwD∂2C∂x2−R ( 3 ) where C is the substrate concentration ( M ) , D the diffusion coefficient in water ( m2s-1 ) , Aw the liquid cross-sectional area ( m2 ) , Vw the volume of water attributed to the node ( m3 ) , x the spatial coordinate along pores ( m ) , and R a sink term ( mols-1 ) attributed to the combined consumption of individual bacterial cells . The volume at each node is calculated using Eq 4 where Vw the volume of water attributed to the node ( m3 ) , Aw the liquid cross-sectional area of connected pore j ( m2 ) , Lj the length of the connected pore j ( m ) and d the degree of the node . Liquid films of pores sharing the same node are assumed to be continuous . Neglecting bacterial consumption , which is calculated separately using FBA , Eq 1 is solved numerically using an implicit scheme . Nutrient sources are represented by fixed concentration boundaries ( e . g . calculated maximum saturation for oxygen using Henry’s law ) . Consumption and production of nutrients is calculated as the sum of contributions from individual bacterial cells and subtracted from the local nutrient content before calculation of diffusion . Bacteria are represented as individual agents capable of dispersing ( by motility when the aqueous film thickness permits ) within the pore network , intercepting nutrients and growing according to locally available nutrient conditions . Bacterial cells respond locally and independently with no knowledge of global system properties such as concentration fields or bacterial density even at the nearest pore . Thus , global phenomena such as exponential population growth , spatial organization of bacterial species or diffusion fields due to bacterial consumption are emergent properties based on the local interactions of individual agents with their immediate chemical and physical surroundings . Bacterial cells divide when they reach a prescribed mass at division; they may be removed from the simulation if they deplete internal energy and reach a critical biomass [30] . We evaluated the proposed IndiMeSH framework by comparing it to a population-based metabolic model ( COMETS ) [21] and to experimental data [13] , and used it to mimic experiments of glucose perfusion in soil aggregates [24] to evaluate the potential of IndiMeSH to predict dynamic shifts in the total abundance of a bacterial community due to boundary condition perturbations . In addition , simulations with contrasting hydration conditions and varying pore size distributions were created to highlight the capability of IndiMeSH to simulate bacterial life in soil . A list of all user defined parameters can be found in S2 Table in the supplementary material .
The performances of genome-scale metabolic networks ( GEM ) and their reduced-scale networks ( rGEM ) were compared using flux variability analysis in varying environmental conditions . Table 2 summarizes the median percentage and standard deviation of common reactions that differ to varying degrees in their maximum or minimum flux between the GEM and rGEM models for all environmental conditions . Overall , the performance of the reduced-scale model was very similar to the genome-scale models , with approximately 85% of all reactions within the models having minimal flux and maximal flux differences of less than 1 mmol/gDW/h . In addition , the standard deviations were small for most metabolic network comparisons , with the exception of the E . coli metabolic networks . S4 Fig in the supplementary material relates the comparison of the metabolic networks to environmental conditions to elucidate potential biases due to specific combinations of nutrient uptake rates . The metabolic networks of E . coli perform differently at the combination of high lactose , low methionine and medium to high oxygen uptakes . - The aim of comparing COMETS with IndiMeSH was to verify IndiMeSH ability to predict bacterial community trophic interactions in the absence of prior assumptions regarding nutrient uptake ( in contrast with Monod kinetic models ) . Fig 2 shows the final ratio of E . coli and S . enterica after 48 h of simulated growth , including the experimental results and model simulations from the original COMETS publication [21] . Both inoculation ratios ( 99:1 and 1:99 as E . coli:S . enterica inoculated at 100 randomly chosen nodes ) were included , demonstrating the metabolic interdependence of the two species and the convergence of the system to the same stable community composition . No significant difference was found between IndiMeSH simulations and both COMETS experimental and simulation results ( p value of 0 . 66 and 0 . 33 , respectively , two-tailed t-test ) . Additional simulations were performed to ensure that the motility algorithm of IndiMeSH and changing to super agents resulted in similar biomass dispersion characteristics as in the diffusion algorithm used in COMETS . S3 Fig in the supplementary material shows the biomass dispersion for the COMETS algorithm and IndiMeSH run-and-tumble algorithm , both with and without super agents . To evaluate the ability of IndiMeSH predict the spatial organization of a bacterial community in pore networks due to nutrient cross-gradients ( where different metabolism is required ) , we compared IndiMeSH predictions with previously published experimental data [13] . Fig 3 depicts the resulting spatial distribution of P . putida ( further termed aerobes ) and P . veronii/P . stutzeri ( further termed facultative anaerobes ) along the shortest path from the central to the peripheral ports of the physical and simulated pore networks ( details of the physical domain and chemical boundary conditions are described in the methods and shown in S2 Fig of the supplementary material ) . The aerobes proliferate closer to the periphery than the facultative anaerobes , with a small subpopulation of facultative anaerobes residing at the oxygen-rich peripheral ports . The magnitude of the populations of both species is captured well both at the central and peripheral ports , although the location of aerobes is predicted to be in closer proximity to the peripheral port than observed experimentally . For the facultative anaerobes , the simulations capture both the central population utilizing anaerobic metabolism ( fermentation ) as well as the peripheral sub-population using aerobic respiration . We evaluated the capability of IndiMeSH to predict the shift in total bacterial population size due to perturbations in boundary conditions in the form of glucose perfusion into soil aggregates previously under certain steady ecological state . Simulations were set up to mimic experiments performed on 2 mm soil aggregates where the bacterial community was first grown into steady state for 7 days after which glucose was added at high concentrations to observe the short-term impact on the function and the spatial distribution of soil bacteria and fungi [24] . Fig 4 depicts the pore network employed for the simulation congruent to the reported pore size distribution in the experiment ( Fig 4A ) , the resulting diffusional scheme of the pore network calculated using Millington-Quirk equation ( Fig 4B ) as well as the total temporal response of the community during the pre-incubation and dynamic response to the addition of a glucose pulse ( Fig 4C ) . Due to the unsaturated conditions ( expressed by a matric potential of -10 kPa ) , the diffusion of nutrients confined to the aqueous phase was severely limited whereas oxygen in the gas phase was able to penetrate the pore network rapidly . The primary mode of growth was thus aerobic respiration within all regions of the pore network . During the pre-incubation period , the bacterial population grew into steady state dictated by the carrying capacity of the supplied carbon . Glucose addition after 7 days triggered a secondary rapid growth phase until a maximum is reached after approximately 8 days into the simulation . The population then starts to decline due to the lack of carbon availability , tending towards the previously established population size due to the system carrying capacity . Glucose perfusion does not have a homogeneous effect on the population concerning space . The relative increase in population size of the outer aggregate faction ( peeling off 1 mm of the aggregate conceptually ) compared to the inner faction is 2 . 5 to 1 . 1 , respectively . This is similar to the observation made within the experiments where they reported a total increase in cell number of 1 . 6 and 1 . 3 for the outer and inner faction , respectively . Within the simulation this is mostly due to the availability of carbon at the periphery which otherwise is intercepted by the population closer to the center of the aggregate . Thus , the initial spatial distribution of the community was disrupted by the change in carbon availability , creating a more heterogeneous distribution of the population . Finally , we created simulations with two contrasting hydration conditions in three different pore networks to highlight the capability of IndiMeSH to capture and predict the shift in metabolic activity due to the interplay of soil physics , aqueous configuration and bacterial metabolism . Pore size distributions resembling different soils are shown in Fig 5A with the related soil water characteristic curves shown in Fig 5B . A matric potential of -1 kPa and -10 kPa , was applied to simulate wet and dry conditions , respectively , with profound consequences for the hydration conditions for each network as indicated by the green lines in Fig 5B . The degree of saturation in turn dictates the relative diffusion of gaseous and aqueous nutrients as shown in Fig 5C . In dry conditions , aqueous nutrients are restricted in their diffusive capability due to thin water films whilst gaseous nutrient diffusion ( e . g . oxygen ) is facilitated . Due to the constant flux boundary conditions , bacterial populations reach similar magnitudes in wet conditions as shown in Fig 5D . Under dry conditions , diffusion of substrate in the large pore network is restricted more profoundly compared to the small pore networks , which results in a lower total carrying capacity . A main difference between the saturated and unsaturated simulation also lies in the metabolic activity of the population . Due to the penetration of oxygen deep into the aggregate for all pore sizes under dry conditions , the sole mode of growth is via aerobic respiration as shown in the consumption pattern for the small sized pore domain in Fig 5E . Under wet conditions , the additional consumption of nitrate indicates the use of denitrification pathways . Under these conditions , overflow metabolism occurs at the center due to an imbalance in electron donor and acceptor ratio resulting in the production of acetate , which is consumed at the periphery using aerobic respiration as shown in Fig 5F .
The IndiMeSH model has been developed to capture the multifaceted bacterial metabolism of individual-based agents inhabiting a complex habitat such as soil . The use of adaptive metabolic networks enables a life-like transition between aerobic and anaerobic conditions compared to empirical and arbitrary kinetic models . Additionally , FBA predicts uptake and secretion rates depending on locally variable nutrient conditions and thus enables simulations of trophically interacting bacterial species within a complex medium such as , but not limited to , soil . The heavy computational burden related to the motility of individual bacterial cells and the numerous iterations of the FBA requires the reduction of genome-scale metabolic networks using the previously published redGEM [36] and lumpGEM [37] algorithms . The implementation of reduced-scale metabolic models ( rGEM ) significantly reduced the computational time whilst retaining the salient features and predictability offered by the genome-scale models ( GEM ) ( Table 1 ) . Although such a reduction ( GEM to rGEM ) may not be crucial for very small physical domains , simulations of ecologically relevant systems where gradients of environmental variables persist would require spatial domains with a few thousand nodes . For example , in the micromodel comparison ( Fig 3 ) we considered approximately 3000 nodes and two species , thus solving 6000 FBA problems per time step; hence , the use of rGEM greatly improved computational efficiency . Compared to other individual based metabolic models the primary benefit of reduced models is not due to the physical domain size ( e . g . BacArena simulates domains containing 100 by 100 grid nodes [22] ) , but the small time step dictated by the motility algorithm and related large number of iterations in total . Except for E . coli , all reduced-scale models perform very similarly to their genome-scale counterparts . Reactions which have a net flux difference greater than 10 mmol/gDw/h are predominantly attributed to internal cycles within the genome-scale models or cycling of nutrients between compartments of the genome-scale model that are absent in the reduced-scale model , as has been previously reported [39] . There is a discrepancy in acetate metabolism between the reduced-scale and genome-scale models of E . coli . This is due to overoptimistic predictions of the genome-scale model where the predicted high fluxes can only be achieved through net CO2 fixation , which is unlikely [39] . The main difference between a group of spatiotemporal metabolic models and the newly developed IndiMeSH lies in the ability of the latter to represent complex habitats such as soil , including pore spaces , aqueous phase configurations and related constraints to chemical diffusion and bacterial dispersion . The IndiMeSH model is designed for a different range of applications than the COMETS model; however , we obtained comparable simulation scenarios through simple adjustments to the physical and biological domain of the model . These include the representation of bacterial cells as super agents as well as a reduction in velocity to account for growth on agar . The use of super agents has been implemented in the past [44 , 45] and in other studies have been shown to have limited effects on simulation results as long as the scaling ratio is sufficiently small [46] . Using these modifications , IndiMeSH accurately reproduced the COMETS results . These simulations rely on an appropriate reduction of the genome-scale models preserving the stoichiometric relations between consumed and excreted nutrients and demonstrate the ability of IndiMeSh to replicate and predict bacterial growth based on trophic interactions , a process ubiquitous in dense bacterial hotspots . Since both COMETS and IndiMeSH are essentially two-dimensional simulations , the oxygen concentration was kept artificially low to imitate conditions experienced by the layer of cells close to the agar itself . In reality , a bacterial colony growing on agar is a dynamic system where oxygen is consumed at the periphery , resulting in multiple micro niches and redox conditions within the colony itself [47] . Individual-based models coupled with metabolic networks focusing on colony growth are able to capture this phenomenon well [23] , however the scale of those models restricts simulations of a large domain with hundreds of colonies . The importance of emergent spatial patterns and bacterial spatial organization prompted us to examine how IndiMeSH reproduces previously published experimental data of bacterial distributions in micrometric pore networks dominated by carbon-oxygen counter gradients [13] . The original IBM represented facultative anaerobes as obligate anaerobes , which resulted in consistent discrepancies between model and experimental results especially for facultative anaerobes proliferating close to the oxygenated peripheral ports . Integration of metabolic networks as a means of growth calculation enabled us to capture the segregation of facultative anaerobes into a denitrifying population close to the carbon source and a subpopulation using aerobic respiration ( Fig 3 ) . Despite possibilities to represent facultative anaerobes using Monod kinetics and an arbitrary oxygen-threshold-switching strategy ( e . g . [15] ) , IndiMeSH managed to capture the facultative behavior more naturally without an artificial construct of bimodal metabolism . Originally , the interaction between the two species suggested an indirect commensalism where the anaerobes were required to release sufficient carbon to the periphery for complete oxygen consumption by the aerobes and provision of an anoxic niche . Through the integration of metabolic networks and additional intermediate metabolites in the form of acetate ( to ensure mass balance of carbon within the simulation ) , the cooperation between the species was shown to be a passive interaction where acetate was produced due to a lack of electron acceptor ( not sufficient nitrate available ) at the central port . Diffusion of acetate to the periphery enabled the aerobic community ( both facultative anaerobes and obligate aerobes ) to grow closer to the oxygen-rich peripheral port . This mechanism is less susceptible to perturbations and provides a more plausible explanation for cooperation compared to the scenario where one species “farms” the other . Abrupt perturbations are ubiquitous in all natural systems such as soil , either due to rapid changes in hydration conditions following rainfall or irrigation , or chemical alteration by the application of fertilizer or contaminants to the soil , or physical alterations such as tillage or rapid soil compaction . To test the capability of IndiMeSH to simulate such dynamic changes in environmental conditions , a simulation scenario was created where a change in electron donor availability alters the macroscopic conditions within a soil aggregate cross-section based on experimental results of glucose perfusion in soil aggregates [24] . These experimental results describe a non-uniform effect in space as evidenced from peeling off 1 mm of the aggregate with 1 . 6 fold increase in bacterial total abundance in the periphery relative to 1 . 3 in the core ( in the numerical experiment we observed 2 . 5 fold outer to 1 . 1 inner ) . The interpretation we provide to these interesting experimental results is that the perfusion with glucose alleviates the carbon flux limitations in natural aggregates , which is controlled by the inner ( largely anaerobic ) population . The perfusion creates a situation where carbon and oxygen become available at the periphery and promote the rapid growth during the availability window ( until the carbon pulse is consumed ) . In the simulation , the total population size starts to decline after peaking around 8 days into the simulation . This is not easily verified in the experimental data , but acknowledging that only a fraction of the total population was reported to be dividing ( approximately 4 . 5% to 8 . 2% of the total population [24] ) , assuming that the population has reached steady state or is declining does not seem farfetched . These simulations represent a scenario where the physical representation of soil in the form of angular pore networks plays a crucial in shaping the diffusional landscape of the microbial habitat and overall population growth–a unique feature of the presented model framework . An important aspect differentiating IndiMeSH from other current spatiotemporal metabolic network models is the accuracy with which the physical habitat of the bacterial cells is represented . Angular pore networks enable to link the aqueous phase configuration dictated by the pore geometry to the underlying chemical conditions and ultimately to the spatial landscape of bacterial metabolism . For the same matric potential , pore networks of different mean diameter expose bacteria to fundamentally different growth conditions as shown for instance in Fig 5B at -1 kPa . Both the medium and small pore networks are saturated ( nearly 100% ) at this matric potential whereas the pore network containing large pores is desaturated with profound consequences for oxygen diffusion dynamics and bacterial motility . Potentially more important is the ability of IndiMeSH to capture the change in aqueous phase configuration for the same pore network at varying matric potentials . As shown in Fig 5E , the change in matric potential not only alters the magnitude of nutrient consumption but also the relative ratio of for instance glucose to nitrate consumption . In wet conditions , the community is split into two distinct sub populations with one growing anaerobically via denitrification and producing acetate at the center and a second which grows via aerobic respiration of acetate at the periphery of the domain ( Fig 5F ) . In dry conditions , aerobic respiration is favored due to the increased gaseous diffusion at -10 kPa ( Fig 5C ) . The difference in population size depending on hydration conditions is due to the reduction in carbon diffusion under dry conditions . It is important to note that these contrasting conditions emerge due to a single change in model input files–the matric potential–which induces substantial changes to both the physicochemical aspects of the model as well as bacterial growth strategy . Hence , the physical description of the pore habitat , related diffusional constraints and bacterial self-organization in complex pore systems are features that distinguish IndiMeSH from other current spatiotemporal metabolic models such as COMETS[21] or BacArena[22] . IndiMeSH offers a representation of metabolic versatility with an individual-based model of bacterial life in soil habitats . Metabolic networks enabled a natural transition between aerobic and anaerobic pathways and associated nutrient consumption patterns , inducing trophic interactions without prior assumptions , as required by mathematical models using simple growth kinetics . The detailed description of the physical habitat and aqueous phase and emergent nutrient diffusion fields give rise to complex metabolic landscapes with changes in metabolic activity at the millimeter scale . Due to the modular design of the IndiMeSH model , individual components can be modified to represent , for instance , a different physical habitat , method of motility or metabolic network optimization ( thermodynamic FBA , FBA with molecular crowding ) . Thus , the applicability of the model is endless , ranging from the quantification of bacterial metabolism in soil , or the growth of bacteria during food spoilage , to the proliferation of Pseudomonas aeruginosa in the lungs of cystic fibrosis patients .
|
Soil bacterial communities are key players in global biogeochemical cycles and drive other soil regulatory and provisional ecosystem functions . Despite the relatively high bacterial abundance found in fertile soil , bacteria occupy only a small fraction of the soil surfaces and often form hotspots with disproportionate contributions to observed biogenic fluxes . As soil opacity and complexity limit detailed observations of such hotspots in situ , we have developed a modeling platform , IndiMeSH ( Individual-based Metabolic network model for Soil Habitats ) , to enable systematic study of dense multispecies bacterial communities within a structured habitat resembling ( but not limited ) to soil . The model is capable of representing multispecies trophic interactions and spatial self-organization in response to nutrient gradients , as confirmed in comparison with published results . IndiMeSH offers new opportunities for quantifying bacterial hotspot formation and dynamics and observe their resilience and response to perturbations in hydration and nutrient conditions .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"carbohydrate",
"metabolism",
"medicine",
"and",
"health",
"sciences",
"chemical",
"compounds",
"pathology",
"and",
"laboratory",
"medicine",
"oxygen",
"pathogens",
"metabolic",
"networks",
"carbohydrates",
"glucose",
"metabolism",
"organic",
"compounds",
"glucose",
"simulation",
"and",
"modeling",
"oxygen",
"metabolism",
"network",
"analysis",
"bacteria",
"research",
"and",
"analysis",
"methods",
"computer",
"and",
"information",
"sciences",
"chemistry",
"pathogen",
"motility",
"biochemistry",
"chemical",
"elements",
"organic",
"chemistry",
"virulence",
"factors",
"monosaccharides",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"metabolism",
"organisms"
] |
2019
|
Modeling metabolic networks of individual bacterial agents in heterogeneous and dynamic soil habitats (IndiMeSH)
|
Targeted therapy based on adjustment of microRNA ( miRNA ) s activity takes great promise due to the ability of these small RNAs to modulate cellular behavior . However , the efficacy of miR-101 replacement therapy to hepatocellular carcinoma ( HCC ) remains unclear . In the current study , we first observed that plasma levels of miR-101 were significantly lower in distant metastatic HCC patients than in HCCs without distant metastasis , and down-regulation of plasma miR-101 predicted a worse disease-free survival ( DFS , P<0 . 05 ) . In an animal model of HCC , we demonstrated that systemic delivery of lentivirus-mediated miR-101 abrogated HCC growth in the liver , intrahepatic metastasis and distant metastasis to the lung and to the mediastinum , resulting in a dramatic suppression of HCC development and metastasis in mice without toxicity and extending life expectancy . Furthermore , enforced overexpression of miR-101 in HCC cells not only decreased EZH2 , COX2 and STMN1 , but also directly down-regulated a novel target ROCK2 , inhibited Rho/Rac GTPase activation , and blocked HCC cells epithelial-mesenchymal transition ( EMT ) and angiogenesis , inducing a strong abrogation of HCC tumorigenesis and aggressiveness both in vitro and in vivo . These results provide proof-of-concept support for systemic delivery of lentivirus-mediated miR-101 as a powerful anti-HCC therapeutic modality by repressing multiple molecular targets .
Hepatocellular carcinoma ( HCC ) is one of the most common malignancy worldwide [1] . In China , HCC is the second highest cancer killer , which along accounts for 53% of all liver cancer deaths in the world [2] . HCC is often diagnosed at an advanced stage and there is no effective therapeutic strategy for non-resectable HCCs . so far , since highly active drug-metabolizing pathways and multidrug resistance transporter proteins in tumor cells always diminish the efficacy of current therapeutic regimens for HCC . Therefore , alternative modalities of treatment are urgently needed for this uniformly fatal disease . MicroRNA ( miRNA ) s are a class of highly conserved short RNAs that suppress gene expression [3] and have a functional contribution to cellular transformation and/or tumorigenesis [4 , 5] . In human neoplasms , some miRNAs are often up-regulated and may perform an oncogenic function , while most miRNAs are down-regulated and have a tumor suppressive activity [6] . Thus , potential therapeutic approaches in diseases , such as cancer , that target specific miRNAs have recently attracted attention [7] . Inhibition of oncogenic miRNAs through the use of antisense reagents is clearly one of the approaches [8] . On the other hand , miRNA-replacement therapy is another efficacious strategy [9] . In HCC , it was reported that systemic administration of miRNA ( miR ) -26a in a transgenic mouse HCC model could result in a dramatic suppression of HCC cell proliferation , induction of tumor-specific apoptosis and protection from disease progression without toxicity [10] . Recently , we and other groups have found that the levels of a specific miRNA , miR-101 , were frequently down-regulated in human HCC tissues , and ectopic overexpression of miR-101 dramatically inhibited HCC cells tumorigenicity and invasiveness in vitro by targeting MCL-1 and FOS , respectively [11 , 12] . More recently , it has been reported that miR-101 could inhibit autophagy and enhance cisplatin-induced apoptosis in HCC cells by targeting STMN1 [13] . In other solid tumors , the levels of miR-101 were also decreased in neoplastic tissues [14–17] , and miR-101 could inhibit the tumorigenesis and/or cancer progression by repressing the oncogenes EZH2 and COX2 [17–20] . These data suggest a powerful anti-tumorigenic activity of miR-101 in different human cancers . To date , however , the in vivo efficacy of miR-101 replacement therapy to human cancers , such as HCC , has not been elucidated . In the current study , we thus determined to investigate the therapeutic efficacy of systemic delivery of lentivirus-mediated miR-101 in an orthotopic liver implanted HCC model of mouse , and the tumor repressive functions of miR-101 in HCC and underling mechanisms were further studied .
The plasma levels of miR-101 were examined by Real-time PCR in 163 HCC patients and 50 healthy donors . To identify a single , optimal cutpoint for mature miR-101 , ROC curve analysis was applied to our HCC cohort to determine the cutoff score for high or low expression of miR-101 [21] . Tumors designated as “high expression” for miR-101 were those with scores above the value of 2 . 243928 . The average plasma levels of miR-101 were significantly lower in HCC patients with distant metastasis than that in HCCs without distant metastasis and control healthy donors ( Fig . 1A ) . High expression of plasma miR-101 was examined in 88/163 ( 54 . 0% ) of HCC patients . Correlation analysis showed that low level of plasma miR-101 in HCC patients was significantly associated with a more aggressive phenotype ( Table 1 , p<0 . 05 ) . Further survival analysis established that the plasma level of miR-101 is an independent prognostic factor for HCC patient survival ( p<0 . 0001 , Fig . 1B , Table 2 ) . It has been reported that HBx-mediated miR-101 down-regulation and subsequent induction of aberrant DNMT3A expression contributes to HBV mediated hepatocarcinogenesis [22] . We thus examined the levels of miR-101 in HBV-negative and HBV-positive HCC patient’s plasma . We found that there are no significant differences between the plasma levels of miR-101 in HBV-negative and HBV-positive HCC patients ( S1A Fig . ) . At the same time , so are the results in HBV-negative and HBV-positive HCC patient’s plasma with distant metastasis ( S1B Fig . ) . Consequently , it is unlikely that HBV infection itself induced the differential expression patterns of plasma miR-101 in our set of HCCs . In our study , we subsequently assessed the therapeutic efficacy of miR-101 via tail vein delivery to an orthotopic liver implanted HCC model of mouse . Lent-miR-101 , control lent-miR-ctr and physiological saline ( NaCl ) was administered , respectively , to mice by tail vein at one week after the preparation of the mouse HCC model , 2 times a week for a month . When mice got moribund , mice were euthanized . The liver , the lung and tumor xenograft were assessed . Firstly , we observed that the levels of coGFP in the liver , the lung and tumor tissues of lent-miR-101 treated mice were equivalent to that in lent-miR-ctr treated mice , exhibiting over 90% infection efficiency ( Fig . 2A , upper panel ) . But the expression levels of miR-101 in the liver , the lung and tumor tissues were significantly higher in lent-miR-101 treated mice than that in both control mice ( p<0 . 0001 , Fig . 2A , down panel ) . Meanwhile , the administrations of lent-miR-101 and lent-miR-ctr did not cause acute liver toxicity , as demonstrated by the maintenance of normal levels of serum markers of liver function ( S1 Table ) and an absence of overt histological evidence of toxicity ( S2 Fig . ) . Next , we found that mice in control groups developed larger sized primary tumors than that in lent-miR-101 treated mice ( Fig . 2B , p<0 . 01 ) . Moreover , we assessed the microvessel density ( MVD ) of tumors by Immunohistochemistry ( IHC ) staining of CD34[23] . The MVD-CD34 of tumors in lent-miR-101 group ( mean , 18; range , 9–46 ) was significantly smaller than that in both lent-miR-ctr ( mean , 41; range , 25–69 ) and NaCl ( mean , 43; range , 22–78 ) treated groups ( p<0 . 01 , Fig . 2C ) . Furthermore , we examined that the number of intrahepatic and pulmonary metastatic nodules was dramatically decreased in lent-miR-101 treated group compared to that in both control groups ( p<0 . 01 , Fig . 2D and 2E ) , and the mean survival time in lent-miR-101-treated mice was significantly longer than that in the control mice ( p<0 . 05 , Fig . 2F ) . In our study , the putative targets of miR-101 were predicted using target prediction programs , miRanda and TargetScan . We evaluated that besides the target genes of EZH2 , COX2 , STMN1 , MCL-1 and FOS identified previously [11–13 , 17 , 18] , the 3’-UTR of ROCK2 mRNA contains a complementary site for the seed region of miR-101 , the ROCK2 gene was an additional potential target of miR-101 ( Fig . 3A ) . To verify whether or not ROCK2 is a direct target of miR-101 , ROCK2 3’-UTR ( Fig . 3A ) and two mutants containing the miR-101 binding sites were cloned downstream of the luciferase open reading frame . These reporter constructs were used to cotransfect HCC LM9 cells . The luciferase activity assays showed that increased expression of miR-101 upon infection significantly affected the luciferase expressions of ROCK2 in LM9 cells . Conversely , when we performed luciferase assays using a plasmid harboring the 3’-UTR of ROCK2 mRNA , in which the binding sites for miR-101 were inactivated by site-directed mutant genesis , the luciferase activities of mutant reporters were unaffected by the simultaneous infection of miR-101 ( Fig . 3B ) . In addition , the mRNA and protein levels of ROCK2 were all substantially reduced after miR-101 overexpression in LM9 and Huh7 cells ( Fig . 3C and 3D ) . On the other hand , knocking down miR-101 in HepG2 and miR-101-LM9 cells , dramatically increased protein levels of ROCK2 ( Fig . 3E ) . Furthermore , IHC staining showed that ROCK2 expressions were down-regulated in HCC tissues of mice treated with systemic delivery of lent-miR-101 ( Fig . 3F ) . At the same time , we also confirmed that STMN1 and COX2 are the other targets of miR-101 in HCC ( S3–S4 Fig . ) . The Matrigel invasion and Wound healing assays demonstrated that miR-101 overexpression substantially decreased both LM9 and Huh7 HCC cells motility and invasive capability ( Fig . 4A; S5–S6A Fig . ) . Moreover , after miR-101 overexpression in LM9 and Huh7 lines , the expression levels of all tested epithelial markers ( E-cadherin , α-catenin and β-catenin ) increased , while the levels of mesenchymal markers ( fibronectin , N-cadherin and vimentin ) decreased ( Fig . 4B and 4C; S6B–S6C Fig . ) . We further evaluated the in vivo effects of miR-101 overexpression on HCC cell metastasis using an experimental in vivo metastasis assay . As demonstrated in Fig . 4D and 4E , the numbers of micrometastatic lesions in the liver and the lungs of mice were markedly reduced in lent-miR-101-LM9 group , as compared to that in the control groups . It is known , during tumor invasion and metastasis , changes in Rho-GTPase activity often lead to subsequent reorganization of actin cytoskeleton[4 , 24] . We investigated if miR-101 modifies HCC cell cytoskeleton rearrangement and inhibits Rho-GTPase . The F-actin staining showed that the stress fiber was observed in the control lent-miR-ctr-LM9 and lent-miR-ctr-Huh7 cells , but not in lent-miR-101-LM9 and lent-miR-101-Huh7 cells ( Fig . 5A and S7A Fig . ) and meanwhile , a lower level of GTP-RhoA , GTP-Rac1 and GTP-cdc42 was examined in lent-miR-101-LM9 and lent-miR-101-Huh7 cells as compared to that in lent-miR-ctr-LM9 and lent-miR-ctr-Huh7 cells ( Fig . 5B and S7B Fig . ) . To test the function of miR-101 in regulating angiogenesis , we also examined the effect of lent-miR-101-LM9 and lent-miR-101-Huh7 cells on angiogenesis in a CAM model . The results showed that ectopic overexpression of miR-101 in LM9 and Huh7 cells dramatically suppressed the angiogenesis in vivo ( Mock versus lent-miR-ctr , and versus lent-miR-101 LM9 cells were 112%±12 . 1% versus 98 . 67%±8 . 84% , and versus 46 . 33%±9 . 67% , and mock versus lent-miR-ctr , and versus lent-miR-101 Huh7 cells were 115%±8 . 6% versus 100%±10 . 8% , and versus 48%±6 . 3% , Fig . 5C ) .
It is known that most tumors are characterized by globally decreased expression of miRNAs[6] and enforced down-regulation of certain specific miRNAs may enhance cellular transformation and tumorigenesis[25] . Recently , accumulative evidences suggested that therapeutic delivery of certain miRNA ( s ) has a unique advantage in clinical use , since an individual miRNA may influence the cellular behavior through the regulation of multiple target genes and networks[26] . We and other groups previously identified a frequently down-regulated miRNA , miR-101 , in various solid tumors including HCC [11 , 12] . In the present study , our initial Quantitative PCR demonstrated that in a large cohort of HCC patients , all HCCs with distant metastasis had a low level of plasma miR-101 , and it was associated closely with an advanced clinical stage and predicted poor prognosis . Next , in an orthotopic liver implanted HCC model of mouse , we clearly showed that systemic delivery of lenti-miR-101 not only induced a dramatic suppression of tumor growth in the liver , but also substantially diminished HCC intrahepatic metastasis and distant metastasis to the lung and to the mediastinum , resulting in a dramatic suppression of HCC in mice without a measurable toxicity . The results reveal that therapeutic delivery of lenti-miR-101 in mouse potently inhibits HCC development/metastasis in vivo and thereby , establishing a principle that miR-101 may be useful as an effective anti-HCC agent through its ability to broadly suppress HCC cells tumorigenicity and invasiveness . In this study , we chose a lentivirus-based vector system as miR-101 delivery vehicle , since it is an attractive platform for regulatory gene delivery [27 , 28] . Importantly , the safety of using the lentiviral vector in preclinical research and clinical trials is a minimal concern and providing a therapeutic benefit for the patients [29–31] . In our mouse HCC model of the present study , we did observe that systemic delivery of lent-miR-101 exhibited an over 90% infective efficiency and high expression levels of miR-101 in the liver , the lung and tumor tissues of mice without toxicity , indicating that lentivirus provides an effective and nontoxic means to deliver miRNAs to mouse . Overall , our data hereby provide a basis for the concept that the systemic administration of miR-101 mediated by lentivirus might be a clinically viable anti-HCC therapeutic strategy . These results prompt us to further explore the functions and excise molecular mechanisms of miR-101 in the development and/or progression of HCC . Firstly , we observed that systemic delivery of lent-miR-101 in the mouse HCC model substantially inhibited tumor angiogenesis . In a CAM Model , we also demonstrated that enforced expression of miR-101 in HCC cells could diminish angiogenesis . In addition , ectopic overexpression of miR-101 in HCC cells not only suppressed cell motility , invasion and EMT , but also blocked the formation of cells stress fiber . In an experimental in vivo metastasis model of SCID mouse , we further showed that the tail-vein-injection of miR-101-overexpressing HCC cells led to a significant decrease in the number of metastatic lesions in the liver and in the lung . These data , collectively , strongly supported that miR-101 plays a crucial tumor suppressive role in the control of HCC aggressive process . We know that miRNAs exert their functions through regulating downstream target gene ( s ) . The oncogenes , EZH2 and COX2 , were two targets of miR-101 first identified in 2008 [17 , 18] and they were confirmed in our HCC cells of the present study ( S4 Fig . and S8 Fig . ) . EZH2 contributes to cancer metastasis via regulation of actin-dependent cell adhesion and migration [32] and implicated in EMT [33] and angiogenesis induction [34] . In human HCCs , we previously reported that both EZH2 and COX2 are frequently overexpressed in HCCs and positively correlated with high aggressive and/or poor prognostic phenotypes [21 , 35] . In addition , COX-2 inhibitors could prevent HCC cell growth both in vitro and in vivo [35 , 36] . These data suggest that EZH2 and COX2 are two important targets of miR-101 to suppress HCC . In 2009 , another direct target gene of miR-101 , MCL-1 , was identified by our group and enforced expression of miR-101 in HCC cells could dramatically decrease MCL-1 levels and thus , promoting apoptosis to suppress tumorigenicity [11] . Almost at the same time , Li et al [12] showed that miR-101 significantly repressed the abilities of HCC cell migration and invasion by targeting the FOS oncogene . And FOS can induce EMT in mammary epithelial cells [37] . More recently , Xu and colleagues [13] reported that miR-101 could inhibit autophagy and enhance cisplatin-induced apoptosis in HCC cells by targeting STMN1 . STMN1 is a key microtubule-regulatory protein and associated positively with HCCs vascular invasion , intrahepatic metastasis and advanced clinical stage [38 , 39] . In a series of in vitro and in vivo experiments of our present study , we not only confirmed that STMN1 is a target of miR-101 ( S2 Fig . ) , but also identified a novel direct target of miR-101 , ROCK2 , in HCC . Overexpression of ROCK2 was frequently examined in HCCs and it could induce EMT [4] and a more aggressive biological behavior [40] . These data suggest that miR-101 could enhance its inhibiting effects on HCC by targeting an additional oncogene ROCK2 . Cytoskeletal reorganization exemplified by the formation of stress fiber bundling arrays is essential for the contractile motion of cancer cells [24] . In addition , the RhoA/ROCK signaling plays important roles in multiple aspects of VEGF-mediated angiogenesis [41] . Among the members of the Rho family , RhoA , Rac1 and Cdc42 are 3 representative members defined in modulating the actin cytoskeleton [42] and may lead to EMT [43] , and RhoA activation may induce the formation of stress fibers [44] . In our study , we found that after miR-101 overexpression , the formation of stress fiber in HCC cells was inhibited , and concurrently , the levels of active RhoA , Rac1 and Cdc42 were all reduced . As a result of our collective present data , we therefore propose herein a molecular model , in which miR-101 broadly abrogates HCC tumorigenesis and metastasis by a direct suppression of multiple molecular targets and an inactivation of the RhoA/ROCK pathway ( Fig . 5D ) . To sum up , herein , we report , for the first time , an essential role for systemic delivery of lent-miR-101 in the efficient therapy of HCC in a mouse model , and the use of lentivirus vector has a unique advantage to enhance a transduction and therapeutic abundance of miRNA in vivo without toxic effects . Furthermore , functional and/or mechanistic studies of miR-101 as provided in this report , suggest a critical role of miR-101 in the control of HCC cells cytoskeletal reorganization , EMT , invasiveness and angiogenesis , resulting in a potent abrogation of HCC development and progression by means of a “one-hit/multiple-targets” mechanism .
One hundred and sixty-three cases of blood samples from patients with HCC were obtained from the residue of patient blood samples for the purpose of clinical diagnosis in the Clinical Laboratory of Sun Yat-Sen University Cancer Center , Guangzhou , China , between July 2005 and June 2010 . The HCC cases selected were based on distinctive pathologic diagnosis , availability of blood specimens , follow-up data , and had not received previous local or systemic treatment . In this HCC cohort , 147 cases were HCCs without distant metastasis , 16 HCCs had distant metastasis . Relevant corresponding clinical data of HCC patients were detailed in Table 1 . In addition , 50 cases of plasma samples from human healthy donors were utilized as control . Blood samples were processed and plasma was frozen within 2 hours of the blood draw . In the present study , the informed consents of participants have not been conducted and given , since 1 ) the privacy and personal identity information of all participants were protected , i . e . , all the data were analyzed anonymously , 2 ) all the blood samples were not and will not be used for any other purpose and 3 ) the waiver of informed consent did not and will not have adverse effect on the rights and health of the participants . This study was approved by the Institute Research Medical Ethics Committee at Sun Yat-Sen University Cancer Center . HCC cell lines Hep3B , Bel-7402 , SMMC-7721 and MHCC-LM9 were cultured in RPMI1640 medium with 10% newborn calf serum . Immortalized normal liver cell line MIHA , human embryonic kidney cell 293FT and HCC Huh-7 and HepG2 lines in DMEM were cultured with 10% fetal bovine serum . RNA was isolated from 400μl plasma using the mirVana PARIS kit ( Ambion , Carlsbad , CA ) following the manufacturer’s protocol . To allow for normalization of sample to sample variation in the RNA isolation step , synthetic cel-miR-39 was added to each sample as described by Mitchell et al [45] . These samples were processed under the exactly same conditions . To ensure the quality of RNA from plasma , we examined the levels of miR-16 , a miRNA displays the higher stability in plasma [46] , in 163 HCC patients and 50 healthy donors . The results showed that no significant difference was examined in terms of the plasma levels of miR-16 between healthy donors and HCC patients ( S1C Fig . ) . Total RNA from cell lines and tissues was extracted with TRIzol reagent ( Invitrogen , Carsbad , CA ) . cDNA was synthesized with the PrimeScript RT reagent Kit ( Promega , Madison , WI ) . Real-time PCR was carried out using an ABI 7900HT Fast Real-time PCR system ( Applied Biosystems , Foster City , CA ) according to the manufacturer’s recommended conditions . The primer sequences are provided in S2 Table . MicroRNA measurement by real-time PCR was performed in duplicate using Taqman universal PCR kit and miR-101 and RUN6B/ cel-miR-39 probe ( Applied Biosystems , Foster City , CA ) , in scaled-down ( 5μL ) reaction volumes using 2 . 5μL TaqMan 2× Universal PCR Master Mix with No AmpErase UNG , 0 . 25μL miRNA-specific primer/probe mix , and 2 . 25μL diluted RT product per reaction . At the end of the PCR cycles , melting curve analyses as well as electrophoresis of the products on 3 . 0% agarose gels were performed in order to validate the specific generation of the expected PCR product . Each sample was run in duplicates for analysis . △Ct was calculated by subtracting the Ct values of U6/ cel-miR-39 from the Ct values of the miRNA of interest . △△Ct was then calculated by subtracting △Ct of the control from △Ct of disease . Fold change of gene was calculated by the equation 2−△△Ct . Virus particles were harvested 48h after pCDH-CMV-miR-101-coGFP or pCDH-CMV-coGFP ( System Biosciences , CA ) transfection with the packaging plasmid pRSV/REV , pCMV/VSVG and pMDLG/pRRE into 293FT cells by using Lipofectamine 2000 reagent ( Invitrogen ) . Lentivirus-miR-101-coGFP ( lent-miR-101 ) and lentivirus-miR-ctr-coGFP ( lent-miR-ctr ) were condensed and purified for 108 MOI/200μl . Next , LM9 and Huh-7 HCC cells were infected by lent-miR-101 and lent-miR-ctr , respectively , to construct the stable miR-101-expressing and control HCC cells . MiR-101 inhibitor was synthesized by Genepharma ( Shanghai , China ) . The sequence of miR-101 inhibitor is UUCAGUUAUCACAGUACUGUA . SiRNA duplex oligonucleotides targeting human ROCK2 mRNA 5’-CAGAAGCGTTGTCTTATGCAA-3’ , targeting human STMN1 mRNA 5’-AAGAGAAACUGACCC-ACAAdTdT-3’ and targeting COX2 mRNA 5’-GCUGGGAAGCCUUCUCUAAdTdT-3’ were synthesized by Ribobo ( Guangzhou , China ) . Oligonucleotide transfection was performed with Lipofectamine 2000 reagents ( Invitrogen ) . The putative miR-101 binding sites at the 3’-UTRs of ROCK2 , STMN1 and COX2 mRNAs were cloned downstream of the cytomegalovirus ( CMV ) promoter in a pMIR-REPORT vector ( Ambion ) . Two mutant constructs were generated by either deletion or mutations . The primers used are shown in S2 Table . The firefly luciferase construct was cotransfected with a control Renilla luciferase vector into LM9 cells in the presence of either lent-miR-101 or lent-miR-ctr . Dual luciferase assay ( Promega ) was performed 48 hours after transfection . The experiments were performed independently in triplicate . Cell migration was assessed by measuring the movement of cells into a scraped , cellular area created by a 200-μl pipette tube , and the spread of wound closure was observed after 48 hours and photographed under a microscope . We measured the fraction of cell coverage across the line for migration rate . For invasion assays , 105 cells were added to a MatrigelTM Invasion Chamber ( BD Biosciences , Becton Dickson Labware , Flanklin Lakes , NJ ) present in the insert of a 24 well culture plate . Fetal bovine serum was added to the lower chamber as a chemoattractant . After 48 hours , the non-invading cells were gently removed with a cotton swab . Invasive cells located on the lower side of the chamber were stained with crystal violet , air dried and photographed . For colorimetric assays , the inserts were treated with 150μl 10% acetic acid and the absorbance was measured at 560 nm using a spectrophotometer ( Spectramax M5 ) . Proteins were separated on SDS-PAGE and transferred to nitrocellulose membrane ( Bio-Rad ) . The membrane was blocked with 5% non-fat milk and incubated with the corresponding mouse anti-ROCK2 , EZH2 , COX2 , E-cadherin , α-catenin , β-catenin , N-cadherin , fibronectin , vimentin ( BD Biosciences , 1:1000 dilution ) , STMN1 and α-tubulin ( Santa Cruz Biotechnology , Santa Cruz , CA , 1:1000 dilution ) and GAPDH ( Cell signaling Technology , Beverly , MA , 1:500 dilution ) monoclonal antibodies . The proteins were detected with enhanced chemiluminescence reagents . PAK1 PBD-agarose ( for isolating Rac1-GTP and cdc42-GTP ) and rhotekinagarose ( for isolating Rho-GTP ) ( Upstate Biotechnology , Lake Placid , NY ) were used to pull down the GTP-bound form of Rho-GTPase according to the manufacturer’s manual . The levels of active Rac1 , cdc42 and RhoA were detected by Western blot using specific polyclonal anti-Rac1 ( 1:1000 ) , anti-cdc42 ( 1:1000 ) and anti-RhoA ( 1:1000 ) antibodies ( Cell Signaling Technology , Beverly , MA ) . For the IF studies , cells were fixed with 4% paraformaldehyde in phosphate-buffered saline and permeabilized with 0 . 2% Triton X-100 in phosphate-buffered saline . Fixed cells were incubated with 1:2000 fluorescein isothiocyanate-conjugated phalloidin ( Sigma , St . Louis , MO ) or antibodies as indicated . Cells were counterstained with 4 , 6-diamidino-2-phenylindole ( DAPI ) ( Calbiochem , San Diego , CA ) and imaged with a confocal laser-scanning microscope ( Olympus FV1000 , Tokyo , Japan ) . Male nude mice ( BALB/C-nu/nu ) , 4–5 weeks old , 15–20 g , were obtained from the Center of Animal Control of Guangdong Province and maintained in an Animal Biosafety Level 3 Laboratory at the Animal Experiment Center of Sun Yat-Sen Cancer Center . We did the experiment 3–5 days after delivery of the mice to allow them to adapt to the environment . First , to establish an orthotopic liver implanted HCC model of mouse , 2 × 106 populations of LM9 cells , were injected subcutaneously into the flanks of BALB/C-nu/nu athymic nude mice . After 2 weeks , the subcutaneous tumors were resected and diced into 1 mm3 cubes , which were then implanted into the liver of mice . After the model construction , the mice have been intraperitoneally injected gentamycin in order to prevent abdominal infection , one time one day for three days . Then lentivirus-miR-101-coGFP ( lent-miR-101 ) , lentivirus-miR-ctr-coGFP ( lent-miR-ctr ) and physiological saline ( NaCl ) were administered at a dose of 108 MOI per animal by tail vein injection ( 200 μl total volume ) using a 30 gauge ultra-fine insulin syringe at a week after the model construction , 2 times a week for a month . Meanwhile , the general health status of the nude mice was observed everyday , including food intake , activity , and any abnormalities such as diarrhea and dehydration . The body weight was measured every 3 days . When animals became moribund , mice were euthanized by the cervical dislocation method . The liver and the lungs were removed and fixed with phosphate-buffered formalin . Subsequently , consecutive tissue sections were made for each block of the liver and the lung . The numbers of the intrahepatic and pulmonary metastatic nodules in the liver and the lung were carefully examined . All experimental procedures involving animals were are accordant with the Guidelines for the Care and Use of Laboratory Animals ( NIH publications Nos . 80–23 , revised 1996 ) and the laboratory animal ethics committee of Sun Yat-Sen University Cancer Center . The tissue blocks were cut into 5-μm sections and processed for IHC in accordance with a previously described protocol . [21] Eight 4-week-old male SCID-Beige mice in each experimental group were injected with lent-miR-101-LM9 , lent-miR-ctr-LM9 and mock-LM9 cells separately . Briefly , 1×105 cells were injected intravenously through tail vein into each SCID mouse in a laminar flow cabinet . Six weeks after cell injection , mice were sacrificed and examined . Fertilized chicken eggs were purchased from institute of zoo techniques and veterinary science ( Guangzhou , China ) , and incubated at 37℃ with 70% humidity for 8 days . Lent-miR-101 , lent-miR-ctr and mock LM9 cells were re-suspended in PBS buffer solution . Cells ( 2×106 cells , 15 μl ) were mixed with equal volume of Matrigel ( BD Biosciences ) . Aliquots ( 2×106 cells , 30 μl ) of the mixture were then applied onto the CAM of 9-day-old embryos . The area around the implanted Matrigel was photographed 4 days after the implantation , and the number of blood vessels was obtained by counting the branching of blood vessels . Assays for each treatment were carried out using 6 chicken embryos . Statistical analysis was performed using a SPSS software package ( SPSS Standard version 16 . 0 , SPSS Inc . ) . The ROC curve analysis was applied to define a cutoff score for plasma miR-101 level by a 0 , 1- criterion[47] . Briefly , the sensitivity and specificity for the outcome ( survival status ) were plotted to create a ROC curve . The score localized closest to the point ( i . e . , 0 . 0 , 1 . 0 ) at the maximum sensitivity and specificity was selected as the cutoff score to determine the greatest number of tumors that were correctly classified as having or not having the outcome . Data derived from cell line experiments are presented as mean ±SE and assessed by a Two-tailed Student’s t test . P values of <0 . 05 were considered significant . Human miR-101 , MI0000937; human ROCK2 , Homo sapiens ROCK2 NM_004850; Human STMN1 , NM_005563 . 3 Homo sapiens stathmin 1 ( STMN1 ) , transcript variant 3 , mRNA . This study was approved by the Institute Research Medical Ethics Committee of Sun Yat-Sen University Cancer Center , Guangzhou , China .
|
Human hepatocellular carcinoma ( HCC ) is one of the most common malignancy worldwide and among the leading causes of cancer-related death . HCC is often diagnosed at an advanced stage and there is still no effective therapeutic strategy for non-resectable HCCs . It has been suggested that the therapeutic delivery of certain miRNA ( s ) has a unique advantage in clinical use . We first find that the plasma levels of miR-101 are significantly down-regulated in HCC patients with distant metastasis and associated closely with HCCs progression and/or worse disease-free survival ( DFS ) . Next , we identify that systemic delivery of lentivirus-mediated miR-101 in an orthotopic liver implanted HCC model of mouse , not only suppresses tumor xenograft growth in the liver , but also substantially blocks intrahepatic metastasis and distant metastasis to the lung and to the mediastinum , resulting in a dramatic abrogation of HCC tumorigenesis and progression in mice without toxicity . Furthermore , functional and/or mechanistic studies of miR-101 demonstrate that miR-101 in HCC cells inhibits Rho/Rac GTPase activation , and blocks HCC cells epithelial-mesenchymal transition ( EMT ) and angiogenesis , inducing a strong abrogation of HCC tumorigenesis and aggressiveness both in vitro and in vivo .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Systemic Delivery of MicroRNA-101 Potently Inhibits Hepatocellular Carcinoma In Vivo by Repressing Multiple Targets
|
Boundary domains play important roles during morphogenesis in plants and animals , but how they contribute to patterning and growth coordination in plants is not understood . The CUC genes determine the boundary domains in the aerial part of the plants and , in particular , they have a conserved role in regulating leaf complexity across Angiosperms . Here , we used tooth formation at the Arabidopsis leaf margin controlled by the CUC2 transcription factor to untangle intertwined events during boundary-controlled morphogenesis in plants . Combining conditional restoration of CUC2 function with morphometrics as well as quantification of gene expression and hormone signaling , we first established that tooth morphogenesis involves a patterning phase and a growth phase . These phases can be separated , as patterning requires CUC2 while growth can occur independently of CUC2 . Next , we show that CUC2 acts as a trigger to promote growth through the activation of three functional relays . In particular , we show that KLUH acts downstream of CUC2 to modulate auxin response and that expressing KLUH can compensate for deficient CUC2 expression during tooth growth . Together , we reveal a genetic and molecular network that allows coordination of patterning and growth by CUC2-defined boundaries during morphogenesis at the leaf margin .
In all multicellular organisms , morphogenesis relies on the tight control of two intimately linked processes: patterning , which subdivides the tissues in groups of cells with different fates , and growth , which increases tissue size [1] . Such a coordination can be achieved in animals through the production of diffusible signals , or morphogens , that can both regulate proliferation and determine different cell fates in a concentration-dependent manner [2–4] . These morphogens are produced by specific groups of cells , called organizers , that are often located at the boundary between domains with different identities [5 , 6] . Morphogenesis in plants differs from animal development by several aspects as for instance no cell migration and only little cell death occur . Furthermore , the existence of morphogens in plants is still questioned although the plant hormone auxin , small RNAs or small peptides have been proposed to have a morphogen-like activity [7–9] . On the contrary , it has been clearly established that proper plant morphogenesis relies on the formation of functional boundary domains [10–12] . In plants , several types of boundaries have been described . For instance , in the developing leaf , a boundary lies at the junction of the adaxial and abaxial domains [13–16] . During early phases of leaf development , these two domains are directly adjacent and it is only later that interactions between these two domains lead to the formation of a third middle domain a few cells wide [17–19] . Another type of boundary domain is widely found in the aerial part of the plant and is directly related to the plant-specific mode of organogenesis that occurs at the shoot apical meristem . These inter-organ boundaries are narrow cellular domains located between an organ and either , the meristem from which it was initiated , or a neighboring organ . These domains are genetically defined by the expression of several genes . The central role of these boundary domains for plant development is demonstrated by the large range of developmental abnormalities observed following the inactivation of one or several of these genes [10–12] . Hence , mutants affected in boundary function show defects in the initiation of new growth axes: meristem formation is perturbed during the embryonic and post-embryonic phase , leading respectively to lack of a shoot apical meristem and branching defects [20–23] and placenta and ovule formation alterations [24–26] . In addition , boundary mutants exhibit fusions between organs such as adjacent floral organs or between inflorescence stem and floral pedicels [23 , 27] , have a modified phyllotaxy during stem growth [27 , 28] and present reduced leaf shape complexity [29–32] . Because boundary domains are located in grooves between outgrowing structures , the cells they are formed of share specific characteristics . For instance , boundary cells show negative Gaussian curvature and experience a highly anisotropic mechanical stress that leads to the alignment of the cortical microtubule network along the main axis of the boundary domain [33–35] . In addition , growth of boundary cells is reduced and occurs preferentially parallel to the cortical microtubule network orientation while it is perpendicular in most plant cells [33 , 35 , 36] . This particular boundary growth pattern is associated with a depletion of several hormones from the boundary domain [11] . Indeed , auxin is depleted from boundary cells as a result from divergent distribution of the auxin efflux carrier PIN FORMED 1 ( PIN1 ) [37 , 38] and boundary cells show reduced brassinosteroid signaling [39] . In turn , reduced auxin and brassinosteroid signaling , combined with mechanical stresses contribute to shape the specific expression pattern of boundary genes [39–43] . How boundaries control plant development has been initially analyzed in the context of meristem development and more recently of leaf shaping . Leaves are initiated as small primordia at the meristem periphery and go through a process of morphogenesis and growth to acquire their mature shape and size [44–46] . In particular , new growth axes can be formed at the leaf margin and will , depending on the species , develop into small outgrowths such as the teeth of the serrated Arabidopsis leaf , or larger structures such as the leaflets of the compound tomato leaf . Definition of a boundary domain at the leaf margin by the activity of transcription factors from the NO APICAL MERISTEM/CUP-SHAPED COTYLEDON 3 ( NAM/CUC3 ) family is required for these marginal outgrowths to properly form [20 , 21 , 47] . For instance , in Arabidopsis , serration formation is affected in cuc2 mutants while conversely more pronounced serrations are observed when CUC2 expression levels are increased as a result of reduced activity of its regulatory miRNA , miR164 [29 , 48 , 49] . CUC3 also contributes to serration development , while CUC1 , the third member in Arabidopsis is not expressed and appears to have no role during leaf shaping [48] . More generally , reducing NAM/CUC3 activity during leaf development leads not only to defects in marginal outgrowth separation , such as leaflet fusion , but also to patterning defects reflected by the abnormal number and position of leaflets [30–32 , 50] . Similar defects in the patterning of the leaf marginal outgrowth and separation are observed in mutants in which the formation of localized auxin response is perturbed [51–54] . Indeed , it has been shown that leaf marginal outgrowth formation relies on an interdependency between boundaries and auxin signaling: CUC2 activity is required for building up discrete maxima of auxin response via a modification of polar auxin transport and , conversely , localized auxin response is required for proper CUC2 expression [55 , 56] . In addition to auxin , other plant hormones , including gibberellic acid and cytokinins , contribute to leaf margin morphogenesis [57–59] . The KLUH gene , which encodes for a cytochrome P450 protein , extends cell proliferation duration , possibly through an unknown mobile signal distinct from the classical plant hormones [60–62] . Therefore , while leaf margin patterning appears to rely on the interplay between CUC genes and auxin signaling , which factors control later tooth outgrowth and how these patterning and growth processes are intertwined is not understood . Here , we used tooth formation at the leaf margin as a model to dissect the mechanisms by which the inter-organ boundary domain coordinates patterning and growth to direct morphogenesis . To help separating linked events , we used conditional restoration of CUC2 , a regulator of leaf boundary , to induce tooth formation and analyze the downstream molecular effects leading to morphogenesis . Using this system , we showed that a transient pulse of CUC2 expression is sufficient to trigger both patterning and growth of margin serrations , and characterized the contribution of three genetic or molecular actors that can act as functional relays for CUC2 . The role of these actors revealed by the CUC2 conditional expression system was confirmed in wild-type tooth morphogenesis . In particular , we highlight the role of KLUH in relaying CUC2 as a promoter of tooth outgrowth and present evidence that it may act by modulating auxin response . Thus , we propose a sequential scenario accounting for the coordination of patterning and growth via a network activated by the CUC2 boundary gene during leaf serration .
With the aim of trying to separate the contribution of CUC2 during leaf margin patterning and tooth outgrowth , we reexamined the early leaf phenotype of two cuc2 mutant alleles , cuc2-1 and cuc2-3 [29 , 48] . Both mutants have been described as developing leaves with smooth margins when grown under long day conditions [29 , 48] . Because leaf serration is more pronounced in plants grown in short-days , we reexamined the early leaf phenotype of cuc2-1 and cuc2-3 plants grown under these conditions . While leaf margins of cuc2-1 were smooth , small teeth formed along cuc2-3 primordia ( Fig 1A and 1B ) . This phenotype difference prompted us to reexamine the molecular basis of the two mutations . cuc2-1 carries a Tag1 transposon insertion in the first exon [21] while cuc2-3 has a T-DNA insertion 99bp upstream of the ATG [23] . While CUC2 mRNA level was less than 5% of the wild type in cuc2-1 it represented about 20% in cuc2-3 ( S1 Fig ) . Therefore cuc2-3 is a hypomorphic allele of CUC2 while cuc2-1 is likely to be a true null allele . To more precisely characterize leaf margin patterning in these mutants , we introduced a pCUC3:CFP and a pMIR164A:RFP reporter in both genetic backgrounds . In the wild type , both reporters mark the boundary domain , while pMIR164A:RFP also marks the tip of the outgrowing teeth ( Fig 1C ) [29 , 48] . pCUC3:CFP showed a weak , continuous expression in cuc2-1 while pMIR164A:RFP expression was not detected at the margin , suggesting that no patterning of the leaf margin associated with tooth formation occurred in the cuc2-1 mutant ( Fig 1C ) . Conversely , in cuc2-3 the expression of both markers was discontinuous like in the wild type , albeit weaker , confirming that patterning of the leaf margin occurred ( Fig 1C ) . Next , we quantified the rate of tooth outgrowth , which we found to be reduced to about 1/3 of the wild-type level in cuc2-3 ( Fig 1D ) . Therefore , this indicates that in cuc2-1 both patterning and tooth outgrowth are compromised while in cuc2-3 both processes occur , although growth is severely reduced . Altogether , this indicates that these mutants do not allow separating the roles of CUC2 in patterning and growth . Next , we tested whether the roles of CUC2 in patterning and growth could be separated by manipulating the timing of CUC2 expression . For this , we manipulated CUC2 expression timing using the ethanol-switch strategy [63 , 64] to produce inducible restoration of CUC2 expression under its own promoter for different durations . The ethanol-inducible construct containing an RFP-tagged version of CUC2 was introduced in cuc2-1 and this line was designated CUC2i ( Fig 2A ) . Ethanol induction restored tooth formation ( Fig 2B and S2A Fig ) , but not all leaves could form teeth , as leaves that were longer than about 1200μm did not respond ( S2B Fig ) . Increasing induction time lead to more teeth initiated at the margin ( S2C Fig ) , suggesting that in CUC2i , multiple teeth are formed sequentially as in the wild type . Notably , an 8h induction was sufficient to form on average about one pair of teeth per leaf ( Fig 2B and S2C Fig ) . To characterize CUC2-induced morphological changes at the leaf margin , we analyzed in parallel the evolution of two shape descriptors Tooth Aspect Ratio ( reflecting anisotropic deformation during tooth growth ) and Sinus Angle ( reflecting local deformation due to growth repression at the sinus , see S2D Fig ) . Morphometric analyses revealed that , following an 8h induction , teeth started to emerge at 48h , with tooth aspect ratio and sinus angle continuously increasing and decreasing respectively until 168h ( Fig 2C and 2D ) . Next , we wanted to characterize how an 8h-long ethanol induction translates into CUC2 protein accumulation dynamics . For this , we took advantage of the RFP tag to characterize RFP-CUC2 pattern ( Fig 2E and 2F ) after an 8h induction . Eight hours after an 8h induction , RFP-CUC2 formed a continuous domain along the margin . At 28 hours after induction , RFP-CUC2 pattern started to become discontinuous ( seen in 10/22 samples ) , while at 52h RFP-CUC2 could not be detected anymore . To be able to visualize RFP-CUC2 dynamics for a longer time , we next induced the CUC2i line 8h per day for 3 days ( 3x8h ) . Following such a 3x8h induction , RFP-CUC2 clearly resolved into discontinuous domains , with a higher expression at the sinuses of the small outgrowing tooth visible at 52h ( Fig 2E ) , like observed for the expression of the translational reporter pCUC2:CUC2:VENUS ( right panel in Fig 2E ) . Therefore , RFP-CUC2 expression resolved in about 48 hours after induction from a single domain to discontinuous domains , a feature required for CUC2 function during wild-type tooth morphogenesis [56] . Both quantifications of RFP-CUC2 fluorescence and real time RT-PCR showed that following an 8h induction RFP-CUC2 level transiently increased for about 24h-30h hours before decreasing and becoming undetectable between 48h to 72h ( Fig 2F and S2E Fig ) . Repeated induction ( 3x8h ) leads to higher and prolonged RFP-CUC2 levels that however became undetectable at 80h ( Fig 2F ) . Altogether , this indicates that following an 8h ethanol induction , CUC2 is transiently expressed and recapitulates the spatial dynamics observed during wild-type development ( Fig 2E and 2F ) . This pulse of CUC2 expression is not only sufficient to trigger patterning of the leaf margin resulting in tooth initiation , but also for sustained tooth outgrowth that persists after CUC2 expression becomes undetectable ( Fig 2C and 2D ) . We therefore conclude that CUC2 is sufficient to promote patterning at the leaf margin and is dispensable for later tooth growth , indicating that it acts as a trigger for tooth morphogenesis . CUC2 may act as a trigger for tooth morphogenesis via two possible scenarii . It may act as a licensing factor allowing growth to occur . In this scenario , the growth rate would not be related to the level of CUC2 expression . Alternatively , CUC2 may promote growth which rate would therefore be linked to CUC2 level . Earlier observations indicating that the level of leaf serration in mature leaves is related to CUC2 expression levels [29 , 48 , 49 , 55 , 56 , 65] do not allow to discriminate between these scenario as larger serrations in mature organs may result for instance from faster tooth growth or from prolonged growth . Therefore , to further characterize how CUC2 promotes growth we investigated the link between CUC2 protein levels and tooth growth rate . For this , we determined tooth growth rate by morphometrics and quantified the CUC2-VENUS signal in several cuc2-1 or cuc2-3 mutants complemented by a translational pCUC2:CUC2-VENUS reporter . Hence , a positive correlation between mean CUC2 protein levels and tooth growth rates was observed in four cuc2-1 backgrounds that had different CUC2 expression levels as a result of different pCUC2:CUC2-VENUS integration site or copy number ( Fig 3A and S3A and S3B Fig ) . This correlation was also observed in six cuc2-3 backgrounds with a much wider range of pCUC2:CUC2-VENUS expression levels as a result of reduced miRNA inhibition of CUC2 level ( Fig 3B and S3C and S3D Fig ) . Altogether , this indicated that CUC2 acts as a trigger that promotes tooth outgrowth in quantitative manner . A corollary of this observation is that CUC2 may activate one or several downstream factors that act as functional relays to maintain tooth outgrowth after CUC2 disappearance . Because CUC3 is partially redundant with CUC2 in shoot development [23 , 26 , 27 , 47] and contributes to sustained serration outgrowth [48] , CUC3 appeared as one of the possible relay for CUC2 activity . This hypothesis is supported by the expression of the pCUC3:CFP transcriptional reporter in sinuses between emergent teeth in a pattern similar to CUC2 ( Fig 4A ) . We first followed pCUC3:CFP expression in CUC2i after an 8h induction ( Fig 4B ) . Before induction , pCUC3:CFP expression was low and continuous along the margin in the sub-epidermal layers , a pattern identical to the one observed in the cuc2-1 background ( Fig 1C ) . Next , at 28h after induction , pCUC3:CFP expression became more intense and was observed in some epidermal cells ( observed in 12/22 samples ) that had high RFP-CUC2 levels ( S4A Fig ) . At 52h , pCUC3:CFP pattern became discontinuous , disappearing from the outgrowing tooth and finally being mostly restricted to its distal sinus at 100h ( Fig 4B ) . The increase in pCUC3:CFP expression was confirmed by real time RT-PCR analysis on microdissected CUC2i leaf margin tissues that showed higher CUC3 transcript levels at 24 and 48h after induction ( Fig 4C ) . Having shown that CUC3 expression is modified by CUC2 induction , we next tested whether CUC3 also acts as a functional relay for CUC2 . For this , we compared morphometric parameters after ethanol induction in CUC2i and CUC2i cuc3-105 backgrounds ( Fig 4D and S4B Fig ) . The increase in tooth aspect ratio is delayed in CUC2i cuc3-105 compared to the CUC2i control from 96h onwards and , although the sinus angle is initially identical , CUC2i cuc3-105 show more open angles from 120h onwards ( S4B Fig ) . Together , these analyses showed that CUC3 contributes in a quantitative manner to tooth outgrowth following CUC2 induction . Several observations suggested that the role of CUC3 as a functional relay of CUC2 is not limited to the CUC2i line . First , parallel quantification of CUC3 and CUC2 promoter activities in tooth sinuses during wild-type leaf development revealed a strong correlation between them ( S4C Fig ) . Second , three lines with increased CUC2 levels compared to wild type ( mir164a-4 and ago1-27 , Fig 3B and CUC2g-m4 ) also presented an increase in pCUC3:CFP activity ( S4D Fig ) . Third , precise morphometric analysis showed that in addition to a late defect in tooth growth already reported [48] , cuc3-105 tooth growth was also reduced early on during development compared to wild type ( Fig 5A–5D , green compared to red ) . Indeed , tooth aspect ratio was significantly reduced in cuc3-105 leaves with blades longer than 250μm for tooth 1 and longer than 500μm for tooth 2 ( Fig 5 , green compared to red ) . Fourth , the cuc3-105 mutation partially suppressed the increased serration of mir164a-4 and CUC2g-m4 ( Fig 4E and S4E Fig ) . In conclusion , our results show that CUC3 expression level and spatial dynamics are determined by CUC2 and that CUC3 is required for tooth growth . Based on these observations and because CUC3 is essentially expressed in the same domain as CUC2 , we conclude that CUC3 acts as a local functional relay for CUC2 activity during tooth growth . Because CUC expression interacts with auxin response during the formation of new growth axes [24 , 38 , 56 , 66] we investigated whether auxin response mediates CUC2 promoting effect on growth . First , we monitored auxin response after CUC2i induction using pRPS5a:DII-VENUS ( DII-VENUS [67] ) and pDR5:VENUS [38] , which respectively report early and late steps of auxin signaling ( Fig 6A , S5A and S5B Fig ) . Clear localized auxin response could be detected at 48h ( Fig 6A and S5A Fig ) . While the pDR5:VENUS positive domain was maintained until 144h , the domain revealed by the more dynamic DII-VENUS reporter tended to shrink at 127h , suggesting that the local auxin response started to decrease . Because the level of the pDR5:VENUS reporter increased in lines with higher CUC2 expression levels ( S5C Fig ) , it suggests that CUC2 levels are translated into different auxin response intensities . Next , to test the contribution of local increased auxin response to tooth growth at different stages of tooth development we used a pharmacological approach . 1-N-naphthylphtalamic acid ( NPA ) , a polar auxin transport inhibitor , was sprayed on rosettes at different time points relative to ethanol induction ( for instance , NPA@24h designates NPA treatments that start 24 hours after ethanol induction ) . Such treatments perturbed auxin response patterns shown by the pDR5:VENUS reporter as early as 3h following NPA application ( S5D Fig ) . NPA application at any stage of tooth formation ( from @24h to @96h ) impacted localized auxin response as monitored by the pDR5:VENUS reporter ( S5E–S5H Fig ) , but had no effect on overall leaf blade growth ( S5I and S5J Fig ) . NPA application starting early relative to ethanol induction ( from @-48h to @48h ) leads to the most severe inhibition of tooth outgrowth ( Fig 6B and S5K and S5M Fig ) . Conversely , a progressive release of tooth growth inhibition was observed when NPA was applied at later times ( from @48h to @144h , Fig 6C and S5L and S5N Fig ) . Because in the case of late NPA applications ( from @48h to @144h ) , localized auxin response could build up from 48h ( Fig 6A and S5A Fig ) to the time of NPA application , we concluded that localized auxin response continuously promotes tooth outgrowth and that it can therefore act as a long-lasting and quantitative functional relay contributing to tooth outgrowth after CUC2 becomes undetectable . To explore the existence of additional CUC2 relays , we reasoned that they should be expressed in a pattern similar to CUC2 . Among the boundary enriched genes listed by [68] , figured the KLUH/CYP78A5 gene that was also previously described as expressed in meristem boundaries [69] . Interestingly , KLUH has been described as a non-cell autonomous regulator of cell proliferation in flowers and during seed development [60 , 62 , 70] . Although KLUH is expressed in the leaf [61] , it is not known if it is expressed during serration formation . To test this , we followed the expression of a pKLUH:GFP reporter during wild-type leaf development ( Fig 7A ) . In addition to the expression at the base of the petiole , pKLUH:GFP is also expressed in the sinuses of developing teeth . This prompted us to test whether KLUH is involved in the CUC2-triggered tooth formation process . First , we monitored KLUH dynamics after CUC2i ethanol induction ( Fig 7B ) . Interestingly , no pKLUH:GFP could be detected at the leaf margin before ethanol induction , confirming that KLUH expression is associated with boundary formation . Twenty-four hours after CUC2 induction , pKLUH:GFP was expressed at the leaf margin and became localized to the sinuses at 48h , overlapping with CUC2 ( Fig 7B and S6A Fig ) . Then , pKLUH:GFP expression rapidly decreased , being almost undetectable at the leaf margin at 96h . Quantification of KLUH mRNA levels by RT-qPCR on microdissected leaf margins confirmed its transient upregulation following ethanol induction ( Fig 7C ) . Next , we tested the contribution of KLUH to CUC2-triggered tooth outgrowth by comparing tooth morphology in presence or absence of functional KLUH following CUC2 induction ( Fig 7D and S6B Fig ) . Surprisingly , 72h after induction , teeth were pointier and the sinus angle more pronounced in CUC2i kluh-4 compared to CUC2i . Later , morphological parameters became identical for teeth of both lines , while at 168h teeth were flatter with shallower sinus angles in the CUC2i kluh-4 background compared to CUC2i . This complex effect of kluh-4 on tooth growth was not limited to ethanol-induced tooth , as morphometric analysis of kluh-4 revealed identical defects ( Fig 5 , blue compared to red ) . In small leaf primordia ( blade <500μm ) both teeth 1 and 2 of kluh-4 were pointier compared to wild type , while in larger primordia ( blade >1000μm ) they were flatter in the kluh-4 mutant . In addition , KLUH expression level correlates with CUC2 levels as RT-qPCR quantification showed that KLUH mRNA levels were increased in CUC2g-m4 ( S6C Fig ) . The kluh-4 mutation also partially suppressed the increased leaf serration of miR164a-4 and CUC2g-m4 ( Fig 7E and S6D Fig ) . We conclude from these results that CUC2 activates KLUH expression at the leaf margin . In turn , expression of KLUH has a dual role , transiently repressing tooth growth during the early stages of tooth formation while promoting it later . To further test whether KLUH conveys some of the growth promoting effect of CUC2 , we expressed a pCUC2:KLUH construct in the cuc2-3 background in which tooth growth is severely reduced ( Fig 8A–8D ) . Tooth development was partially restored in cuc2-3 pCUC2:KLUH lines , as more teeth could be observed and as they were pointier and had more pronounced sinus . When CUC2 gene dosage was further reduced in the cuc2-1/cuc2-3 heterozygote a similar partial restoration was observed in small primordia ( Fig 8E–8G ) and extended to almost fully expanded leaves ( Fig 8H ) . This indicates that expressing KLUH in the boundary can partially compensate for reduced CUC2 activity during tooth formation and establishes KLUH as an important transiently-activated relay for CUC2-triggered tooth formation . We showed above that CUC2 expression at the future sinus sites induces locally the expression of two boundary genes , CUC3 and KLUH , and leads to strong auxin response at a distance . Because tooth morphogenesis requires a coordination between growth repression at the sinus and growth promotion at the tip , we next tested the interactions between factors acting in the sinus and acting at the tip of the tooth . To test whether CUC3 and KLUH also contribute to localized auxin response , we monitored pDR5:VENUS after induction in CUC2i , CUC2i cuc3-105 and CUC2i kluh-4 backgrounds ( Fig 9A , 9B and 9C ) . pDR5:VENUS upregulation at the leaf margin appeared with a 24 hours delay in CUC2i cuc3-105 compared to CUC2i , and the area of cells expressing the reporter and its expression level remained lower than in CUC2i until 120h ( Fig 9A and 9B ) . This indicates that CUC3 contributes , along with CUC2 , to properly set the dynamic and intensity of the local auxin response maximum . Monitoring pDR5:VENUS expression in CUC2i kluh-4 revealed a complex modification compared to CUC2i ( Fig 9A and 9C ) : pDR5:VENUS expression was detected earlier in CUC2i kluh-4 at 24h compared to CUC2i and remained stronger until 96h when it became similar in CUC2i and CUC2i kluh-4 . Later at 120h and 144h , pDR5:VENUS expression was weaker in the kluh-4 background . This indicates that the changing effects of KLUH on tooth outgrowth depending on the developmental stages ( Fig 7D ) are correlated with similar effects on the auxin response visualized by pDR5:VENUS . Next , we conversely tested the contribution of localized auxin response to the dynamics of CUC3 and KLUH patterns . When NPA was applied early following ethanol induction ( @24h or @48h ) pCUC3:CFP expression remained low and continuous along the margin ( Fig 9D and 9E ) . This indicates that pCUC3:CFP pattern requires localized auxin response upregulation to become discontinuous as does RFP-CUC2 ( S7A Fig ) . But interestingly , later NPA applications did not impact pCUC3:CFP discontinuous pattern ( Fig 9F and 9G ) , suggesting that although local auxin response is important to initially restrict CUC3 expression to the sinuses , this pattern is later maintained in an auxin and CUC2-independent manner . Because pKLUH:GFP is only expressed at early stages ( Fig 7B ) , we could only test the effects of early NPA application @24h . In contrast to pCUC3:CFP , pKLUH:GFP pattern 48h after induction was not modified by early NPA applications ( @24h; S7B Fig ) indicating that local auxin response is not required for KLUH expression to arise at the leaf margin . Altogether , this shows that CUC2-mediated induction of CUC3 and KLUH expression is required for proper dynamics of the auxin response at the leaf margin , and that in turn strong auxin response is necessary for the initial establishment of CUC3 expression pattern , but not for its later maintenance .
In plants , the repeated formation of new growth axes throughout their lives is the basis for their developmental plasticity , permitting adaptation to the environment . In the shoot and flowers , formation of such new growth axes relies on the interplay between local upregulation of auxin response , that determines the position of the new growth axis , and a boundary domain that contributes to its individualization [24 , 25 , 30 , 56 , 71 , 72] . Until now , patterning and growth during the formation of new axes appeared intimately intertwined as they rely on the same regulators , auxin and the CUC genes . Here , using tooth formation at the leaf margin as a model , we show that patterning and growth can be genetically separated , as the former strictly requires CUC2 while the latter can happen independently of CUC2 . Nevertheless , we provide evidence that CUC2 acts as a quantitative trigger for growth , as CUC2 levels direct growth rate through the quantitative activation of three downstream functional relays , CUC3 , KLUH and auxin response . Although these relays all contribute to growth , our functional analyses show that they act at different points in time and space . In particular , CUC3 acts locally while auxin has a most distant and very long-lasting role for sustained growth . We also reveal the involvement of a new actor , KLUH/CYP78A5 , during leaf margin morphogenesis , showing that it can partially substitute for CUC2 to promote tooth outgrowth . Based on our observations we propose a three-step mechanism for tooth morphogenesis ( Fig 10 ) . During the first phase , the leaf margin is patterned into a boundary domain marked by CUC2 , CUC3 and KLUH expressions and a tooth tip domain characterized by a high auxin response . Such a patterning is initiated by CUC2 that promotes CUC3 and KLUH expressions and leads to the formation of a strong auxin response at distance via modification of PIN1-mediated auxin transport [55 , 56] . In turn , auxin response contributes to refine CUC2 and CUC3 expression patterns . During the second phase for which CUC2 is not absolutely required , differential growth is initiated as a result of a strong auxin response that is promoted by CUC3 and inhibited by KLUH . Expression of CUC3 may also contribute to differential growth by locally repressing growth in the boundary domain as was suggested for CUC2 [65] . During the third phase , although KLUH is likely not expressed anymore , its effect remains and maintains auxin response and growth high . Overall , this model provides a scenario by which the boundary domains coordinate patterning and growth events during the formation of new growth axes Our results reveal the central role of CUC2 in the activation of an interconnected downstream network that promotes growth . Our quantifications show that variations in CUC2 levels are translated into different activity levels of the downstream actors , in particular auxin response that appears to control growth quantitatively . Another important feature of the network is that , although it initially enlarges downstream of CUC2 into three downstream actors , it seems to converge on auxin response . CUC2 promotes the formation of a strong auxin response at distance . In parallel , our observations suggest that CUC2 sets the expression of CUC3 and KLUH at the leaf margin . In addition , auxin response and also other factors may contribute to their expression dynamics . For instance , because CUC3 expression was shown to be regulated by mechanical stresses in the meristem [41] , one could imagine that similar mechanical stresses accompany tooth outgrowth and contribute to CUC3 local upregulation . Our pulsed CUC2 expression experiments show that this network can substitute for CUC2 to promote growth . However , during wild-type leaf development CUC2 expression is maintained during a longer duration and temporally overlaps with those of the 3 functional relays we identified . Furthermore , our results show strong interconnections between the actors of the network as both CUC3 and KLUH contribute to modulate auxin response and , that conversely , a strong auxin response is required for the proper dynamics of CUC3 and CUC2 ( our results and [55 , 56] ) . Such an interconnected network may allow buffering stochastic variations in the activity of individual actors of the network to provide a robust developmental response while providing enough flexibility to transduce stable variations of their activity into quantitative differences in growth , as we show here for CUC2 . We show that KLUH expression at the leaf margin is activated in response to CUC2 . In addition , proper tooth growth , either following ethanol-induced restoration of CUC2 expression or in wild-type leaf margins requires KLUH , suggesting that part of CUC2 effects on growth occurs via KLUH . Indeed , we show that tooth growth is partially restored in cuc2-3 and cuc2-1/cuc2-3 when a pCUC2:KLUH construct is introduced , demonstrating that KLUH is an important player in the CUC2-induced network promoting tooth growth . KLUH has been previously described as controlling final organ size by regulating cell proliferation timing in a non-cell-autonomous mechanism [60 , 61 , 70] . Interestingly , the unknown mobile signal hypothetically produced by KLUH was suggested to be able to control growth of adjacent organs in flowers and even different flowers in the inflorescence , indicating that it could diffuse on long distances ( on the cm range ) [60 , 62] . But KLUH’s involvement on leaf margin morphogenesis implies much shorter diffusion ranges for the mobile signal ( on the range of tooth size , which is about a few hundred μm ) . This suggests that the mobile signal could have different diffusion ranges depending on the organ or the developmental stage . Initial studies suggested that KLUH acts independently of classical phytohormones [60] . However , overexpression of PLASTOCRHON1 , a gene that belongs to the same class of CYP78A as KLUH ( although it is located in a different clade ) , has been recently shown to lead to higher auxin levels and response during maize leaf development [73] . Our work supports a role for KLUH in the modulation of auxin response , which appears to strictly correlate with the dynamic effects of KLUH on growth . However , whether all the KLUH effects on growth are mediated by changes in auxin response or whether KLUH acts on growth also independently of auxin signaling remains to be determined . Another standing question is the basis of the bimodal action of KLUH shown here , as KLUH represses early tooth growth while promoting it at later stages . One possibility is that cellular responses to KLUH may vary in time or space , thus providing the basis of the contrasted effects of KLUH . Alternatively , the latter effect could be an indirect consequence of the early effect . For instance , early modification of auxin transport or signaling could affect these processes during later stages . Interestingly , such a bimodal action also occurs at the whole plant scale , as leaf primordia arise faster but show reduced growth in the kluh mutant [60 , 74] , indicating that it is not specific to morphogenesis at the leaf margin . In conclusion , we show that plant boundaries coordinate patterning and growth to direct morphogenesis and we provide evidence that these two processes can be temporally and genetically separated , as the former requires CUC2 while the latter can occur independently of CUC2 . In the absence of CUC2 , differential growth is maintained via the activation of a regulatory network that can act as a functional relay . Like animal boundaries , plant boundaries control morphogenesis through multiple pathways , but they differ in their effect on morphogen distribution . While in animals , morphogens are produced by the boundary and therefore form a boundary–centered gradient [2 , 4] , plant boundaries locally modify the distribution of the morphogenetic regulator auxin and lead to its accumulation at distance . In addition , while animal morphogens have been proposed to control proliferation in a concentration-independent manner [75 , 76] our results indicate that auxin controls growth in a quantitative manner . However , we show that auxin , like the morphogen Decapentaplegic , is continuously required throughout development to promote growth [77–79] . Beside auxin , the putative mobile signal produced by KLUH in the boundary domain could form a morphogen-like gradient , similar to animal morphogens . Validation of such a hypothesis awaits the identification of the putative signal .
All genotypes are in the Columbia-0 ( Col-0 ) ecotype . The cuc2-1 mutant was originally isolated in the Landsberg erecta ecotype but was backcrossed 5 times in Col-0 [48] . The mir164a-4 [29] , cuc2-3 , cuc3-105 [23] , ago1-27 [80] , kluh-4 [60] , and jaw-D [81] mutants were previously described , as well as the pCUC3:CFP [26] , pCUC2: RFP [82] , pDR5:VENUS [38] pRPS5a:DII-VENUS , pRPS5a:mDII-VENUS [67] and CUC2g-m4 [29] transgenic lines . The cuc2-3 pCUC2:CUC2:VENUS line ( 33 ) used was described before [26] and two additional lines ( 44 and 45 ) were generated using the same approach in the cuc2-1 background . Heterozygous versions of the pCUC2:CUC2-VENUS reporter used in Fig 3 and S2 Fig were generated by crossing T3 homozygous plants bearing unique insertions of the reporters to homozygous cuc2-3 or cuc2-1 mutants . Seeds were soaked in water at 4°C for 48 hours prior to sowing . Plants were grown in soil in short-day conditions [1 h dawn ( 19°C , 65% hygrometry , 80 μmol . m-2 . s-1 light ) , 6 h day ( 21°C , 65% hygrometry , 120 μmol . m-2 . s-1 light ) , 1 h dusk ( 20°C , 65% hygrometry , 80 μmol . m-2 . s-1 light ) , 16 h dark ( 18°C , 65% hygrometry , no light ) ] . Plants from in S3C Fig were grown in vitro ( Arabidopsis medium Duchefa ) in long day conditions [16h light / 8h dark at 21°C] . The CUC2i line was generated in several steps . First , we generated a pCUC2:ALCR pAlcA:GUS driver construct using a 3 . 7kb CUC2 promoter sequence used in a previously described pCUC2:GUS reporter [29] and transformed it into wild-type Col-0 background . Two lines with a single pCUC2:ALCR pAlcA:GUS locus site based on the segregation of hygromycin resistant and sensitive plants in the T2 generation and showing the expected GUS staining in the meristem and leaves upon ethanol induction were selected . These two lines were crossed with the cuc2-1 mutant introgressed into Col-0 [48] and lines double homozygous for cuc2-1 and pCUC2:ALCR pAlcA:GUS were identified in the resulting F3 generation . Second , we generated N- and C- terminal fusions of CUC2 with the RFP using pH7RWG2 and pH7WGR2 vectors [83] . The resulting fusions were cloned between the pAlcA and 35S terminator and inserted into a pGreen0229 and transformed into the cuc2-1 mutant introgressed into Col-0 . Third , at least fifteen primary transformants with either CUC2 fusion were crossed with the two cuc2-1 pCUC2:ALCR pAlcA:GUS double homozygous lines . Leaf serration rescue was observed following ethanol induction of the resulting F1 lines . We noticed no difference in the leaf serration rescue by the two fusions . To further confirm this we constructed two lines homozygous for cuc2-1 , pCUC2:ALCR pAlcA:GUS and either pAlcA:RFP-CUC2 or pAlcA:CUC2-RFP . Both lines responded similarly to varying durations of ethanol treatment ( S2F and S2G Fig ) and showed no detectable fluorescence in the cytoplasm ( S2H Fig ) , suggesting that fusion with the RFP did not severely affect CUC2 function and was not cleaved off . We selected the RFP-CUC2 fusion for further analysis . Fourth , we generated 3 lines homozygous for cuc2-1 , pCUC2:ALCR pAlcA:GUS and pAlcA:RFP-CUC2 ( with 2 independent transformation events for pCUC2:ALCR pAlcA:GUS and pAlcA:RFP-CUC2 ) . We next characterized tooth formation in these lines following increasing levels of ethanol induction by watering the plants with 0 . 01% to 1% ethanol solutions . As we observed no difference between these lines we selected one of them , called CUC2i here , for detailed analyses . For the pMIR164A:RFP reporter , the endoplasmic reticulum targeted RFP cassette from the pCUC2:RFP reporter [82] was cloned behind a 2 . 1 kb long MIR164A promoter [29] within a pGreen0129 vector . A line segregating a single locus based on the hygromycin resistance segregation and showing an expression pattern similar to the previously pMIR164A:GUS line was selected . For the pKLUH-GFP reporter , a 4116 bp promoter sequence ending 10 bp after the initiation codon was amplified from Col-0 genomic DNA and cloned in front of a GFP-NOS terminator cassette contained in a pGreen0229 vector . This 4 . 1 kb 5’genomic region was shown to be sufficient to rescue a kluh-2 mutant when driving a KLUH–vYFP fusion [60] . Fifteen primary transformants were identified based on their resistance to basta and we selected one that showed the expected expression pattern at the organ basis and integrated the pKLUH-GFP construct at a single locus based on the segregation of basta resistant and sensitive plants in the T2 generation . For the pCUC2:KLUH construct , the full KLUH coding sequence was amplified from Col-0 seedling cDNA , cloned into the pGEM-T Easy System Vector and sequenced . The KLUH CDS was cloned as a NotI fragment into a pGreen0129-t35S-ProCUC2 vector [48] to generate the pCUC2:KLUH construct . The resulting construct was sequence-verified and transferred into Agrobacterium tumefaciens strain GV3101 , and cuc2-3 plants were transformed by floral dipping . Primary transformants were selected in vitro for their resistance to hygromycin . Two independent lines were selected based on a hygromycin resistance segregation indicating the integration of the transgene as a single locus . pCUC2:KLU#18 . 1 and #19 . 6 homozygous lines were used for further analyses . Transgenic lines were genotyped for the presence of both the cuc2-3 mutation and the pCUC2:KLUH transgene . The cuc2-1/cuc2-3 pCUC2:KLUH transheterozygotes were constructed by crossing the homozygous cuc2-3 pCUC2:KLUH with cuc2-1 . Plants were grown for 3 to 4 weeks prior to observations . All observations are done in leaves with a rank higher than 10 , which were imaged with their adaxial face closer to the objective . Typically 10–20 leaves coming from 3–6 different plants were imaged . Leaves were isolated from the meristem using surgical syringe needles and mounted between slide and coverslip . Mounting media has the following composition: Tris HCl 10mM pH = 8 , 5 , Triton 0 , 01% . Confocal imaging ( Figs 2E , 4B , 6A , 7B and 9A and S2H , S4A , S5A , S5B , S5D , S6A and S7A Figs ) was performed on a Leica SP5 inverted microscope ( Leica Microsystems , Wetzlar , Germany ) . Lenses are Leica 20x or 40x HCX PL APO CS . Acquisition parameters are presented in S1 Table and were kept constant throughout acquisitions so that intensity levels are comparable . The binocular imaging ( Figs 1A , 1C , 2B , 4A , 7A , 8D , 9D and 9F and S2A , S5E , S5G , S5M , S5N and S7B Figs ) was done using an Axio Zoom . V16 macroscope ( Carl Zeiss Microscopy , Jena , Germany , http://www . zeiss . com/ ) , RFP was imaged using a custom-made filter block ( excitation band pass filter 560/25; beam spliter 585 , emission band pass filter 615/24 , AHF , Tuebingen , Germany , https://www . ahf . de/ ) , CFP was imaged using the Zeiss 47 HE filter set ( excitation band pass filter 436/25; beam spliter 455 , emission band pass filter 480/40 ) , VENUS was imaged using the Zeiss 46 HE filter set ( excitation band pass filter 500/25; beam spliter 515 , emission band pass filter 535/30 ) , and fluorescence of the chlorophyll was imaged using the Zeiss 63 HE filter set ( excitation band pass filter 572/25; beam spliter 515 , emission band pass filter 535/30 ) . Figures were made using the ImageJ plugin FigureJ [84] . Most images are represented using the Fire LUT from ImageJ . In this case the Fire LUT was applied to the whole panel after it was assembled and a calibration bar is provided on the panel’s right end . White dashed lines always mark the leaf margin limit . We quantified RFP-CUC2 and CUC2-VENUS fluorescence in the CUC2 domain along the margin of young leaf primordia on confocal images obtained as described above . After tooth initiation that leads to discontinuous CUC2 domains [56] , we focused on the sinus distal to the first tooth , as CUC expression in this domain has been shown to drive the outgrowth of marginal structures [30 , 66] . The quantification of the RFP-CUC2 fluorescence was manually performed using ImageJ . The intensity of the 12 most intense nuclei was measured on the medial plane of each nucleus . The mean intensity of the background was substracted from the mean of the intensity of the nuclei . The pCUC2:CUC2-VENUS signal was quantified by a similar approach ( [82] for details ) . pCUC2:RFP , pCUC3:CFP and pDR5:VENUS signal quantifications presented in S3C and S3D Fig and S4C Fig were performed on Axio Zoom . V16 macroscope obtained images ( see above ) using the Qpixie macro previously described [82] . Most measures were manually performed using ImageJ on pictures made either with the binocular or the confocal microscope . Blade Length is defined as the length between the blade petiole junction and the leaf apex . Tooth With is the distance between two consecutive primary sinuses . Tooth Height is the tooth altitude , which is the distance starting at the tooth tip and meeting perpendicularly the tooth width segment . In order to evaluate the anisotropic tooth growth without taking global leaf growth into account , we normalize Tooth Height by Tooth Width and call this new parameter Tooth aspect ratio . The Sinus Angle is the local angle formed by the blade margin at the distal sinus site ( see S2D Fig ) . Phenotype quantifications and mean leaf contours in Figs 4E and 7E , S3E and S5D Figs were performed using the Morpholeaf application installed on the FreeD software [65 , 85] . Dissection index ( DI ) presented in S3E and S5D Fig is defined as DI=Leafperimeter24 . π . Leafarea . A perfect circle has DI = 1 . Ethanol inductions were performed on 3-week-olds plants using ethanol vapors for the time indicated in the figure legends . Plants were covered with plastic covers during induction . NPA way sprayed on plants until they were covered in solution . Spraying solution: NPA 10μM , DMSO 0 , 1% , Triton 0 . 01% . Once NPA applications were started , plants were sprayed every two days . In order to have identical total number of spray applications between the different treatments , plants were mock treated on days they did not receive NPA . Leaf margins were microdissected with the ZEISS PALM MicroBeam using the Fluar 5x/0 . 25 M27 objective . Leaves under 2mm long of rank >10 were placed on MMI membrane slides ( Prod . No . 50103 ) and microdissected samples were collected in ZEISS AdhesiveCaps . Cutting parameters are the following: speed 10–15% , energy: 67% , focus: 76% . Approximately 20 microdissected leaf margins were collected in each sample . Total RNAs were extracted using the Arcturus PicoPure RNA Isolation Kit following manufacturer’s instruction . RNA quality was controlled using the Agilent RNA 6000 Pico Kit . Total RNA were isolated using using RNAeasy Plant Mini Kit ( Qiagen ) following manufacturer’s instruction for plant tissue including on-column DNAse treatment . Reverse transcription was performed using RevertAid H Minus M-MuLV Reverse transcriptase ( Fermentas ) using 2μg of total RNA . Real time PCR analysis was performed on a Bio-Rad CFX connect machine using the SsoAdvance Universal SYBR Green Supermix following manufacturer’s instruction . PCR conditions are as follows: Conditions: 95°C 3min; ( 95°C 10s; 63°C 10s; 72°C 10s ) x45 cycles . Primers used for real time PCR analysis are available in S2 Table . Analysis was carried out using the ΔΔCt method [86] . Statistical analysis were performed on R [87] and graphical output was produced with the package ggplot2 .
|
During organogenesis , patterning , the definition of functional subdomains , has to be strictly coordinated with growth . How this is achieved is still an open question . In plants , boundary domains are established between neighboring outgrowing structures and play a role not only in the separation of these structures but also in their formation . To further understand how these boundary domains control morphogenesis , we used as a model system the formation of small teeth along the leaf margin of Arabidopsis , which is controlled by the CUP-SHAPED COTYLEDON2 ( CUC2 ) boundary gene . The CUC genes determine the boundary domains in the aerial part of the plants and in particular they have been shown to have a conserved role in regulating serration and leaflet formation across Angiosperms and thus are at the root of patterning in diverse leaf types . We manipulated the expression of this gene using an inducible gene expression that allowed restoration of CUC2 expression in its own domain at different developmental stages and for different durations , and followed the effects on patterning and growth . Thus , we showed that while CUC2 is required for patterning it is dispensable for sustained growth of the teeth , acting as a trigger for growth by the activation of several functional relays . We further showed that these findings are not specific to the inducible restoration of CUC2 function by analyzing multiple mutants .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"&",
"methods"
] |
[
"plant",
"anatomy",
"medicine",
"and",
"health",
"sciences",
"chemical",
"compounds",
"geometry",
"organic",
"compounds",
"aspect",
"ratio",
"hormones",
"developmental",
"biology",
"plant",
"science",
"mathematics",
"plant",
"hormones",
"plants",
"alcohols",
"morphogenesis",
"flowering",
"plants",
"digestive",
"system",
"dentition",
"chemistry",
"ethanol",
"head",
"leaves",
"biochemistry",
"plant",
"biochemistry",
"teeth",
"eukaryota",
"anatomy",
"organic",
"chemistry",
"physiology",
"jaw",
"biology",
"and",
"life",
"sciences",
"auxins",
"physical",
"sciences",
"digestive",
"physiology",
"organisms"
] |
2019
|
Dissecting the pathways coordinating patterning and growth by plant boundary domains
|
Mycobacterium ulcerans ( MU ) is responsible for disfiguring skin lesions and is endemic on the Bellarine peninsula of southeastern Australia . Antibiotics have been shown to be highly effective in sterilizing lesions and preventing disease recurrences when used alone or in combination with surgery . Our practice has evolved to using primarily oral medical therapy . From a prospective cohort of MU patients managed at Barwon Health , we describe those treated with primary medical therapy defined as treatment of a M . ulcerans lesion with antimicrobials either alone or in conjunction with limited surgical debridement . From 1/10/2010 through 31/12/11 , 43 patients were treated with exclusive medical therapy , of which 5 ( 12% ) also underwent limited surgical debridement . The median patient age was 50 . 2 years , and 86% had WHO category 1 and 91% ulcerative lesions . Rifampicin was combined with ciprofloxacin in 30 ( 70% ) and clarithromycin in 12 ( 28% ) patients . The median duration of antibiotic therapy was 56 days , with 7 ( 16% ) receiving less than 56 days . Medication side effects requiring cessation of one or more antibiotics occurred in 7 ( 16% ) patients . Forty-two ( 98% ) patients healed without recurrence within 12 months , and 1 patient ( 2% ) experienced a relapse 4 months after completion of 8 weeks of antimicrobial therapy . Our experience demonstrates the efficacy and safety of primary oral medical management of MU infection with oral rifampicin-based regimens . Further research is required to determine the optimal and minimum durations of antibiotic therapy , and the most effective antibiotic dosages and formulations for young children .
Buruli ulcer , also known as Bairnsdale ulcer , Daintree ulcer , or Mossman ulcer is a necrotising infection of skin and subcutaneous tissue caused by Mycobacterium ulcerans ( MU ) [1] . The major burden of disease is found in tropical climates , but cases have been reported from 33 countries worldwide [2] . M . ulcerans infection has become endemic on Victoria's Bellarine Peninsula in South-eastern Australia [1] Surgical management was traditionally the standard treatment for M . ulcerans disease [1] , [3] , however recurrence of infection ensued in 17–32% of patients [4] , [5] . Evidence of the effectiveness of antimicrobials used alone or combined with surgery [4]–[7] , has led to an evolution of our standard treatment practice over the last 15 years to now comprise combination antimicrobial therapy with limited surgical debridement when required [5] , [8] . Improved awareness in our region about MU lesions in the community has resulted in earlier referral of patients with smaller lesions to our department at Barwon Health [9] . We previously advocated for further studies using oral antibiotic regimens [7] . Here we describe results from an observational cohort of patients from South-eastern Australia managed with primary oral medical treatment for Mycobacterium ulcerans infection .
This is an observational cohort study , approved by Barwon Health's Human Research and Ethics Committee . All previously gathered human medical data were analysed anonymously . Data on all confirmed M . ulcerans cases managed at Barwon Health has been collected prospectively since January 1998 . From October 2010 , our standard treatment practice for initial M . ulcerans lesions has comprised combination antimicrobial therapy , with limited surgical debridement performed to aid wound healing . The data extracted from medical records included; patient demographics and co-morbid conditions , details of the MU lesion , regimen and duration of antimicrobial therapy , and details of surgical procedures . Cases treated between October 2010 and December 2011 with 12 months follow-up were included in this cohort . A M . ulcerans case was defined as the presence of a lesion clinically suggestive of M . ulcerans plus any of ( 1 ) a culture of M . ulcerans from the lesion , ( 2 ) a positive Polymerase Chain Reaction ( PCR ) from a swab or biopsy of the lesion , or ( 3 ) histopathology of an excised lesion showing a necrotic granulomatous ulcer with the presence of acid-fast bacilli ( AFB ) consistent with acute M . ulcerans infection . The anatomical location of a M . ulcerans lesion was described as distal if it was on the elbow or below , or on the knee or below [7] , [10] . Primary medical treatment was defined as treatment of a M . ulcerans lesion with either antimicrobials alone or antimicrobials in conjunction with limited surgical debridement . Drug dosages for adults included ciprofloxacin 500 mg twice daily , moxifloxacin 400 mg daily , rifampicin 10 mg/kg/day ( up to a maximum of 600 mg daily ) , and clarithromycin 500 mg twice daily . Paradoxical reactions were defined by the presence of one or both of the following features: a ) clinical: an initial improvement on antibiotic treatment in the clinical appearance of a M . ulcerans lesion followed by deterioration of the lesion or its surrounding tissues , or the appearance of a new lesion ( s ) , and b ) histopathology: examination of excised tissue from the clinical lesion showing evidence of an intense inflammatory reaction consistent with a paradoxical reaction [11] . Limited surgical debridement was defined as curettage of the lesion or a minor excision to remove excess granulation tissue and to debride ulcer margins , with or without the use of a split skin graft ( SSG ) . Limited surgical debridement was undertaken primarily to remove necrotic tissue from the MU lesion in order to promote healing by secondary intention . Patients who underwent extensive surgery ( defined as complete excision of the entire lesion including margins of non-necrotic tissue , with either direct closure or the use of a SSG or a vascularised skin and tissue flap for reconstruction or to cover the defect ) were excluded from the formal analysis . Criteria for primary medical therapy in our practice includes; patient willingness to take antimicrobials , and no contraindications to antimicrobial therapy ( for example; drug interactions , or severe liver disease ) . Criteria for complete surgical excision include factors such as; a lesion suitable for removal with direct wound closure , need for reconstruction to close a skin defect via flap or SSG , patient unable or unwilling to take antimicrobials , and patient or surgeon preference . Definitions of treatment success , treatment failure , disease recurrence , and immune suppression were as published previously [2] , [5] . A complication of medical therapy was defined as an adverse event attributed to an antibiotic that required cessation of that medication . Data was collected using Epi-Info 6 ( CDC , Atlanta ) and analysed using STATA 12 ( StataCorp , Texas , USA ) . Categorical values were compared using the Mantel-Haenszel test and median values were compared using the Mann-Whitney test .
From 1/10/2010 through 31/12/11 , there were 54 patients with MU infection managed at Barwon Health . From this cohort 11 patients ( 20% ) were excluded from further analysis; 3 patients underwent primary complete surgical excision alone , and 8 patients were prescribed antimicrobials but also underwent complete surgical excision . There were no significant differences in baseline characteristics of those excluded from those included in the analysis ( table 1 ) . Forty-three ( 80% ) patients were therefore included in this analysis . Baseline characteristics can be seen in table 1 . All patients were primarily managed as outpatients . The majority of patients were male ( 65% ) , and the median age was 50 . 2 years ( range 1 . 5–87 . 9 years ) . Lesions were ulcerative in 91% and WHO stage 1 in 86% of patients . All patients resided in areas of the Bellarine peninsula where MU is endemic; the majority residing in Point Lonsdale ( 36% ) and Barwon Heads ( 29% ) . Four patients ( 9% ) had known co-morbidities including diabetes ( 2 ) , immune suppression ( 1 ) , and malignancy ( 1 ) . No patients were known to be HIV-infected though active screening was not performed . Antimicrobial regimens were all rifampicin-based . Rifampicin was combined with; ciprofloxacin in 30 ( 70% ) patients , clarithromycin in 12 ( 28% ) patients , and moxifloxacin in 1 ( 2% ) patient . The median duration of therapy was 56 days ( range 28 to 91 days ) . Seven of 43 patients ( 16% ) received less than 56 days of therapy . Antibiotic-associated complications requiring cessation of one or more antibiotics occurred in 7 of 43 patients ( 16% ) . Two patients developed complications attributed to the combination of rifampicin and ciprofloxacin , 2 patients developed complications attributed to ciprofloxacin , 1 patient developed complications due to moxifloxacin , and 2 patients developed complications due to rifampicin . The most common complications were gastrointestinal upset in 4 patients , joint aches in 2 patients , hepatitis in 2 patients , tendonitis in 1 patient and thrombocytopenia in 1 patient . Of the 11 patients who underwent complete excision and were excluded from the study cohort , 2 of those 8 who took antibiotics developed complications . Nine of 43 patients ( 21% ) developed paradoxical reactions a median of 34 days after antibiotic therapy initiation ( IQR 20–92 days ) . Five of 43 ( 12% ) medically managed patients also underwent limited surgical debridement , and 3 of these procedures involved a SSG for coverage of the defect . Overall , 42 of 43 patients ( 98% ) were cured with primary medical therapy . Cosmetic outcomes were excellent in these medically managed MU cases ( Figures 1 & 2 ) . One patient failed primary medical therapy . This patient was a 17 month-old boy who presented with a nodular MU lesion on his arm of 1 cm diameter . He completed 56 days of rifampicin ( 10 mg/kg/day ) with clarithromycin ( 13 mg/kg/day in twice daily dosing ) both in liquid formulations with initial reduction in the size of the lesion . Four months after antimicrobials were completed the lesion enlarged and was debrided . Tissue from the debridement was culture positive for M . ulcerans . The patient was re-treated with rifampicin ( 10 mg/kg/day ) and clarithromycin ( 13 mg/kg/day in twice daily dosing ) both in liquid formulations , with subsequent ulceration of the nodule . A paradoxical reaction was diagnosed 4 weeks after re-treatment commenced based on the clinical deterioration of the lesion and was treated with prednisolone at a dose of 1 mg/kg for 4 weeks . The ulceration progressed and ultimately extended over an area of 12×5 cm and healed fully by secondary intention over a period of 9 months ( 16 months after initial treatment ) .
Our experience demonstrates the efficacy and safety of primary oral medical management of M . ulcerans in an Australian cohort . Further research is required to determine the optimal and minimum durations of antimicrobial therapy and the most effective dosages and formulations of antimicrobials for young children .
|
Mycobacterium ulcerans ( MU ) is responsible for disfiguring skin infections which are challenging to treat . The recommended treatment for MU has continued to evolve from surgery to remove all involved tissue , to the use of effective combination oral antibiotics with surgery as required . Our study describes the oral medical treatment utilised for consecutive cases of MU infection over a 15 month period at our institution , in Victoria , Australia . Managing patients primarily with oral antibiotics results in high cure rates and excellent cosmetic outcomes . The success with medical treatment reported in this study will aid those treating cases of MU infection , and will add to the growing body of knowledge about the relative roles of antibiotics and surgery for treating this infection .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine"
] |
2013
|
Mycobacterium ulcerans Disease: Experience with Primary Oral Medical Therapy in an Australian Cohort
|
Malaria and lymphatic filariasis ( LF ) continue to cause a considerable public health burden globally and are co-endemic in many regions of sub-Saharan Africa . These infections are transmitted by the same mosquito species which raises important questions about optimal vector control strategies in co-endemic regions , as well as the effect of the presence of each infection on endemicity of the other; there is currently little consensus on the latter . The need for comprehensive modelling studies to address such questions is therefore significant , yet very few have been undertaken to date despite the recognised explanatory power of reliable dynamic mathematical models . Here , we develop a malaria-LF co-infection modelling framework that accounts for two key interactions between these infections , namely the increase in vector mortality as LF mosquito prevalence increases and the antagonistic Th1/Th2 immune response that occurs in co-infected hosts . We consider the crucial interplay between these interactions on the resulting endemic prevalence when introducing each infection in regions where the other is already endemic ( e . g . due to regional environmental change ) , and the associated timescale for such changes , as well as effects on the basic reproduction number R0 of each disease . We also highlight potential perverse effects of vector controls on human infection prevalence in co-endemic regions , noting that understanding such effects is critical in designing optimal integrated control programmes . Hence , as well as highlighting where better data are required to more reliably address such questions , we provide an important framework that will form the basis of future scenario analysis tools used to plan and inform policy decisions on intervention measures in different transmission settings .
Malaria and lymphatic filariasis ( LF ) cause the largest public health burden of all vector-borne diseases worldwide [1] with around 350–500 million clinical episodes and 1 million deaths every year caused by malaria [2] and more than 120 million people globally infected with LF . The diseases are co-endemic in many regions in sub-Saharan Africa and , importantly , are transmitted by the same vector species , the Anopheles spp . mosquito [3] . The infections can co-exist in both vectors [4] , [5] and hosts [6] . Interactions between malaria and LF parasites are thought to have an effect on the transmission of both infections , in particular through changes in vector mortality as a result of either single infection or co-infection [7] , [8] . Interactions in the host are likely to affect susceptibility and disease severity [9]–[13] , and are determined by the effect the parasites have on immunological cytokines . Cytokines are proteins secreted by the immune system carrying signals to cells , mediating and regulating immunity , inflammation , and the development of blood cells . They are commonly divided into two categories – type 1 and type 2 . Malaria is associated with a Th1 response , with increases in the production of type 1 cytokines , including IFN- and TNF- [14] , which stimulate immunity and can result in extreme inflammatory responses . Lymphatic filariasis induces both Th1 and Th2 responses [15] , [16] . Initially the response is Th1 biased , causing inflammation and protecting against incoming larvae . Subsequently , Th2 responses are induced , in particular cytokines IL-4 , IL-10 and TGF- among others [14] , [17] , [18] which induce strong antibody responses and act to limit and contain infection . As infection progresses , Th2 levels increase , decreasing the Th1 response . Understanding how co-infection affects the dynamics and control of important infectious diseases has become increasingly significant as evidence of meaningful within-host interactions between pathogens becomes better established [19]–[23] . Changes in the course of infection of co-infected individuals have been observed but , in general , the mechanisms of how transmission is altered are poorly understood . For example , HIV-infected individuals co-infected with Hepatitis C have been reported to experience a more rapid clinical progression compared to single infected individuals [24] , [25] . Helminth infections are thought to exacerbate malaria symptoms by causing blood loss ( and thus anaemia ) and inhibiting the ability of the host to mount a Th1-type immune response [21] . These examples highlight two key ways in which co-infections can affect transmission and disease: each infection alters the ability of the immune system to adequately mount an immune response to the other infection , or one infection has a related symptom that exacerbates the associated symptoms and effects of the other infection . Currently , there is little consensus on the effects of malaria and helminth co-infection on human hosts . There is evidence that co-infection can reduce [26] , [27] or increase [14] , [27] , [28] malaria severity . A meta-analysis of 54 experiments conducted on laboratory mice investigating the effects of helminth infection on microparasite density suggested that the effect of interactions is dependent on the species pair [11] , although none of the studies considered Wuchereria bancrofti ( LF ) and Plasmodium ( malaria ) co-infection . However , it is thought that infection with W . bancrofti increases mosquito susceptibility to Plasmodium infection , since migration of microfilariae disrupts the midgut , allowing Plasmodium easier access through the midgut to the salivary glands [3] , [5] , [29] , [30] . On the other hand , mosquitoes carrying worm parasites have been found to reduce Plasmodium infectivity , with such vectors possessing a lower infection intensity compared to uninfected mosquitoes [31] . This suggests that reducing worm burden in a population could increase mosquito susceptibility to malaria infection . These mechanisms clearly need to be investigated further in natural worm-Plasmodium-Anopheles systems before inclusion in co-infection transmission models and we therefore do not consider this further here . Similarly , there is also evidence suggesting that Plasmodium-infected mosquitoes have higher numbers of W . bancrofti parasites [3] , [29] , [32] . A study in Papua New Guinea found that co-infected vectors are more common than we would expect from the prevalence of single infections [5] , suggesting that infected vectors are more susceptible to other diseases . Co-infection has also been reported to affect the size , development and density of larvae and oocysts in the vector [32] . However , any advantages to disease transmission due to increased susceptibility may be lost by the reduction in survivorship caused by co-infection [5] . High levels of L3 larvae in co-infected vectors increases mortality , reducing the probability that vectors survive long enough to become infectious and transmit these diseases [3] , [29] , suggesting that this may be an important regulatory mechanism underlying co-transmission of malaria and filariasis . The basic reproduction number , , is a key concept in infectious disease epidemiology , which for a microparasite infection is defined as the average number of secondary cases generated per primary case in an entirely susceptible population . For a macroparasitic infection , may be analogously defined as the average number of female offspring per adult female worm surviving to reproduction in the absence of density-dependence [33] . Consideration of this metric as a key measure of the transmission potential of either infection thus raises the question of how the reproductive potential of malaria or LF is modified in the presence of the other . In particular , a better understanding of this effect will be important for assessing the conditions under which either disease will successfully invade ( and co-existence of both diseases may occur ) into regions where the other is endemic . Given that we adopt a deterministic compartmental co-infection model here , the criteria for either disease to successfully invade reduces to the standard criterion ( while this represents a necessary , but not sufficient , condition in stochastic approaches ) . Individually , malaria and LF models have been studied extensively [34]–[41] , and these have frequently included analysis of key determinants of R0 , but this study represents the first attempt to develop a combined LF and malaria transmission model . The modelling framework developed in this study is based on the hypothetical macroparasite-microparasite co-infection modelling framework developed in [42] . Explicitly modelling the interactions between malaria and LF is important for understanding how co-infection may impact the prevalence , reproduction number and elimination thresholds of both diseases , which clearly is also of import to quantifying the efficacy of integrated control approaches . It has been suggested that targeting only LF may actually increase malaria incidence – LF infected mosquitoes have a higher mortality than uninfected mosquitoes due to the costs of larval burden [7] , so eliminating LF increases vector lifespan , enabling greater malarial parasite transmission . Here , we develop a model of malaria and LF transmission to investigate ( a ) how these diseases , and their interactions , may be concurrently included in a consistent mathematical framework , ( b ) how effects due to parasites within hosts and vectors affect the baseline transmission dynamics of each disease in the presence of the other , and ( c ) how the basic reproduction number of each disease is affected by prevalence of the other .
A generic framework is developed in [42] for modelling microparasite-macroparasite co-infections , using a simple SI ( susceptible-infected ) model for microparasites in humans and a macroparasite model tracking the number of worms in hosts susceptible to , and infected with , the microparasite . Worms subsequently produce eggs that are released into the environment and may be passed to humans where they become adult macroparasites . We use the basic ideas behind this approach and apply these to the specific case of co-infection with malaria and LF . This involves several new additions including ( 1 ) explicitly modelling the vector population , with the external macroparasite infective stage represented as larvae in the vector , ( 2 ) dividing the human and vector populations into different compartments depending on malarial status and modelling the macroparasite population in each of these compartments , ( 3 ) including two parasite stages in the host , namely adult LF worms and microfilariae , ( 4 ) explicitly modelling the development of larvae in the vector , and ( 5 ) capturing the effect of co-infection on host infection dynamics . The malaria component ( Figure 1 ) of the full co-infection framework takes the form of an SEIRS model for human hosts ( where we track the number of susceptible , exposed but not infectious , infectious , and recovered hosts , respectively denoted , , and ) and an SEI model for vectors ( with the number of vectors susceptible , exposed but not infectious , and infectious respectively denoted , and ) , which are assumed not to recover from infection should it arise [34] , [43] . Humans are assumed to be immune for a short duration after recovering from infection , before re-entering the susceptible population . Once an infectious mosquito bites a susceptible human and Plasmodium parasites enter the blood , the host is typically infected for several weeks before becoming infectious . If a susceptible vector bites an infectious human , it may become infected . The vector then becomes infectious at a rate dependent on the duration of the parasite sporogonic cycle ( which is temperature-dependent , but typically takes around 12 days at 25°C [34] ) and if it successfully bites a susceptible human , infection is passed on and the cycle continues . The number of humans progressing from susceptible to exposed is determined by the force of infection from infectious vectors to susceptible hosts , and depends on the biting rate a ( defined as the number of bites taken per vector per day ) and the transmission probability of infectious vectors successfully transferring infection to susceptible humans . The rate at which humans move from exposed to infectious is determined by the duration of latency , and movement from infectious to recovered by the duration of infectiousness . Humans become susceptible again according to the rate at which immunity wanes . We also include host and vector births and deaths . Progression of vectors from susceptible to exposed is dependent on the force of infection from infectious hosts , while the progression from exposed to infectious is determined by the duration of the sporogonic cycle . The LF component ( Figure 2 ) of the full co-infection model is a simplified version of the model in [39] and extended in [40] , where we assume age-independent LF transmission . We include an additional compartment in the model to represent the immature stages of larval development within vectors . Worms in the host produce microfilariae ( mf ) , which may be ingested by biting mosquitoes and develop first into immature larvae , and then L3 larvae , before entering another human host at the next blood meal . These L3 larvae subsequently develop into worms in humans and the process continues . In this basic model , the number of worms , mf , immature larvae and L3 larvae are respectively denoted , , and . A simplified schematic ( omitting all birth and deaths rates and the labelling of rates in terms of model parameters ) of the full co-infection model is shown in Figure 3 . The basic LF model is modified to account for the number of total worms and mf carried by hosts in each malaria compartment , and we thus track the total number of worms and mf in hosts who are susceptible , exposed ( but not infectious ) , infectious and temporarily immune ( recovered ) from malaria . We denote these state variables , , and respectively for the worm burden and , , and for the number of mf in humans . The number of new worms entering each worm compartment at each time step is determined by the biting rate of mosquitoes , the proportion of L3 larvae that leave mosquitoes and successfully enter the host , the total number of L3 larvae across all vectors , and the lower probability of worm development at higher total worm burden due to greater individual-level immunity . The total number of worms in each compartment is calculated by dividing the total worm burden up according to the proportion of hosts in each malaria compartment . We also account for the mortality of worms and humans , along with the movement of worms between compartments due to the changing malaria status of their hosts . The microfilaria equations are parameterised in terms of the number of mf produced per worm ( per unit time per 20 L of blood ) and account for mf losses due to natural mf mortality , death of the host , and movement to new compartments due to changes in host malaria status . Similarly , we track the number of underdeveloped larvae and fully-developed L3 larvae in vectors who are susceptible , exposed or infectious with respect to malaria . We denote these , and for immature larvae and , and for L3 larvae respectively , where the overbar notation in the subscript emphasises reference to the malaria status of vectors , rather than humans . The number of larvae entering each developmental stage at each time step is determined by the biting rate , the probability that an mf enters a vector and successfully develops , and a density-dependent uptake function that governs the maximum number of mf that can be taken per mosquito . The rate of progression from underdeveloped larvae to developed L3 larvae is simply tracked by assuming a constant rate of progression . We include terms to account for the death of L3 larvae and immature larvae upon mosquito death , as well as larval movement between compartments as vectors change malaria status . Table 1 summarises state variables in the full model . Interaction between the LF and malaria models occurs through ( a ) changes in mortality of mosquitoes infected with LF ( assumed to be a linear hazard of L3 larval density ) , and ( b ) interaction between microparasites and macroparasites within hosts through the Th1/Th2 immune response , which affects the course of each infection ( discussed further shortly ) . Consider first the LF component of the model ( see Figure 2 ) . The number of worms and microfilariae in hosts are governed by the differential equations: ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) ( 7 ) ( 8 ) In addition , the function represents an immunity term for worms , where γ is a variable ( which can be thought of as the ‘experience of infection’ ) that models the change in immunity over time as a function of worm burden per host and the current immunity level , and satsifies the equation . LF prevalence in hosts can be calculated using ( where k0 = 0 . 0029 and k1 = 0 . 0236 ) [33] . For the number of immature and L3 larvae in vectors: ( 9 ) ( 10 ) ( 11 ) ( 12 ) ( 13 ) ( 14 ) where is the mf uptake function ( with baseline values larvae per mf per 20 µL of human blood and L3 larvae ) . For the malaria components of the model , the number of hosts and vectors in each compartment is given by: ( 15 ) ( 16 ) ( 17 ) ( 18 ) ( 19 ) ( 20 ) ( 21 ) where the effects of co-infection enter the malaria model through an increase in vector mortlaity due to the presence of larvae and the antagonistic Th1/Th2 response . Malaria prevalence in hosts is given by HI/H . A list of model parameters is given in Table 2 .
With the parameters in Table 2 , Figure 5 contrasts the dynamics of malaria and LF with and without the presence of the other infection . We find that malaria prevalence is lower in humans when LF is present ( Figure 5 ( a ) ) . Host immunity is biased towards a Th1 response in the absence of LF , corresponding to a faster recovery from malaria and hence a decreased duration of infectiousness; however , while the presence of LF induces a greater Th2-skewed host immune response and would cause a slower Plasmodium clearance rate and hence increase in malaria prevalence if acting in isolation , this effect is less significant than larval-induced mortality decreasing vector life expectancy and hence the time for onwards malaria transmission . For the ( realistic ) parameter regime considered here , the net effect of LF presence is therefore to reduce malaria prevalence ( although we note the importance of further experimental and field studies to address uncertainties in parameterisations of these two interactions to assess the generality and robustness of this result ) . The higher vector death rate when LF is present ( Figure 5b ) due to larval-induced mortality also results in a decrease in vector malaria prevalence and this again dominates over indirect effects due to changing host immune response . Host LF prevalence is similarly reduced when malaria is present , since the presence of the microparasite elicits a more Th1-skewed response , resulting in higher filarial worm mortality in malaria-infected hosts and thus reduced LF prevalence . When malaria is absent , host immune response is Th2-biased and acts to sustain LF infection , rather than eliminate it , meaning that LF worms live for longer . In mosquitoes , LF prevalence is lower when malaria is present , since there are fewer worms in hosts and thus fewer mf per blood meal that may eventually develop into infective larvae . The introduction of either infection into regions where the other is endemic alters the prevalence of the original infection . If malaria is introduced into an LF-endemic region , the prevalence of LF decreases marginally in both hosts and vectors ( Figure 6 ) , since worm mortality is higher in malaria-infected individuals , reducing overall worm burden . When LF is introduced into regions with endemic malaria , host and vector malaria prevalence decreases due to increased vector death rate resulting from LF larval-induced mortality , meaning that vectors have less time to complete sporogony and transmit malaria parasites . We also note the speed at which both diseases reach a new equilibrium after introduction of the other infection . When malaria is introduced into LF-endemic regions , malaria prevalence quickly increases to its equilibrium level , while LF takes around 12 years to reach its new endemic state ( Figure 6 ) . Similarly , when LF is introduced into a malaria-endemic region , it subsequently takes around 30 years to reach its equilibrium , with malaria prevalence changing as soon as LF begins to increase significantly ) . These temporal differences are due to the significantly shorter lifecycle of malaria transmission compared to LF – the time taken for individuals to become infected with malaria and return to the susceptible class is less than a year , while LF worms can survive in hosts for around 10 years and mf can live for around 300 days . In addition , malaria transmission is more ‘efficient’ than LF transmission – only one bite is required from malaria-infected vectors to infect a human , whereas , on average , thousands of bites are needed from LF-infected vectors to produce one filarial worm . The dynamics of LF are therefore typically far slower and infections take longer to ‘take-off’ . Furthermore , LF takes even longer to establish when malaria is present , since the immune response remains Th1-skewed at low LF prevalence , resulting in an increased worm mortality rate . These results are dependent on model parameters . We briefly explore the sensitivity of these results with respect to different assumptions regarding the worm lifespan in infectious hosts ( Figure 7 ) . Here , it is clear that when malaria is introduced into LF endemic regions , the magnitude of the reduction in LF prevalence depends on the extent to which malaria infection reduces worm lifespan in infectious hosts . Various approaches may be used to derive , but arguably the most general is that of the next-generation approach introduced by [52] , which holds , in principle , independent of model structure . Consider first the basic reproduction number of malaria in the presence of endemic LF , which we denote . Following the formalism of [53] , we consider equations ( 16 ) , ( 17 ) , ( 20 ) and ( 21 ) describing the dynamics of the infected compartments in the malaria component of the model , from which it is readily shown in Text S1 that calculating the dominant eigenvalue of the next-generation matrix gives ( 25 ) Substituting for , evaluating at the malaria-free equilibrium ( where , and ) , and defining as the number of new infectious individuals produced by a single infectious individual in that class ( which leads to the required being the square of ( 25 ) ; see [53] ) gives ( 26 ) which reduces to the standard expression for malaria in the absence of LF ( i . e . when ) and where is the number of vectors per host . Thus , the reproductive potential of malaria is reduced by the presence of L3 larvae in infected and infectious mosquitoes ( through decreased vector life expectancy ) , but increased due to the longer duration of host infectiousness ( resulting from down-regulation of the Th1 response in the presence of LF ) as mean worm burden in humans increases; further experimental data leading to more reliable estimation of μv' , ωhmax , ωhmin , and ε will enable more robust quantitative conclusions to be drawn about the magnitude of these competing interactions on ( Figure S1 ) . To investigate how the R0 of malaria varies at different background levels of LF , we consider a range of biting rates ( the largest of which are the most realistic ) and vary the value of μm ( since this affects the equilibrium LF state , but without changing R0M , as well as representing a parameter that may be influenced by LF controls ) ( Figure 8 ) . The arrow on each biting rate curve denotes increasing μm ( where larger values imply shorter mf life expectancy and thus lower LF prevalence ) . Figure 8 confirms the analytical result from ( 13 ) that the R0 of malaria decreases as LF endemicity increases , and this effect becomes increasingly pronounced as the biting rate increases ( with the steepest curves at the highest biting rates ) . The drop in R0M with increasing LF is consistent with Figure 5 , which indicates that the introduction of LF causes malaria prevalence to decrease in humans and mosquitoes . In seeking a more complete understanding of the response of malaria transmission to LF presence across the full range of parameter space , however , it is nonetheless important to recognise that R0M arises as the product of human and mosquito components of the parasite lifecycle . In some parameter regimes ( not shown ) , introducing malaria into LF-endemic regions can increase the duration of human infectiousness ( and hence human prevalence ) due to a Th2-skewed host response , yet vector prevalence remains lower than if LF is not present due to the absence of larval-induced mortality . In this case , since the proportional drop in mosquito malaria prevalence is greater than the human component , the net effect is an overall drop in malaria prevalence ( and hence R0 ) , which is still consistent with ( 13 ) , Figure 8 , and standard theory on the monotonic relationship between R0 and endemic prevalence [33] . This subtle interplay between human and vector prevalence to produce the observed response of R0M to LF presence is explored further in Figure S1 in Text S2 as a function of larval-induced vector mortality and human recovery from malaria . An identical approach may be followed to calculate the basic reproduction number of LF in the presence of endemic malaria , which we denote , by considering equations ( 1 ) – ( 14 ) describing the LF component of the model . The need to track the number of worms and mf in four possible host states of malaria , together with the number of immature and L3 larvae within mosquitoes in three malaria states , results in a high-dimensional next-generation matrix ( see Text S3 ) . Evaluating this matrix at the LF-free equilibrium and calculating the dominant eigenvalue yields a closed-form solution for that is too unwieldy to reproduce here , but that we note reduces , in the absence of malaria , to ( 27 ) a standard expression for the basic reproduction number of LF . In our model , the only effect of malaria presence on LF transmission is through the increased worm mortality rate in malaria infected hosts . However , since the reproduction number describes the number of new infections resulting from one primary infection in a totally susceptible population ( and is thus a meaningful transmission metric for only a short duration before saturation effects occur ) , it is clear that the underlying malaria prevalence will not significantly affect because new LF infections develop over a considerably shorter time span than the time taken for LF worms to die ( even in malaria infected hosts where filarial worms experience a higher death rate relative to hosts susceptible to malaria ) . If malaria presence impacted microfilaria production or larval dynamics , we would expect to be more strongly dependent on malaria prevalence , but in the absence of these effects , we obtain a very weak dependence ( which does , nonetheless , marginally reduce LF prevalence in both humans and vectors and is thus consistent with Figure 5 ) . Malaria does , however , alter LF transmission dynamics when both infections are endemic , as shown in Figures 5 and 6 .
The model presented here , for malaria and LF co-infection within human and vector populations , represents the necessary groundwork towards a scenario analysis tool that could be used for policy planning . Three key interactions between the two parasites are introduced very simply into the basic interaction-free model through ( 1 ) increased mortality of vectors that are infected by either or both parasites , ( 2 ) increased mortality of LF worms in malaria co-infected hosts , and ( 3 ) increased recovery period from malaria in LF co-infected hosts ( with the latter through modification of the human immune response towards one parasite in the presence of the other ) . These interactions , while reasoned judiciously here , are not expected to be comprehensive and are used here , along with plausible parameter values , to illustrate how these interaction terms ( mortality rates and rates of recovery from infection ) might appear in the expressions for R0 and subsequently impact the prevalence of each infection in the presence of the other . Nonetheless , there are other potentially important interactions between these infections . For example , as discussed earlier , there is the possibility of infection with one parasite affecting vectors' susceptibility to the other infection . Data on the effects of LF-malaria co-infections is almost non-existent; however , we can look at other helminth-malaria co-infections . In hosts , it has been reported that schistosomiasis can both increase and decrease the frequency of malaria attacks in co-infected individuals [54] , [55] , and that children with low intensities ( but not higher intensities ) of schistosomiasis have significantly lower P . falciparum densities than worm-free individuals [55] . Studies investigating how co-infection affects the course of each infection , as well as studies exploring the immune responses to co-infection , are needed to better inform the interactions assumed in our model . Overall , such scenario modeling is essential in an era of large scale Mass Drug Adminstration ( MDA ) and control programmes of tropical diseases , so that possible perverse effects are thought through in advance . The results here show that perverse outcomes might be more complicated in a co-infection framework – even though the presence of one parasite appears to decrease the R0 of the other in both cases , the R0 calculated here is the overall value for the entire lifecycle and therefore takes into account human and vector components . The introduction of one parasite in the presence of the other may reduce the overall prevalence , but actually increase the prevalence in the humans while decreasing it in vectors in certain parameter regimes . Figure 6 shows that introducing LF reduces the prevalence of malaria , suggesting that , if LF was eliminated from a co-endemic region ( using MDA , for example ) , this could actually result in an increase in malaria prevalence . These effects obviously need to be better understood so that inadvertant rises in human prevalence are avoided and improving our parameterisation of key interactions between malaria and LF epidemiology , such as those considered here , through new experimental studies is vital . In vectors , for example , better data on how susceptibility to infection and mortality are altered by co-infection are required . In humans , we need a better understanding of how the interplay between the Th1 and Th2 responses affect the ability of the host to mount an immune response to each infection , specifically , the impact of LF infection on the duration of malaria and whether or not malaria infection reduces the number of LF worms . We also need to find ways to translate the immune response findings from laboratory studies into meaningful assertions about how co-infections alter key epidemiological parameters in transmission models . In addition to these laboratory studies , parasite prevalence and intensity in communities with both infections should be monitored at high frequency prior to and during control programmes for high quality time-series to which epidemiological models such as ours can be fitted . The factors influencing breakpoints – prevalence levels below which parasites become extinct – are the relative sizes of negative and positive density-dependent effects and the overall value of the reproduction number . In our model , the included density-dependencies are negative for the sake of simplicity , and breakpoints will not occur , but we plan to examine this important phenomenon in future work . Certainly , the changes induced in the LF reproduction number by the presence of malaria will alter the size of the breakpoint , though we have shown that the effect of malaria on the R0 of LF will be small for the interactions we include . An important next stage of our work is also to fit the parameters to data from field sites in which interventions have occurred and both infections have been monitored . Large increases in the malaria parasite rate in humans , following treatment for LF , would strongly determine the interaction parameters occuring in R0M , for example , as would the impact upon LF of the treatment for malaria . With commitment from international agencies and pharmaceutical companies to treat infectious tropical diseases , such data should become available soon and a parameterised modelling tool would then become invaluable .
|
Malaria and lymphatic filariasis ( LF ) are thought to be co-endemic in many regions of Africa . Currently , most interventions targeted at these infections do not consider the impacts of co-infection . However , there have been increasing calls to adopt integrated control programmes that can achieve synergistic effects . Malaria and LF are both vector-borne diseases transmitted by Anopheles spp . mosquitoes , suggesting that well-designed vector control strategies have the potential to affect the transmission of both infections . In this study , we develop a modelling framework incorporating the specifics of malaria-LF co-infection to investigate how the transmission of each infection is altered for a range of possible interaction scenarios . We find that a control strategy that reduces LF transmission ( via mass drug administration , for example ) could potentially increase malaria prevalence . This work illustrates the potential perverse effects of targeting just one infection and emphasises the need to take into account co-endemic diseases when designing control programmes . The developed modelling framework can provide the basis for exploring the mix of options for joint control of these infections . We also highlight the need for better data on how co-infection impacts hosts and vectors in order for future predictions on both co-transmission dynamics and control to become more reliable .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"mathematics",
"theoretical",
"biology",
"ecology",
"epidemiology",
"applied",
"mathematics",
"population",
"biology",
"biology"
] |
2013
|
Modelling Co-Infection with Malaria and Lymphatic Filariasis
|
Population suppression through mass-release of Aedes aegypti males carrying dominant-lethal transgenes has been demonstrated in the field . Where population dynamics show negative density-dependence , suppression can be enhanced if lethality occurs after the density-dependent ( i . e . larval ) stage . Existing molecular tools have limited current examples of such Genetic Pest Management ( GPM ) systems to achieving this through engineering ‘cell-autonomous effectors’ i . e . where the expressed deleterious protein is restricted to the cells in which it is expressed–usually under the control of the regulatory elements ( e . g . promoter regions ) used to build the system . This limits the flexibility of these technologies as regulatory regions with useful spatial , temporal or sex-specific expression patterns may only be employed if the cells they direct expression in are simultaneously sensitive to existing effectors , and also precludes the targeting of extracellular regions such as cell-surface receptors . Expanding the toolset to ‘non-cell autonomous’ effectors would significantly reduce these limitations . We sought to engineer female-specific , late-acting lethality through employing the Ae . aegypti VitellogeninA1 promoter to drive blood-meal-inducible , fat-body specific expression of tTAV . Initial attempts using pro-apoptotic effectors gave no evident phenotype , potentially due to the lower sensitivity of terminally-differentiated fat-body cells to programmed-death signals . Subsequently , we dissociated the temporal and spatial expression of this system by engineering a novel synthetic effector ( Scorpion neurotoxin–TetO-gp67 . AaHIT ) designed to be secreted out of the tissue in which it was expressed ( fat-body ) and then affect cells elsewhere ( neuro-muscular junctions ) . This resulted in a striking , temporary-paralysis phenotype after blood-feeding . These results are significant in demonstrating for the first time an engineered ‘action at a distance’ phenotype in a non-model pest insect . The potential to dissociate temporal and spatial expression patterns of useful endogenous regulatory elements will extend to a variety of other pest insects and effectors .
Advances in molecular tools have allowed the development of a range of novel Genetic Pest Management ( GPM ) strategies [1 , 2] . One such GPM strategy utilises the mass-release of males from the target species which have been genetically modified to express a repressible , dominant-lethal gene . In the wild , these released males mate wild females; in their offspring this lethality is not repressed so they die before reproducing . With successive releases , a target population can thus be reduced , potentially to the point of eradication [3–5] . Modelling has shown that , if the population dynamics of the target pest are regulated by negative density-dependence–reduced population growth at high population densities e . g . through competition for resources–suppression can be significantly enhanced if the engineered lethality occurs after the density-dependent phase , rather than before it as with radiation-sterilisation [4 , 6] . System development for mosquitoes such as Aedes aegypti ( the primary vector for dengue , Zika , chikungunya and yellow fever viruses ) has therefore aimed to induce late-acting ( e . g . pupae/adult ) lethality as most density-dependent effects are evident at the larval stage . These ‘self-limiting’ GPM strategies have been extended to a wide variety of agricultural and human health pest insects , and have been successfully demonstrated in the field in multiple geographic locations [7–12] . A variety of repressible lethal systems have been constructed in insects using the “tet-off” bipartite expression system [7 , 13–16] , allowing the engineered phenotype to be repressed prior to field release by providing transgenic animals with tetracycline or a suitable analogue ( Fig 1A and 1B ) [17] . Using this system , expression of an effector gene ( tetO-effector ) is controlled by the action of the tetracycline-repressible Transactivator ( tTAV ) . In current designs , the spatial and temporal expression pattern of effector expression mirrors that of the regulatory elements used to drive tTAV . Whilst building systems in this manner has allowed novel control phenotypes e . g . sex-specific lethality/sterility [5 , 16] , it restricts the function of the system ( i . e . the action of a toxic protein ) to the cells in which tTAV is expressed . This current paradigm of using ‘cell-autonomous effectors’ is a significant limitation on developing more complex and flexible GPM technologies as it necessitates that the cell-type expressing the effector must simultaneously be sensitive to its effects , precluding the use of either a useful effector , or regulatory element , where this overlap does not occur . The potential to dissociate the temporal and spatial expression patterns of an effector ( i . e . a ‘non cell-autonomous’ effector ) would allow the use of a far wider panel of endogenous regulatory components for building GPM systems but , to date , has been limited by the available molecular tools . We hypothesised that a route towards achieving non cell-autonomous effectors would be to use the secretory pathway of those cells engineered to show transcriptional activity as a mechanism for allowing effector proteins to dissociate away from these areas and affect distant tissues . Here , we demonstrate the successful use of such a system by engineering a novel synthetic effector designed to be secreted out of the adult fat-body following a blood-meal ( a female-specific behaviour ) and affect the functioning of motor neurons–leading to paralysis .
Aedes aegypti VitellogeninA1 ( VgA1 ) is expressed primarily in the female fat body , following a blood meal [18–22] . Replicating this adult female-specific expression profile to drive a synthetic transactivator ( tTAV ) was the first integral step towards developing bloodmeal-inducible lethality using the bi-partite tet-off expression system ( Fig 1A and 1B ) . We generated a transgenic line carrying tTAV driven by a VgA1-derived promoter sequence ( VgA1-tTAV ) . Consistent with previous studies on VgA1 , qPCR of a transgenic line carrying the VgA1-tTAV construct showed significant blood-meal-inducible upregulation of tTAV ( mean = 182 x male pupae expression: P = 0 . 03 , t = -5 . 56 , df = 2 ) with very low basal expression in adult males and non-blood-fed females ( S1 Fig ) . This induction occurred rapidly ca 24 h post blood-meal ( pbm ) . Crossing this line to an existing tetO-DsRed2 reporter line and analysing transhemizygous progeny confirmed DsRed expression in the female fat body with some low level expression in males ( S2 Fig ) , consistent with the tTAV qPCR data and previous transgenic characterisation of the VgA1 promoter fragment [18] . Initial attempts to pair this expression profile with a reduced-fitness phenotype–e . g . lethality–were unsuccessful . Crosses of the VgA1-tTAV line to tetO-Michelob_X and tetO-reaperKR lines gave no discernible phenotype in transhemizygous females pbm , despite the previously established upregulation of the tTAV transactivator during this period and confirmed overexpression of the Michelob_x transcript in this context ( S3 Fig ) –overexpression behaviour of reaperKR was assumed to follow a similar pattern to Michelob but was not directly assessed . Survival of double hemizygotes was not significantly different from controls ( Kaplan-Meier survival analysis; Michelob: P = 0 . 616 , X2 = 3 . 5 , df = 5 , ReaperKR: P = 0 . 677 , X2 = 3 . 2 , df = 5 ) . These results were surprising as ectopic/exogenous expression of pro-apoptotic genes including Reaper and Michelob_x has previously been shown to be lethal in insect cell lines as well as causing tissue ablation in vivo [23–25] . Additionally , this tetO-Michelob_X effector line has previously been successfully used to cause disruption of female indirect flight muscles ( Actin4 promoter ) and hence flightlessness in Ae . aegypti [16] . One possible reason for this discrepancy may be due to an interplay between the differentiation stage of the cells being targeted in this study and previous studies and their relative sensitivities to programmed cell death signals . Whereas the expression profile of the Actin4 promoter used previously coincides with the development of the pupal female-flight muscles ( and thus with significant cell division and differentiation ) , the VitellogeninA1 gene expresses in a tissue ( adult fat body ) which has already developed and whose cells are fully differentiated . With expression of our previously validated lethal effectors in the fat body having no apparent phenotype , we chose to employ a non-cell-autonomous approach ( Fig 1C ) in order to maintain blood-meal inducible timing but target potentially more sensitive tissues . Widespread use against adult mosquitoes of insecticides that target voltage-gated sodium channels ( VGSC ) [26] at neuromuscular junctions indicated that this could be an appropriate target for an effector at this life-stage . We designed a synthetic , neurotoxic effector construct consisting of a fusion between the invertebrate-specific , VGSC-targeting , scorpion-toxin gene AaHIT from Androctonus australis hector [27 , 28] and the secretory signal peptide from Autographa californica baculovirus major envelope glycoprotein ( gp67 ) [29] , under the transcriptional control of tetO . Our hypothesis was that , when combined with the VgA1-tTAV line , this construct would allow blood-meal inducible , fat-body specific expression of AaHIT , which would be secreted into the haemolymph after translation; a common feature of fat-body expressed proteins such as VitellogeninA1 [30] , where it would circulate , eventually being made available to VGSCs at neuromuscular junctions . To test this we generated a mosquito line carrying tetO-gp67 . AaHIT ( tetO-AaHIT ) and crossed it to the VgA1-tTAV line to look for a blood-meal inducible phenotype in the progeny under full factorial conditions ( all three genotypes i . e . tetO-AaHIT , VgA1-tTAV and tetO-AaHIT+ VgA1-tTAV ( VgA1>AaHIT ) , on and off-tetracycline ) . Beginning at 16h pbm , VgA1>AaHIT females reared in the absence of tetracycline ( “off-tet” ) exhibited loss of motor control and paralysis consistent with the known mode of action of AaHIT , an excitatory neurotoxin ( Fig 2 ) . “Knockdown” comprised a gradient of behaviours from mild , where females could not fly but staggered haphazardly on the bottom of the cage , to severe , where they lay on their dorsal thorax with their legs and wings convulsing asynchronously ( see S1–S5 Videos for recordings of individual knockdown paralysis phenotypes ) . The proportion of the post-blood-feeding cohort that showed this phenotype increased rapidly from c . 20h pbm , consistent with the temporal pattern of tTAV expression in the VgA1-tTAV line . The last knockdown event occurred at 26h pbm at which point 44/78 ( 56 . 4% ) of VgA1>AaHIToff-tet females displayed the phenotype . Starting from 27h pbm , knockdown females began to recover ( defined as fully regaining the ability to fly–see methods and S3–S5 Videos ) . By 67h pbm all knockdown females had either recovered or died ( 77 . 3% recovery ) with the average time spent knocked down before recovery being 20 . 4h ± 1 . 94 . This recovery behaviour suggests that the relatively brief expression of tTAV driven by VgA1 was insufficient to cause more than a sub-lethal effect and/or the mode of action of AaHIT did not result in irreversible depolarisation of the nerve axons/terminals , similar to some type I pyrethroids [26] . One single female each from the VgA1-tTAVon-tet and tetO-AaHIToff-tet cages were recorded as non-moving ( at 27h and 1h pbm , respectively ) . However , it was immediately established that these females had died , rather than become paralysed . Analysis of cages where at least one individual died did not identify significantly higher levels of mortality in the VgA1>AaHIToff-tet cage ( which uniquely displayed the knockdown phenotype ) compared to other genotype x tet-status cages ( Pairwise Fishers exact test , VgA1>AaHIToff-tet: tetO-AaHIToff-tet , p = 0 . 096; VgA1>AaHIToff-tet: VgA1-tTAVon-tet , p = 0 . 096; VgA1-tTAVon-tet: tetO-AaHIToff-tet , p = 1 ) . As background levels of mortality were not significantly different than in control cages , data from females that died was removed prior to further analysis of the reversible knockdown phenotype in the VgA1>AaHIToff-tet cage . A binomial Generalised Additive Model ( GAM ) was fitted to the data set ( Fig 3A ) using the mgcv R package and was found to have a highly significant smoothing term ( X2 = 371 , p = <0 . 005 ) . This model predicted peak knockdown of the VgA1>AaHIToff-tet cohort at 27 . 7h pbm with 44 . 1% ± 2 . 11 of the cage paralysed at this point . Using the output from this model , the rate of change in proportion knockdown over time was calculated ( Fig 3B ) and it was found that females in this cage were knocking down most rapidly at 21 . 9h pbm at an estimated rate of approx . 0 . 1%/min . Surviving females were subdivided depending on whether they had experienced knockdown in the previous experiment ( KnockDown = KD; this only applies to VgA1>AaHIToff-tet females as no knockdown was observed for other genotypes or for VgA1>AaHITon-tet ) or had not ( Not KnockDown = NKD ) . A Dunn test for multiple comparisons with Benjamini-Hochberg correction found no significant difference in the numbers of eggs laid by VgA1>AaHIToff-tet—KD females versus females from the same cage which had not knocked down ( VgA1>AaHIToff-tet -NKD; Z = 0 . 797 , p = 0 . 213 ) suggesting no significant effect of paralysis on oviposition capability . VgA1>AaHITon-tet -NKD females laid significantly fewer eggs than the single hemizygotes , but did not differ significantly when compared to VgA1>AaHIToff-tet irrespective of knockdown ( S1 Table ) . A Poisson GLM found that the amount of time individual females spent knocked down did not significantly correlate with their subsequent fecundity ( β = 0 . 498 , t = -1 . 75 , p = 0 . 091 ) . Although the knockdown phenotype was pronounced in those females experiencing it , the penetrance of this phenotype was incomplete . With VGSC resistance mechanisms such as the kdr mutation being extremely widespread , we were interested to examine whether the lack of sensitivity to the expressed scorpion toxin may also have a genetic basis . As a first step towards this we performed a second knockdown experiment using those females which had survived the first , immediately after oviposition . The aim of this experiment was to assess whether previous susceptibility to AaHIT knockdown predicted behaviour following a second exposure to this neurotoxin . As for the first blood meal , VgA1>AaHIToff-tet females showed a knockdown response after a second blood meal ( S4 Fig ) . However , unlike for the first blood meal , a small proportion of VgA1>AaHITon-tet females ( 2/30 = 6 . 66% ) also showed a knockdown phenotype , albeit at a later time period ( c . 10h later ) than those fed off-tetracycline . Within the VgA1>AaHIToff-tet females , a higher percentage of KD females became paralysed ( 8/38 = 21 . 0% ) compared with NKD females ( 4/42 = 9 . 52% ) although this effect was non-significant ( binomial GLM: z = 1 . 41 , p = 0 . 159 ) . Therefore our data do not support the conclusion that there is a consistent susceptibility of individuals to knockdown that may have implied an underlying genetic basis for the lack of penetrance observed . However , to fully elucidate this , multi-generational heritability experiments would be required . In general , levels of knockdown in this second experiment were more modest than in the first . One possibility for this may have been the reduced activity of the VgA1 promoter fragment . With VitellogeninA1 protein already having been produced for the first gonotrophic cycle , the levels of induction may be diminished on subsequent blood-feeding events leading to lower levels of tTAV/AaHIT expression from the integrated transgene . Alternatively , it is possible that sensitivity of the target neuromuscular junctions to AaHIT may exhibit plasticity with repeated sub-lethal exposure resulting in reduced response . Recovery behaviour was observed in this second knockdown experiment but this too was reduced compared to that seen previously ( VgA1>AaHIToff-tet—KD = 3/8 recovered—average time spent knocked down before recovery = 18 . 3h ± 1 . 67 , VgA1>AaHIToff-tet—NKD = 1/4 recovered—time spent knocked down before recovery = 25h ) . However , all females that were knocked down a second time ( including those which initially recovered ) died prior to oviposition . Together these results suggest that with increasing age/gonotrophic cycles , females may become less sensitive to the neurotoxic effects of AaHIT , however , the consequences for those that do become paralysed are more serious , resulting in significant mortality . We note that knockdown behaviour after the second blood-meal would be less relevant in any eventual field use as transgenic females would likely be required to not survive to a second blood meal in order to reliably reduce disease transmission . As none of the females that were knocked down a second time survived to lay eggs , only VgA1>AaHIToff-tet -NKD and VgA1>AaHIToff-tet—KD females which had not been knocked down during the second blood meal could be compared during a second oviposition assay . In effect , this comparison analysed the longer-term consequences on fecundity of a single , initial , knockdown . Results of a Gaussian GLM of log ( eggs laid ) suggested no significant difference in oviposition ability between the two groups of females ( t = 0 . 966 , p = 0 . 338 ) . Here we demonstrate for the first time an ‘action at a distance’ phenotype in a globally important human disease vector , Aedes aegypti . Using a synthetic neurotoxic effector engineered to be secreted out of the tissue in which it is produced , we achieved a non-cell autonomous , blood-meal-inducible paralysis phenotype . As with some insecticides which also target VGSCs , this knockdown was transient with affected females recovering and displaying no significant reductions in terms of fecundity or survival . Under field conditions , however , such prolonged incapacitation would likely be fatal unless the mosquito is able to find and maintain a safe resting place before onset of incapacitation . The lack of full penetrance and lethality allowed us to investigate this phenotype over multiple gonotrophic cycles . However , in order to reduce disease transmission in the field a more reliable mortality rate after the first blood-meal would be necessary . This might be achievable simply by testing additional insertion lines , or by design modifications such as integration of a positive-feedback system such that , once induced by the VgA1 promoter , tTAV could then continue to induce its own ( and therefore AaHIT ) expression indefinitely . Choice of promoter and integration site may also prove useful in maximising the competitiveness of transgenic larvae through limiting expression of a chosen effector to the post-blood-meal female . This consideration is of particular importance to GPM strategies such as those described here which aim to limit any deleterious effect until after the density-dependent stage . Additionally , alternative arthropod-specific neuroactive effectors could be tested for increased penetrance , for example the spider [31] or sea anemone [32] toxins . A class of potentially highly potent but as yet unrealised neuroactive effectors are the endogenous insect neuropeptides [33] . These small secreted molecules act on neuronal cell-surface receptors and are responsible for regulating an extensive range of insect behaviours from reproduction and diapause/migration through to feeding and homeostasis/development . The ability to manipulate these behaviours in insects holds great promise for development of pest management tools [34 , 35] . Other bloodmeal-inducible promoters are also available , for example , the blood-meal-inducible , mid-gut specific CarboxypeptidaseA promoter [36] , with different timing and tissue specificity . The diversity and flexibility of these non-cell autonomous phenotypes suggests that they will be extremely useful in engineering more potent and effective forms of genetic pest management .
Aedes aegypti mosquitoes ( Liverpool wild type strain ) were reared in standard insectary conditions including 12:12 hour light:dark cycle , 70% ( ±10% ) relative humidity , 26°C ( ±1°C ) and constant air circulation . Larvae were fed TetraMin ornamental Fish Flakes ( Tetra GmbH ) using the following feeding regimen per larva: 0 . 08 mg on day 1; 0 . 48 mg on day 2; 0 . 48 mg on day 3; 0 . 32mg on days 5–10; 0 . 06 mg on day 12 , or were fed ad libitum in excess for the characterisation of reversible paralysis phenotype . Sex of pupae was determined by identification of the genital lobe . Identification of transgene by fluorescent profile was also conducted at pupal stage using a Leica 165FC microscope , VgA1-tTAV: 3xP3-ECFP or tetO-AaHIT: HR5-IE1-DsRed . Adult mosquitoes were maintained in BioQuip cages with a 10% sucrose solution and/or a water-soaked cotton wool pad . Bloodfeeds were conducted using a Hemotek blood feeder with defibrinated horse blood ( TCS Bioscience ) . The VgA1-tTAV tissue specific construct ( piggyBac[Hr5IE1-AmCyan-VgA1-tTAV-SV40] ) ( Genbank accession number: MK795197 ) was made by modifying piggyBac[Hr5IE1-AmCyan-Carb-tTAV-SV40] ) plasmid to change the Carb promoter region to the VitellogeninA-1 promoter region [37] The effector constructs are piggyBac-based and contain the Hr5IE1-DsRed2 transformation marker , tetO repeats and an open frame for the effector . The tetO-Michelob_X effector construct is as previously described [16] . The tetO-Reaper construct contains the Drosophila melanogaster Reaper ( Gene Id: 40015 ) CDS and the tetO-AaHIT construct contains the full length AaHIT CDS , which was synthesised by Geneart ( Germany ) ( Genbank accession number: MK795198 ) The tetO-Dsred2 reporter line is as previously described [16] . Transgenic lines were created using protocols previously described [6 , 16 , 38] . Details of injection results , line selection and insertion site characterisation [39 , 40] are provided in Supporting Information ( S2 Table , S3 Table and S4 Table ) . VgA1-tTAV hemizygous individuals were crossed to the hemizygous tetO-michelob_x and tetO-ReaperKR effector strains . Half of the progeny were reared on-tetracycline ( tet ) conditions and the other half were reared off-tetracycline by hatching separately in 200 ml of filtered water . Three hours later , tet was added to the ‘on-tet’ half to a final concentration of 30μg ml-1 . 1000 L1 larvae were aliquoted into trays ( n = 3 per treatment ) , and reared at 1 larvae ml-1 . In parallel , wild-type larvae were hatched and aliquoted into separate trays and reared at the same density with no tet , and males were kept for crosses . Larvae were fed powdered TetraMin Ornamental Fish Flakes ( Tetra GmbH , Germany ) according to the feeding regimen described . Pupae were screened for transformation markers and male pupae were discarded . Female adults were blood fed three days after the last adult eclosed , and to enable synchronous blood feeding , sugar was removed from the females’ cages the day before . To prevent damage to engorged females , insects that had not taken a blood meal were removed from the cages and were excluded from the analysis . On the same day , wild-type males were added to cages at a 0 . 5:1 male:female ratio . Dead females were counted and removed from cages daily until the end of the experiment , with particular attention paid to 20–28 hours after a blood meal , which corresponds to previously published reports on the VgA1 promoter’s peak activities . Eggs were collected from the cages overnight . Females were blood fed for a total of three gonotrophic cycles; non-engorged females were separated from cages after each blood meal . Kaplan Meier survival analysis was carried out using the ‘Rcmdrplugin . survival’ package in the statistical program ‘R for Mac OS X Version 1’ , available publicly from http://cran . r-project . org . Details of individual statistical analysis are described in the text . All statistical analysis was conducted using R . In general , a GLM framework was preferred for data analysis . Models fit was checked using various transformations/variance structures and QQ-plot and checked for over-dispersion . If assumptions could not be met , a non-parametric alternative was chosen . When multiple comparisons were made this was first tested using an appropriate omnibus test followed by post-hoc MultiComp testing .
|
A recent addition to the toolbox for controlling populations of the disease vector Aedes aegypti is the mass-release of males engineered with dominant , lethal transgenes . The lethal effect of these transgenes is activated in the progeny of these released engineered males and wild females they mate with in the field and with continuous release of males can cause population collapse . To date , these systems have relied on the use of ‘cell-autonomous’ effectors , meaning that their action is restricted to the cells in which they are expressed , limiting the flexibility of designing new , more complex systems . Here we demonstrate that it is possible to engineer ‘non-cell autonomous’ effectors–that is where the effect ( e . g . the action of a toxic protein ) can act on cells distant from the tissues in which they are originally expressed . To achieve this we utilised the endogenous cell secretory pathway to engineer a novel control phenotype–blood-meal inducible ( i . e . late-acting , female-specific ) reversible paralysis . The logic behind engineering such ‘action at a distance’ phenotypes will extend to a variety of other pest insects and control phenotypes .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"discussion",
"Methods"
] |
[
"antimicrobials",
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"drugs",
"microbiology",
"animals",
"reproductive",
"physiology",
"insect",
"pests",
"developmental",
"biology",
"tetracyclines",
"antibiotics",
"pest",
"control",
"pharmacology",
"insect",
"vectors",
"pests",
"infectious",
"diseases",
"aedes",
"aegypti",
"life",
"cycles",
"disease",
"vectors",
"insects",
"agriculture",
"arthropoda",
"mosquitoes",
"eukaryota",
"blood",
"anatomy",
"physiology",
"oviposition",
"microbial",
"control",
"biology",
"and",
"life",
"sciences",
"species",
"interactions",
"larvae",
"organisms"
] |
2019
|
Engineered action at a distance: Blood-meal-inducible paralysis in Aedes aegypti
|
The binding of transcription factors to short recognition sequences plays a pivotal role in controlling the expression of genes . The sequence and shape characteristics of binding sites influence DNA binding specificity and have also been implicated in modulating the activity of transcription factors downstream of binding . To quantitatively assess the transcriptional activity of tens of thousands of designed synthetic sites in parallel , we developed a synthetic version of STARR-seq ( synSTARR-seq ) . We used the approach to systematically analyze how variations in the recognition sequence of the glucocorticoid receptor ( GR ) affect transcriptional regulation . Our approach resulted in the identification of a novel highly active functional GR binding sequence and revealed that sequence variation both within and flanking GR’s core binding site can modulate GR activity without apparent changes in DNA binding affinity . Notably , we found that the sequence composition of variants with similar activity profiles was highly diverse . In contrast , groups of variants with similar activity profiles showed specific DNA shape characteristics indicating that DNA shape may be a better predictor of activity than DNA sequence . Finally , using single cell experiments with individual enhancer variants , we obtained clues indicating that the architecture of the response element can independently tune expression mean and cell-to cell variability in gene expression ( noise ) . Together , our studies establish synSTARR as a powerful method to systematically study how DNA sequence and shape modulate transcriptional output and noise .
The interplay between transcription factors ( TFs ) and genomically encoded cis-regulatory elements plays a key role in specifying where and when genes are expressed . In addition , the architecture of cis-regulatory elements influences the expression level of individual genes . For example , transcriptional output can be tuned by varying the number of TF binding sites , either for a given TF or for distinct TFs , present at an enhancer [1 , 2] . Moreover , differences in its DNA-binding sites can modulate the magnitude of transcriptional activation , as exemplified by the glucocorticoid receptor ( GR ) , a hormone-activated TF [3–5] . The sequence differences can reside within the 15 base pair ( bp ) core GR binding sequence ( GBS ) consisting of two imperfect 6 bp palindromic half-sites separated by a 3 bp spacer . Although the effects on activity are more modest than those observed for changes within the core , sequences directly flanking the core also modulate GR activity [3] . However , these sequence-induced changes in activity cannot be explained by affinity [3] . Instead , the flanking nucleotides induce structural changes in both DNA and the DNA binding domain of GR , arguing for their role in tuning GR activity [3] . Notably , the expression level of a gene is typically measured for populations of cells and thus masks that expression levels can vary considerably between individual cells of an isogenic population [6–9] . This variability in the expression level of a gene , called expression noise , results in phenotypic diversity , which can play a role in organismal responses to environmental changes ( so called bet-hedging ) and in cell fate decisions during development . Expression noise can be explained by the stochastic nature of the individual steps that decode the information encoded in the genome . For example , transcription occurs in bursts [7 , 10–12] , which can induce variability in gene expression due to differences in burst frequency and in the number of transcripts generated per burst ( burst size ) [13] . Noise levels are gene-specific , which can be explained in part by differences in the sequence composition of cis-regulatory elements [11 , 14–16] . For instance , the sequence composition of promoters influences expression variability with high burst size and noise for promoters containing a TATA box [15 , 17] . In addition , chromatin and the presence or absence of nucleosome-disfavoring sequences have been linked to transcriptional noise [16–19] . Finally , noise levels can also be tuned by the number and by the affinity of TF binding sites [11 , 16] . Many fundamental insights regarding the role of sequence in tuning transcriptional output and noise have come from reporter studies [20 , 21] . A key advantage of reporters is that they can provide quantitative information in a controlled setting where everything is kept identical except for the sequence of the region of interest . Until recently , a limitation of reporter studies was that sequence variants had to be tested one at a time . However , the recent development of several parallelized reporter assays allows the simultaneous assessment of many sequence variants [21] . One of these parallelized methods is STARR-seq ( Self-Transcribing Active Regulatory Region sequencing ) [22] . In this assay , candidate sequences are placed downstream of a minimal promoter , such that active enhancers drive their own expression and high-throughput sequencing reveals both the sequence identity and quantitative information regarding the activity of each sequence variant . The STARR-seq method has been used to assay enhancer activity genome-wide [22 , 23] , to study regions of interest isolated either by Chromatin Immunoprecipitation ( ChIP ) or a capture-based approach [24 , 25] , and to study the effect of hormones on enhancer activity [25 , 26] . Here , we adapted the STARR-seq method to systematically study how sequence variation both within the 15 bp GBS and in the region directly flanking it modulate GR activity . Specifically , we generated STARR-seq libraries using designed synthetic oligos ( synSTARR-seq ) with randomized nucleotides flanking the core GBS to show that the flanks modulate transcriptional output by almost an order of magnitude . When grouping sequences based on their ability to either enhance or blunt GBS activity , we found that each group contained a broad spectrum of highly diverse sequences , but striking similarities in their DNA shape characteristics . Using the same approach , we also assayed the effect of sequence variation within the core GBS . Finally , using single cell experiments with individual enhancer variants , we study how the sequence composition of the response element influences expression mean and noise . Together , our studies establish synSTARR-seq as a powerful method to study how DNA sequence and shape modulate transcriptional output and noise .
To test if we could use the STARR-seq reporter [22] to study how sequence variation of the GR binding site influences GR activity , we first tested if a single GBS is sufficient to facilitate GR-dependent transcriptional activation of the reporter . Therefore , we constructed STARR reporters containing either a single GBS as candidate enhancer ( Fig 1A ) , a randomized sequence or as positive control a larger GBS-containing sequence derived from a GR-bound region close to the GR target gene FKBP5 . The resulting reporters were transfected into U2OS cells stably expressing GR ( U2OS-GR ) [27] and their response to treatment with dexamethasone ( dex ) , a synthetic glucocorticoid hormone , was measured . As expected , no marked hormone-dependent induction was observed for the reporter with the randomized sequence . This was true both at the level of RNA ( Fig 1B ) and at the level of the GFP reporter protein ( S1 Fig ) . In contrast , we observed a robust hormone-dependent activation both at the level of RNA and GFP protein for reporters with either a single GBS or with the larger genomic FKBP5 fragment ( Fig 1B and S1A Fig ) , showing that a single GBS is sufficient for GR-dependent activation of the STARR-seq reporter . Our previous work has shown that the sequence directly flanking GBSs can modulate DNA shape and GR activity [3] . For a parallelized and thorough analysis of sequence variants flanking a GBS , we generated STARR-seq libraries for two GBS variants , we previously named Cgt and Sgk , that showed a strong influence of flanking nucleotides on activity [3] . Specifically , we generated libraries using designed synthetic sequences ( synSTARR-seq ) containing a GBS with five consecutive randomized nucleotides directly flanking the imperfect half site ( Fig 1A and S2A Fig ) . Next , we transfected the GBS flank libraries into U2OS-GR cells to determine the activity of each of the 1024 flank variants present in the library . We performed three biological replicates for each condition and found that the results were highly reproducible ( r ≥ 0 . 91 for vehicle treated cells , r ≥ 0 . 98 for dex treated cells; Fig 1C and S1B–S1E Fig ) . Notably , we retain duplicate reads in our analysis , which is essential to get quantitative information for individual sequence variants of the library . To calculate the activity for each flank variant , we used DESeq2 [28] to compare the RNA-seq read number between dex- and vehicle ( ethanol ) treated cells ( Fig 1A ) . This resulted in the identification of 189 flank variants with significantly higher activity ( enhancing flanks ) , 125 flank variants with significantly lower activity ( blunting flanks ) and 710 flank variants that did not induce significant changes in activity ( neutral flanks ) . To test the accuracy of the synSTARR-seq data , we cloned 5 flank variants from each activity group ( enhancing , blunting and neutral ) and assayed the activity of each variant individually by qPCR . Consistent with what we observed for the synSTARR library , the activity of blunting flanks was significantly lower than for the neutral flanks whereas the activity of the enhancing flanks was significantly higher ( Fig 1D ) . Notably , all flank variants tested were activated upon dex treatment ranging from 2 . 1 to 15 . 3 fold ( 627% higher ) depending on the sequence of the flank . Together , our results show that the synSTARR-seq assay produces reproducible and quantitative information and can be used for a high-throughput analysis of the effect of the flanking sequence on GBS activity . To assess how the sequence composition of the flanking region influences GBS activity , we ranked the flank variants by their activity and used a color chart representation to plot the sequence at each position for the Cgt ( Fig 2A ) and Sgk GBS ( S2A Fig ) , respectively . In addition , we generated consensus sequence motifs for the significantly enhancing and blunting variants ( Fig 2B and S2B Fig ) . Notably , these consensus sequence motifs treat each sequence equally and do not take the quantitative information regarding the activity of each sequence into account . To take advantage of the quantitative information provided by the synSTARR-seq assay , we used kpLogo [29] , which uses the fold change as weight for each sequence variant , and statistically evaluates the enrichment/depletion of specific nucleotides at each position . The resulting probability logo can be interpreted as an activity logo that visualizes for each position which nucleotides are associated with either higher ( letters above the coordinates ) or lower ( below the coordinates ) GBS activity ( Fig 2C and S2C Fig ) . The activity logo , consensus motifs and color chart highlight several sequence features for enhancing and blunting flank variants . For example , high activity is associated with a T at position 8 for both the Cgt and Sgk GBS , which matches what we found previously when we studied the activity of endogenous GR-bound regions [3] . In addition , the most active flank variants preferentially have an A at position 9 followed by a C at position 10 ( Fig 2A and S2A Fig ) . To validate that this “TAC” signature results in high activity , we shuffled the sequence to either TCA or CAT and found that this indeed resulted in markedly lower activity ( Fig 2D ) . For blunting flank variants , we observed a preference for an A at position 8 and a bias against having a C at position 10 ( Fig 2A and 2C and S2A and S2C Fig ) . However , altogether we find that the consensus motifs for enhancing and blunting flanks only have low information content and that a broad spectrum of distinct sequences can enhance or blunt the activity of the adjacent GBS ( Fig 2B and S2B Fig ) . Our previous work [3] indicates that DNA shape can influence GR activity downstream of binding . Consistent with this notion , we measured similar Kd values for flanks variants from the different activity classes ( Fig 2E ) . These findings are also in agreement with published work showing that the nucleotides directly flanking GBSs have little effect on GR affinity [30] . To examine if the flank effects might be explained by differences in DNA shape , we calculated the predicted minor groove width , roll , propeller twist and helix twist [31] for enhancing and blunting flank variants ( Fig 3A and S2D Fig and S3 Fig ) . Consistent with a role for DNA shape in modulating GR activity , we found shape characteristics that differ between enhancing and blunting flanks . For example , we observed a wider minor groove at position 6 , and to a lesser degree at position 7 for blunting flanks of the Cgt GBS , when compared to enhancing flanks ( Fig 3A and S4A Fig ) . In addition , blunting flanks for the Cgt GBS have a narrower minor groove than enhancing flanks for positions 8–12 ( Fig 3A and S4A Fig ) , a region with several non-specific minor groove contacts with the C-terminal end of the DNA binding domain of GR [5] . For the Sgk GBS library , we find similar shape characteristics associated with blunting flanks with a wider minor groove at position 6 and a narrower minor groove for positions 8–12 ( S2D Fig and S4B Fig ) . DNA-shape-based hierarchical clustering recapitulates these characteristics in cluster 4 , containing many more blunting flanks than any of the other clusters , for both the Cgt and Sgk GBS flank libraries ( Fig 3B and 3C and S2E and S2G Fig ) . Of note , the consensus motifs for cluster 4 and for the other shape clusters have only low information content ( Fig 3D and S2F Fig ) indicating that distinct sequences can give rise to similar shape characteristics with shared effects on the activity of the adjacent GBS . Together , these synSTARR-seq experiments uncover how sequence variation in the flanking region of the GBS influences activity and point at a role for DNA shape in modulating GBS activity . We next generated an additional synSTARR-seq library to study the effect of variation within the 15bp core sequence . This library contains a fixed GBS half site followed by eight consecutive randomized nucleotides ( Fig 4A ) . The library , containing over 65 , 000 variants , was transfected into U2OS-GR cells and the read count for each variant was determined both in the presence and absence of hormone treatment . Compared to the flank library , we observed a lower correlation between experiments , especially for variants with a low read count ( S5 Fig ) . Specifically , when we compared the read count between biological replicates , we found that sequences with a read count below 100 were typically detected in only one of the replicates . Therefore , we decided to remove sequences with a mean read count below 100 across all experiments . Next , we analyzed data from three biological replicates to determine the activity of variants in the library ( Fig 4B ) . To validate the measured activities , we cloned 4 sequences that repress , 4 that show a weak activation ( log2 fold change <2 ) and 8 strongly activating GBS variants . Consistent with the results from our screen , the three groups showed distinct levels of activity ( Fig 4B and 4C ) . However , for the group of repressed GBS variants we did not recapitulate the observed repression in our screen ( Fig 4C ) , indicating that these variants might behave differently in isolation . Alternatively , what looks like repression might be a consequence of issues with data normalization , which assumes that the distribution of the log fold changes is centered on 0 , which is not given when GBS variants can activate but not repress gene expression . Notably , a lack of GR-dependent transcriptional repression was also reported in another study using the STARR-seq approach to study the regulatory activity of GR-bound genomic regions [25] indicating that GR might not be able to repress transcription in the STARR-seq context . Given that the observed repression was not reproducible , we concentrated our analysis on 1696 sequences that facilitated significant GR-dependent transcriptional activation . Consistent with activation , we found that the consensus motif for activating sequence variants recapitulates the known GR consensus sequence with the second half site 3-bp downstream of the fixed first half site of our library ( Fig 4D ) . Accordingly , the GBS motif weight , which serves as a proxy for DNA binding affinity , is higher for activating sequences when compared to sequences that did not respond to hormone treatment ( Fig 4G ) . However , the score for the top 10% most active sequences was not higher than for all active variants ( Fig 4G ) , arguing that higher affinity does not drive the high levels of activation . As expected and consistent with the GR consensus motif , the color chart ( Fig 4D ) and activity logo ( Fig 4E ) highlight a strong preference for a G at position 3 and accordingly GBS activity is significantly lower for variants with a nucleotide other than G at this position ( S6A Fig ) . The activity logo also highlights that a G at position 2 is associated with lower activity ( Fig 4E and 4F ) . Previous studies have shown that the sequence of the spacer can modulate GBS activity [4 , 5] . Therefore , we compared the activity of all 16 spacer variants in our library that match the GBS consensus for the second half site at the key positions 3 , 4 and 6 ( S7A Fig ) . In line with a role for the spacer in modulating transcriptional output , we find significant differences between the spacer variants ( S7B Fig ) . For example , the activity for variants with an AC spacer is significantly higher than for several other spacer variants ( S7B Fig ) whereas the activity for GT variants is significantly lower ( p . adj < 0 . 01 ) than either AA , AC or TC variants ( S7B Fig ) . Unexpectedly , the activity logo and top of the color chart indicated a high activity for variants with a C at position 2 ( Fig 4D and 4E ) , instead of a consensus T observed in the GR consensus motif and from in vitro experiments studying the effect of DNA sequence on GR DNA binding affinity [30] . A careful examination of the sequence composition of the most active variants also revealed a preference for TC at the preceding positions within the spacer ( Figs 4E and 5A ) . To test if the high activity for sequences with a C at position 2 depends on the nucleotide composition of the preceding nucleotides , we changed them to GG and found that this resulted in a marked reduction in GR-dependent activation ( Fig 5B and S8A Fig ) . In addition , we compared the activity between variants with a T or a C at position 2 . The activity was higher for the C variant when preceded by TC . However , when we changed the preceding nucleotides to GG the activation was stronger for the T than the C variant ( Fig 5B and S8A Fig ) . These experiments indicated that the high activity for the C variant depends on the preceding nucleotides . Interestingly , the most active variants resemble the sequence composition of the “combi” motif we identified previously [32] . The combi motif contains only a single GR half site followed by TTCC and we found evidence that GR binds this sequence as a monomer in conjunction with a partnering protein [32] . Similar to the combi motif , several of the most active variants ( Fig 5A ) contain a GR half site followed by TTCC . However , whereas the combi motif lacks a second GR half site , the motif for the 25 most active variants from our screen ( named “combi2” ) also contains a recognizable second GR half site ( Fig 5A ) . To gain insight into the mode of GR binding at the combi2 motif , we examined published ChIP-exo data [32] . ChIP-exo is an assay that combines ChIP with a subsequent exonuclease step [33] which results in a base-pair resolution picture of GR binding . The ChIP-exo signal takes the form of sequence-specific peak patterns ( footprint profiles ) , detectable on both strands with the program ExoProfiler [32] . We applied ExoProfiler to scan GR-bound regions with the combi2 motif ( Fig 5D and 5E , solid lines ) . As control , we analyzed the footprint profile for the canonical GR consensus motif ( Fig 5D; JASPAR MA0113 . 2 ) and recovered peak pairs on the forward and reverse flanks that demarcate the protection provided by each of the monomers of the GR dimer ( Fig 5E , shaded area ) . The signal for the first half site is essentially the same and a similar pattern is also observed for the second half site , indicating that GR binds as a dimer on regions bearing the combi2 motif , however with additional signal ( highlighted with black arrows in Fig 5E ) . In addition , we compared the footprint profile between the original combi ( Fig 5D; [32] ) and the combi2 motif ( Fig 5F ) . Again , the position and shape of the peaks are compatible for the first half site but the ChIPexo signal for the second half site looks markedly different . The aforementioned additional signal for the combi2 motif aligns with the position of the second peak pair of the combi motif ( Fig 5F ) , indicating that the footprint profile for the combi2 motif appears to be a composite of the signal for homodimeric GR binding at canonical GBSs and the signal for monomeric GR binding together with another protein . Our previous work suggests that this partnering protein on combi motif might be Tead or ETS2 . The ChIP-exo profile thus points to three alternative binding configurations on combi2: homodimeric GR , monomeric GR binding with Tead/ETS2 or the simultaneous binding of homodimeric GR complex together with Tead/ETS2 . Structural modeling suggests that this third mode is possible given the absence of obvious sterical clashes that would prevent this mode of binding ( Fig 5G ) . However , additional functional studies are needed to determine if GR indeed partners with Tead/ ETS2 , or possibly with other proteins , at the combi2 motif . To assess if DNA shape could play a role in modulating GBS activity , we calculated the predicted minor groove width for all 1696 significantly activated sequences ranked by activity ( S6B Fig ) . Comparison of the top 20% most active and bottom 20% least active sequence variants highlighted two regions with significant differences . First , consistent with our findings for the flank library , we find that a wider minor groove at positions 6 and 7 correlates with weaker activity ( S6B and S6C Fig ) . Second , we find that a narrower minor groove in the spacer ( position -1 and 0 ) correlates with weaker activity ( S6B and S6C Fig ) . As we observed for the flank variants , the different activity classes do not show a distinct sequence signature ( S6B Fig ) again arguing that DNA shape might modulate GBS activity . Together , the findings for our half site library suggest a role for both DNA shape and sequence in tuning the activity of GBS variants . Moreover , our screen uncovered a novel high-activity functional GR binding sequence variant . Thus far , we analyzed the effect of sequence composition on transcriptional output by analyzing mean expression levels for populations of cells . To test if sequence variation in the enhancer influences cell-to-cell variability in gene expression ( noise ) , we measured GFP levels for individual STARR constructs in single cells ( Fig 6A and 6B ) . Cells were transfected with individual constructs along with an mCherry expression construct to remove extrinsic noise , for example caused by differences in transfection efficiency . We first analyzed sequence variants containing a single GBS ( single GBS group ) including known GBSs; two variants matching the combi2 sequence motif and the Cgt GBS with an enhancing flank variant . Consistent with previous findings [5] , we found that GBS variants from the single GBS group induced different mean levels of GFP expression . For example , the mean GFP level upon dex treatment was lower for the Pal GBS than for the Cgt variant ( Fig 6C , orange and red squares ) . In line with findings by others [16] , we observed that transcriptional noise scales with mean expression with lower noise for variants with higher mean expression ( Fig 6C ) . Next , we assayed two additional groups of sequences with distinct binding sites architectures that both result in more robust GR-dependent activation when compared to single GBS variants ( Fig 6A ) . The first group contained three instead of one GBS copy ( triple GBS group ) whereas the second group ( composite group ) contains a GBS flanked by a sequence motif for either AP1 , ETS1 or SP1 , three sequence motifs that can act synergistically with GR [34 , 35] . As expected , the mean GFP expression was higher for each member of both the triple GBS and the composite group when compared to the single GBS group ( Fig 6A and 6C ) . Interestingly , the increase in mean expression we found for the groups of triple GBS and composite enhancers was not accompanied by a decrease in expression noise ( Fig 6C ) . The high noise to mean expression ratio was especially striking for several triple GBS variants ( 3xPal , 3xCgt , 3xSgk and 3x Fkbp5-2 ) but observed in general for each member of the groups of triple and composite enhancers when compared to the single GBS group . Furthermore , enhancer variants with similar mean expression levels ( e . g . 3xSgk and Ets1+FKBP5-2 ) can have vastly different noise levels indicating that binding sites architecture can independently tune both mean expression and cell-to-cell variability in gene expression with noisier expression for enhancers with multiple GBSs .
Taken together , we present synSTARR , an approach to measure how designed binding site variants influence transcriptional output and noise . The systematic analysis of sequence variants presented here resulted in the identification of a novel functional GR binding sequence and provides evidence for an important role of DNA shape in tuning GR activity without apparent changes in DNA binding affinity . Our simple approach using designed sequences can be applied to other TFs and can be used to systematically unravel how the interplay between sequence and other signaling inputs at response elements modulate transcriptional output .
Plasmids . STARR reporter constructs were generated by digesting the human STARR-seq vector [22] with SalI-HF and AgeI-HF and subsequent insertion of fragments of interest by in-Fusion HD cloning ( TaKaRa ) . All inserts had the following sequence composition: 5’- TAGAGCATGCACCGGACACTCTTTCCCTACACGACGCTCT—-INSERT—-AGATCGGAAGAGCACACGTCTGAACTCCAGTCACTCGACGAATTCGGCC-3’ . Sequence homologous to the STARR reporter construct in bold; Sequence for p5 and p7 adaptors underlined . The exact sequence of the insert for each construct used in this study is listed in S1 Table . Cell lines , transient transfections and luciferase assays . U2OS cells stably transfected with rat GRα ( U2OS-GR ) [27] were grown in DMEM supplemented with 5% FBS . Transient transfections were done essentially as described [5] using either lipofectamine and plus reagents ( Invitrogen ) or using kit V for nucleofections ( Lonza ) . Synthetic STARR-seq . Library design and generation: To generate GBS variant libraries , oligos containing degenerate nucleotides ( N ) at defined positions were ordered from IDT as “DNA Ultramer oligonucleotide” ( sequence listed below ) . The oligonucleotides were made double stranded using Phusion polymerase ( NEB; 98°C for 35 sec , 72°C for 5 min ) using the revPrimer ( GGCCGAATTCGTCGAGTGAC ) . The resulting double stranded inserts ( 25ng ) were recombined with 100ng linearized ( SalI-HF and AgeI-HF ) STARR-seq vector [22] by in-Fusion cloning in 5 parallel reactions . After pooling the reactions , the DNA was cleaned up using AMPure XP beads ( Beckman Coulter ) , transformed into MegaX DH10B cells ( Invitrogen ) and plasmid DNA was isolated using a Plasmid Plus Maxi kit ( Qiagen ) . STARR-seq: For STARR-seq experiments , 5 million U2OS-GR cells were transfected with 5 μg library-DNA by nucleofection using kit V ( Lonza ) . The next day , cells were treated for 4 h with 1 μM dexamethasone or with 0 . 1% ethanol as vehicle control . Reverse transcription and amplification of cDNA for subsequence Illumina 50bp paired-end sequencing were done as described [22] . Cgt flank library DNA Ultramer oligonucleotide: TAGAGCATGCACCGGACACTCTTTCCCTACACGACGCTCTTCCGATCTCAGCGCAAGAACAtttTGTACGNNNNNCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACTCGACGAATTCGGCC Sgk flank library DNA Ultramer oligonucleotide: TAGAGCATGCACCGGACACTCTTTCCCTACACGACGCTCTTCCGATCTCAGCGCAAGAACAtttTGTCCGNNNNNCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACTCGACGAATTCGGCC GBS half site library DNA Ultramer oligonucleotide: TAGAGCATGCACCGGACACTCTTTCCCTACACGACGCTCTTCCGATCTCAGCGAAAGAACAtNNNNNNNNCGTCGCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACTCGACGAATTCGGCC RNA-seq U2OS-GR cells ( Fig 5C ) . U2OS-GR cells were treated for 4h with either 1μM dexamethasone or 0 . 1% ethanol as vehicle control . RNA was isolated from 1 . 2 million cells using the RNeasy kit from Qiagen . Sequencing libraries were prepared using the TruSeq RNA library Prep Kit ( Illumina ) . Prior to reverse transcription , poly adenylated RNA was isolated using oligo d ( T ) beads . Paired end 50bp reads from Illumina sequencing were mapped against the human hg19 reference genome using STAR [45] ( options:—alignIntronMin 20—alignIntronMax 500000—chimSegmentMin 10—outFilterMismatchNoverLmax 0 . 05—outFilterMatchNmin 10—outFilterScoreMinOverLread 0—outFilterMatchNminOverLread 0—outFilterMismatchNmax 10—outFilterMultimapNmax 5 ) . Differential gene expression between dex and etoh conditions from three biological replicates was calculated with DESeq2 [28] , default parameters except betaPrior = FALSE . Electrophoretic mobility shift assays . EMSAs were performed as described previously [3] using Cy-5 labeled oligos as listed in S2 Table . RNA isolation , reverse transcription and qPCR analysis . RNA was isolated from cells treated for either 4 h or overnight with 1 μM dexamethasone or with 0 . 1% ethanol vehicle . Total RNA was reverse transcribed using gene-specific primers for GFP ( CAAACTCATCAATGTATCTTATCATG ) and RPL19 ( GAGGCCAGTATGTACAGACAAAGTGG ) which was used for data normalization . qPCR and data analysis were done as described [5] . Primer pairs for qPCR: hRPL19-fw: ATGTATCACAGCCTGTACCTG , hRPL19rev: TTCTTGGTCTCTTCCTCCTTG , GFP-fw: GGCCAGCTGTTGGGGTGTC , GFP-rev: TTGGGACAACTCCAGTGAAGA . Noise-Measurements . For noise measurements , U2OS-GR cells were transfected using lipofectamine and plus ( Invitrogen ) essentially as described [5] . In short: The day before transfection , 40 , 000 U2OS-GR cells were seeded per well of a 24 well plate . The following day , cells were transfected with individual STARR reporter constructs ( 20ng/well ) along with a SV-40 mCherry expression construct ( 20ng/well ) and empty p6R plasmid ( 100 ng/ well ) . Transfected cells were treated overnight with either 1μM dexamethasone or with 0 . 1% ethanol vehicle control . Fluorescence intensity was measured using an Accuri C6 flow cytometer ( BD Biosciences ) and the yellow laser ( 552nM ) and filter 610/20 for mCherry and the deepblue laser ( 473nM ) and filter 510/20 to measure GFP . Gates were set for mCherry and GFP and only cells showing both mCherry and GFP fluorescence were included in the analysis . Relative expression of GFP ( GFP/Cherry ) , from 800–1600 individual dexamethasone-treated cells , was used to calculate mean expression and the standard deviation of cell populations . Mean and standard deviation for noise ( CV2 ) and for relative GFP expression were derived from three biological replicates . Analysis of synSTARR-seq data . RNA-seq reads were filtered and only sequences exactly matching the insert sequence in length and nucleotide composition were included in the analysis . The number of occurrences for each sequence variants was counted for each experimental condition and differentially expressed sequences were identified using DESeq2 [28] using a p adjusted value <0 . 01 as cut-off . To fit the dispersion curve to the mean distribution , we used the local smoothed dispersion ( DESeqwithfitType = "local" ) . Notably , each of the constructs of the flank libraries contains a functional GBS . Therefore , flanks that blunt activity will appear repressed after hormone treated because their fraction in the total pool of sequences decreases relative to flank variants with higher activities . For the flank libraries , we obtained information for each sequence variant ( 1024 ) in the library . For the half site library , we identified 61 , 582 out of the 65 , 536 possible variants present in this library . We found that including sequences with low read coverage resulted in many false positive differentially expressed GBS variants . To avoid this , we only included sequences with a mean read count above 100 across all experiments , leaving us with information for 33 , 689 sequence variants . The pearson correlation coefficient for replicates was calculated using the ggscatter function of the ggpubr library in R . Boxplots comparing groups of sequence variants as specified in the figure legends show center lines for the median; box limits indicate the 25th and 75th percentiles; whiskers extend 1 . 5 times the interquartile range from the 25th and 75th percentiles . Sequence logos to depict the consensus motif for groups of sequences were generated using WebLogo [46] . The probability logo ( activity motif ) was generated with kpLogo [29] using as input the sequence and fold change ( dex/etoh ) for each variant and the default settings for weighted sequences . Motif weight . The motif weight for each variant was calculated using the RSAT matrix-scan program [47 , 48] . Specifically , the motif weight was calculated using Transfac motif M00205 truncated to the core 15bp , and a custom background model created with RSAT create background program , trained on human open chromatin available at UCSC genome browser ( http://genome . ucsc . edu/cgi-bin/hgTrackUi ? db=hg19&g=wgEncodeRegDnaseClustered ) . Boxplots comparing groups of sequence variants show center lines for the median; box limits indicate the 25th and 75th percentiles; whiskers extend 1 . 5 times the interquartile range from the 25th and 75th percentiles . Comparison of ChIP-seq peak height between combi2 and canonical GBS motif . GR ChIP-seq data sets for U2OS-GR cells were downloaded as processed peaks from EBI ArrayExpress ( E-MTAB-2731 ) . ChIP-seq peaks in a 40 kb window centered on the transcription start site of differentially expressed genes ( RNA-seq data: E-MTAB-6738 ) were scanned using RSAT matrix-scan [47 , 48] for the occurrence of either a GBS-match ( Transfac matrix M00205 , p value cut-off: 10−4 ) or the combi2 matrix we generated ( Fig 5D , p-value cut-off 10−4 ) . Next , peaks were grouped by motif match and median peak height was calculated for each group and the p-value comparing both groups was calculated using a Wilcoxon rank-sum test to produce Supplementary S8B Fig . Comparison of gene regulation . To compare the level of activation between genes with nearby peaks with either a GBS match ( Transfac matrix M00205 , p value cut-off: 10−4 ) or a combi2 match ( motif Fig 5D , p-value cut-off 10−4 ) , we first scanned ChIP-seq peaks ( U2OS-GR cells: E-MTAB-2731 ) in a 40 kb window centered on the transcription start site ( using all annotated TSSs from Ensembl GRCH37 ) for motif matches using RSAT matrix-scan [47 , 48] . Only peaks with an exclusive motif match were retained to generate a boxplot comparing the log2 fold change for genes of each group ( RNA-seq data: E-MTAB-2731 ) . Center lines show the median , box limits indicating the 25th and 75th percentiles and whiskers extending 1 . 5 times the interquartile range from the 25th and 75th percentiles . p-value comparing the log2 fold change for both groups was calculated using a Wilcoxon rank-sum test to produce Fig 5C . DNA shape prediction . We used DNAshapeR [31] to predict the minor groove width , roll , propeller twist or helix twist for sequence variants of interest . Boxplots for individual nucleotide position show center lines for the median; box limits indicate the 25th and 75th percentiles; whiskers extend 1 . 5 times the interquartile range from the 25th and 75th percentiles . The Wilcoxon rank-sum test was used to calculate the p-values comparing nucleotide position variants between groups . Individual sites were clustered using K-means clustering with k = 4 clusters nstart = 20 and 100 restarts with the function 'kmeans' from the R 'stats' package . Classification of GBS activity . To assess classifier performance we generate ROC curves using 10-fold cross-validation . Four different models were tested to classify GBS activity into blunting or enhancing . A mononucleotide model consisting of sequence motifs estimated from relative nucleotide frequencies within the two classes . Class affiliation is predicted with a likelihood ratio test . We also tested a similar model based on dinucleotides . In addition , we tested two random forest ( RF ) classifiers with 100 trees , based on sequence and shape information . We used the R package "randomForest" for constructing the classifiers [49] . Since RF classifiers are not designed for categorical data , we coded nucleotide sequences using 00 for 'A' , 01 for 'C' , 10 for 'G' , and 11 for 'T' . ChIP-exo footprint profiles . ChIP-exo footprint profiles were generated using the ExoProfiler package [32] and published ChIP-exo ( EBI ArrayExpress E-MTAB-2955 ) and ChIP-seq ( E-MTAB-2956 ) data for IMR90 cells as input . Peaks were scanned using either the JASPAR MA0113 . 2 motif [50] , the PWM for the combi1 motif [32] , the combi2 motif ( Fig 5D ) or for the AC flank variant , the motif depicted in S9A Fig . Hits were included if the p-value was <10−4 . Overlay plots for distinct motifs were generated by aligning the profiles on the GBS and normalizing the signal for each motif variant to 1 . Structural alignment of GR:ETS1 complex . Structural alignment of the GR:ETS1 complex on a combi2 sequence was done as described previously [32] except that both GR dimer halves are retained in the resulting model . In short: A structural model of the DNA hybrid sequence ( AGAACATTCCGGCACT ) was generated using 3D-Dart [51] using the ETS1 structure ( PDB entry 1K79 ) and the GR structure ( PDB entry 3G6U ) . GR and the ETS2 binding motifs were aligned using the CE-align algorithm [52] to the 3D-DART DNA model of the hybrid sequence . Data were deposited in ArrayExpress under the accession numbers: E-MTAB-6738 ( RNA-seq U2OS-GR ) and E-MTAB-6737 ( synSTARR-seq U2OS-GR ) . In addition , we used the previously deposited datasets: E-MTAB-2731 ( ChIP-seq U2OS cells ) , E-MTAB-2955 and E-MTAB-2956 ( ChIP-seq and ChIP-exo data IMR90 ) .
|
The expression level of genes is controlled by transcription factors , which are proteins that bind to genomic response elements that contain their recognition DNA sequence . Importantly , genes are not simply turned on but need to be expressed at the right level . This is , at least in part , assured by the sequence composition of genomic response elements . Here , we studied how the recognition DNA sequence influences gene regulation by a transcription factor called the glucocorticoid receptor . Specifically , we developed a method to test the activity of variants in a highly parallelized setting where everything is kept identical except for the sequence of the binding site . The systematic analysis of tens of thousands of sequence variants facilitated the identification of a previously unknown sequence variant with high activity . Moreover , we report how sequence variation of the response element influences cell-to-cell variability in expression levels . Finally , we observe similar activity profiles for distinct sequence variants that share similar three-dimensional DNA shape characteristics arguing that the three-dimensional perception of DNA by the glucocorticoid receptor , modulates its activity towards individual target genes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"cdna",
"libraries",
"sequencing",
"techniques",
"dna",
"transcription",
"forms",
"of",
"dna",
"sequence",
"motif",
"analysis",
"molecular",
"biology",
"techniques",
"dna",
"dna",
"libraries",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"bioinformatics",
"gene",
"expression",
"complementary",
"dna",
"molecular",
"biology",
"nucleotide",
"sequencing",
"biochemistry",
"dna",
"sequence",
"analysis",
"nucleic",
"acids",
"database",
"and",
"informatics",
"methods",
"genetics",
"biology",
"and",
"life",
"sciences"
] |
2018
|
Synthetic STARR-seq reveals how DNA shape and sequence modulate transcriptional output and noise
|
Ticks successfully feed and transmit pathogens by injecting pharmacological compounds in saliva to thwart host defenses . We have previously used LC-MS/MS to identify proteins that are present in saliva of unfed Amblyomma americanum ticks that were exposed to different hosts . Here we show that A . americanum serine protease inhibitor ( serpin ) 27 ( AAS27 ) is an immunogenic saliva protein that is injected into the host within the first day of tick feeding and is an anti-inflammatory protein that might act by blocking plasmin and trypsin functions . Although AAS27 is injected into the host throughout tick feeding , qRT-PCR and western blotting analyses indicate that the respective transcript and protein are present in high amounts within the first 24 h of tick feeding . Biochemical screening of Pichia pastoris-expressed recombinant ( r ) AAS27 against mammalian proteases related to host defense shows it is an inhibitor of trypsin and plasmin , with stoichiometry of inhibition indices of 3 . 5 and 3 . 8 , respectively . Consistent with typical inhibitory serpins , rAAS27 formed heat- and SDS-stable irreversible complexes with both proteases . We further demonstrate that rAAS27 inhibits trypsin with ka of 6 . 46 ± 1 . 24 x 104 M-1 s-1 , comparable to serpins of other tick species . We show that native AAS27 is part of the repertoire of proteins responsible for the inhibitory activity against trypsin in crude tick saliva . AAS27 is likely utilized by the tick to evade the hosts inflammation defense since rAAS27 blocks both formalin and compound 48/80-induced inflammation in rats . Tick immune sera of rabbits that had acquired resistance against tick feeding following repeated infestations with A . americanum or Ixodes scapularis ticks reacts with rAAS27 . Of significant interest , antibody to rAAS27 blocks this serpin inhibitory functions . Taken together , we conclude that AAS27 is an anti-inflammatory protein secreted into the host during feeding and may represent a potential candidate for development of an anti-tick vaccine .
The lone star tick Amblyomma americanum is a hard tick species of medical and veterinary importance in the United States and Mexico [1–3] . This tick species is a known vector of a number of tick-borne diseases ( TBD ) agents including Borrelia lonestari , the causative agent of southern tick-associated rash illness ( STARI ) [4]; Francisella tularensis , the causative agent of tularemia in humans [5 , 6]; Ehrlichia chaffeensis and Ehrlichia ewingii , the causative agents of human ehrlichiosis [7 , 8]; and Rickettsia amblyommii , the causative agent of rickettsiosis of the spotted fever group [9 , 10] . A recent study demonstrated the vector capability of A . americanum in acquiring , maintaining and transmitting Rickettssia rickettsii isolates originating from two different geographical regions of the US [11] . Furthermore , the possible role of A . americanum to transmit Heartland and Bourbon viruses was documented [12 , 13] . Likewise , A . americanum is a competent vector of Cytauxzoon felis and Theileria cervi pathogens affecting domestic cats and white-tailed deer , respectively [2 , 14] . In absence of vaccines against major ticks and TBD agents , current tick control strategies rely mostly on the use of chemical acaricides , even though selection of resistant tick populations to most used acaricides has been confirmed [15 , 16] . This is recognized as a drawback to a successful tick control , and not to mention environment and food chain contamination hazards . Immunization of animals against tick feeding emerged as a sustainable tick control strategy [17 , 18] . In the effort to find effective targets for an anti-tick vaccine development , understanding tick-feeding physiology could lead to the discovery of important tick saliva proteins that can be targeted for anti-tick vaccine development . Ticks accomplish blood meal feeding by disrupting host tissue and sucking up blood from the feeding lesion . This feeding style triggers host defense responses including pain , hemostasis , inflammation , complement activation , and tissue repair responses [19] . Serine proteases mediate some of the host defense pathways to tick feeding and are controlled in some pathways by inhibitors belonging to the serine protease inhibitor ( serpin ) family [20] . From this perspective , ticks were thought to inject serpins into the host to mediate evasion of host defenses . The presence of serpins in tick saliva was well demonstrated though saliva proteomic studies [21–23] and recent evidence shows that some of the tick-encoded serpins are functional inhibitors of host defense system proteases [24 , 25] . We have also recently described A . americanum serpins that are expressed by both male and female A . americanum ticks [26] . In a recent study , we used LC-MS/MS to identify proteins in saliva of unfed A . americanum that were stimulated to start feeding on different hosts ( rabbits , dogs and humans ) [27] . The rationale to focus on early stages of tick feeding is that most TBD pathogens are transmitted within the first 72 h of tick feeding [28] , thus identifying tick saliva proteins that are important for initial tick feeding success could serve as ideal antigens targets in designing effective anti-tick vaccines against tick feeding and pathogen transmission . However a major limitation to discovery of effective anti-tick vaccines is the lack of understanding the functional roles of these molecular components in tick feeding . To begin understanding the functional roles to tick saliva serpins in tick feeding success , this study was undertaken to characterize A . americanum serpin 27 ( AAS27 ) , one of the serpins that is highly abundant in saliva of unfed adult female ticks that were stimulated to start feeding on different hosts ( rabbit , dog , and human ) [27] . Our data show that , native AAS27 is likely one of the tick saliva proteins that mediate the tick’s evasion of the host’s inflammatory defense to tick feeding .
A spatial and temporal transcription profile of AAS27 was used to determine its relationship to the tick-feeding process ( Fig 1A–1C ) . Results from qRT-PCR analysis show that AAS27 mRNA is transcribed in salivary glands ( SG ) , midguts ( MG ) , and carcass ( CA , tissue remnant after removal of SG and MG ) ( Fig 1A–1C ) . In all tissues analyzed transcript abundance was substantially higher at the unfed stage , and being reduced within 24 h of attachment . With reference to the 24 h feeding time point , there is an apparent transcript increase in SG at 96 h ( Fig 1A ) , and at 48 and 120 h in CA ( Fig 1C ) . Fig 1D–1G summarizes native AAS27 protein expression in different life stages and tick organs of unfed and fed adult female A . americanum . Native AAS27 is expressed in eggs at 22 days after eggs were laid , unfed larvae , nymphs , female and male adults ticks ( Fig 1D ) . Consistent with qRT-PCR data , native AAS27 protein is apparently highly abundant at the unfed stage in all analyszed tick organs: salivary glands ( SG ) , midgut ( MG ) , Malpighian tubules ( MT ) , synganglion ( SYN ) , ovary ( OV ) , carcass ( CA ) ( Fig 1E–1G ) . The mature AAS27 is 377 amino acids long with a 41 kDa calculated molecular mass [26] . Immunoblot analysis detected a single native AAS27 protein band within the expected size range of between 37 and 50 kDa . Pre-immune sera did not show binding ( S1 Fig ) demonstrating specificity of the reaction . Please also note in S1 Fig , we have provided images of full gels that have been cropped and presented in Fig 1D–1G . Given the fact that AAS27 protein is highly abundant in unfed ticks , we decided against conducting RNAi silencing of this protein to determine its significance in tick feeding . Based our previous work [29 , 30] , we reasoned that RNAi disruption of encoding mRNA might not sufficiently deplete AAS27 protein that was translated prior to RNAi silencing , and thus our results might not be informative . Our preliminary attempts to deplete mRNA from unfed ticks as described [29 , 30] before conducting RNAi silencing analyses were unsuccessful , and we did not proceed with RNAi silencing . AAS27 was successfully expressed using Pichia pastoris system ( S2 Fig ) . Approximately 17 mg of purified rAAS27 was obtained from culture supernatant ( 1 L ) . Purified rAAS27 migrates as a 50 kDa band on 12% SDS-PAGE under reducing conditions ( S2 Fig ) . When treated with glycosidases we could observe a downward molecular weight shift suggesting rAAS27 is expressed as a glycoprotein ( S2 Fig ) . Comparative modeling using neuroserpin tertiary structure as a template showed that AAS27 predicted tertiary structure retains the typical serpin fold ( S3 Fig ) . Surface electrostatic potential was calculated and showed the presence of putative basic patches on AAS27 model ( dashed circles in S3 Fig ) . There is evidence that basic patches on serpins could bind GAGs [31] . We used two different strategies to check if rAAS27 is able to interact with two classes of GAGs ( heparin/heparan sulfate and chondroitin/dermatan sulfate ) : ( i ) an affinity purification using heparin-Sepharose ( S4 Fig ) and ( ii ) GAG-binding plate assay ( S4 Fig ) . These results show that putative GAG-binding sites predicted on AAS27 model were not able to bind GAGs used in this study . S4 Fig shows that rAAS27 did not bind onto heparin , since rAAS27 was detected only in run-through ( lane 2 ) and in fractions eluted with washing buffer ( lane 3 ) . Similarly , no interaction of rAAS27 with heparin , heparan sulfate , dermatan sulfate , and chondroitin sulfate was observed using a GAG-binding plate assay ( S4 Fig ) . The inhibitory profile of rAAS27 was tested against 17 mammalian serine proteases related to host defense pathways as previously described [32 , 33] . Incubation of rAAS27 with each protease in a molar excess showed that rAAS27 ( 1 μM ) inhibits the activity of bovine pancreatic trypsin ( 0 . 3 nM ) by 99% , the activity of pancreatic bovine α-chymotrypsin ( 1 . 4 nM ) by 98% , the activity of human plasmin ( 5 . 3 nM ) by 94% , the activity of human factor XIa ( 3 . 7 nM ) by 80% , and the activity of rat trypsin IV ( 20 nM ) by 71% ( Fig 2 ) . To check the inhibitory efficiency , stoichiometry of inhibition ( SI ) index were calculated . Accordingly , the SI index for rAAS27 was 3 . 5 against bovine pancreatic trypsin ( Fig 3A ) , 3 . 8 against human plasmin ( Fig 3B ) , and higher than 10 against chymotrypsin ( Fig 3C ) , human factor XIa ( Fig 3D ) . The rAAS27 mechanism of action as a typical inhibitory serpin was confirmed by the inability of heat and SDS to dissociate it from bovine pancreatic trypsin ( Fig 4A ) and human plasmin ( Fig 4B ) . After incubation of trypsin or plasmin with rAAS27 , high molecular weight complexes were formed ( as indicated by arrows in Fig 4 ) and there was an apparent increase in consumption of protease with higher concentrations of rAAS27 . The formation of a covalent complex was observed on 12% SDS-PAGE ( Fig 4 ) as a band migrating at approximately the same position as the sum of the target protease and the cleaved rAAS27 ( approximately 75 kDa ) . These irreversible complexes between rAAS27 and the target proteases were observed at similar molar ratios comparable to SI assays ( Fig 3A and Fig 3B ) . The association rate constant ( ka ) of rAAS27 with pancreatic bovine trypsin was measured under pseudo-first order conditions using a discontinuous assay [34] . The ka for the interaction of rAAS27 and pancreatic bovine trypsin was determined as ka 6 . 46 ± 1 . 24 x 104 M-1 s-1 , demonstrating rAAS27 is a fast and effective inhibitor of trypsin ( Fig 5 ) . Given the fact that native AAS27 is present in tick saliva , we investigated anti-trypsin inhibitory activity in whole tick saliva . To prevent any contamination from host proteins , whole tick saliva was collected from unfed adult A . americanum female ticks ( Fig 6A ) . In Fig 6B we used the monospecific antibody to rAAS27 to confirm that native AAS27 was present in whole tick saliva as demonstrated by specific reactivity of the expected ~41 kDa protein band . Since we could identify native AAS27 in A . americanum tick saliva , we next investigated the effect of saliva on trypsin amidolytic activities . We show that tick saliva ( 1 μg total protein ) completely inhibited the activity of trypsin ( 2 nM ) ( Fig 6C ) . When tick saliva was co-incubated with trypsin , we could observe a covalent complex formation between native AAS27 and the protease ( Fig 6D ) . Altogether , these results showed that native AAS27 is present in tick saliva and has anti-trypsin activity . Repeated tick infestation of animals can induce protective host immunity that reduces tick feeding efficiency [35] . In order to determine if native AAS27 was among immunogens that provoke a humoral immune response in rabbits that were repeatedly infested with A . americanum , rAAS27 ( glycosylated and de-glycosylated ) was subjected to western blotting analysis using rabbit antibodies generated during experimental infestation with A . americanum and I . scapularis ticks ( Fig 7 ) . Similar to binding pattern of the monospecific antibody to rAAS27 ( positive control , Fig 7A ) , serum antibodies from rabbits repeatedly infested with adult A . americanum ( Fig 7B ) and I . scapularis ( Fig 7C ) as well as nymph I . scapularis ticks ( Fig 7D ) bound to rAAS27 , but not to pre-immune sera ( Fig 7E ) . It is notable that rabbit antibodies to I . scapularis adult tick saliva proteins weakly bound de-glycosylated rAAS27 ( Fig 7C ) . Taken together , this data demonstrates that rAAS27 is one of the multiple immunogens that collectively provokes protective humoral immune response against tick feeding in rabbits that are repeatedly infested by A . americanum or I . scapularis ticks . Given the fact that rAAS27 was immunogenic , we were curious to investigate if antibodies to rAAS27 affected its function . Of significant interest , monospecific antibodies to rAAS27 significantly neutralized the inhibitory function of rAAS27 against trypsin in a dose dependent manner ( Fig 8 ) . Purified IgG from pre-immune serum was used as control and did not affect serpin inhibitory activity ( Fig 8 ) . The finding that rAAS27 inhibits trypsin and plasmin suggests this serpin might attenuate inflammation and could contribute to the immunomodulatory activity of tick saliva [36–38] . We evaluated the effects of rAAS27 in a rat model of acute inflammation induced by formalin and compound 48/80 ( an agonist of mast cell degranulation ) . Injecting compound 48/80 into the skin degranulates mast cells as occurs during to tick-feeding [39–41] . With respect to normal physiological functions , mast cells are known to regulate vasodilation , vascular homeostasis , innate and adaptive immune responses , and angiogenesis [42] . The cytoplasm of the mast cell contains large granules that store inflammatory mediators , including histamine , heparin , a variety of cytokines , chondroitin sulfate , and neutral proteases , including chymase and tryptase [43] . In animal models , injection of compound 48/80 induces mast cell degranulation accompanied by thermal hyperalgesia , tissue edema , and neutrophil influx [44] . Similarly , injection of formalin into mouse paw releases locally several forms of active trypsin-like serine proteases . These proteases generate PAR-derived peptides and activates cells via PAR-2-dependent mechanism , resulting in an acute inflammatory response characterized by edema formation in the paw [36] . When inoculated into the rat footpad , formalin and compound 48/80 induces an acute inflammatory process characterized by edema formation . This effect was confirmed by the increase in paw thickness reaching a maximum 1 h post injection for formalin ( Fig 9A ) and 30 min post injection for compound 48/80 ( Fig 9B ) . In the presence of rAAS27 ( 25 μg/paw ) edema formation was significantly reduced . More specifically , in the formalin-induced paw edema assay the decrease in edema formation reached 38% ( p = 0 . 027 ) at 1 hour and 82% ( p = 0 . 027 ) at 3 hours post-treatments , compared to positive control ( formalin-injected footpads ) . The inhibition observed after 2 and 4 hours of formalin injection with rAAS27 was 51% and 78% , compared with positive control , but it was not statistically significant ( Fig 9A ) . Similarly , in the compound 48/80-induced paw edema assay , the decrease in edema formation reached 49% ( p = 0 . 014 ) at 30 min , 78% ( p = 0 . 009 ) at 60 min , and 71% ( p = 0 . 007 ) at 120 min post-injections ( Fig 9B ) . Intradermically injected formalin increases vascular permeability into the subcutaneous tissue and can be estimated using the Miles assay by measuring Evans blue dye fluid extravasation in rat skin [45] . Fig 10 shows that formalin significantly increased the vascular permeability into skin subcutaneous tissue ( Fig 10A ) , while rAAS27 ( 25 μg/spot ) prevented this stimulatory response almost completely ( p = 0 . 005 ) ( Fig 10B ) .
Ticks are blood feeding arthropods that salivate while they puncture host skin in their search for blood . Tick saliva contains hundreds of compounds that have anti-coagulant , vasodilatory , anti-inflammatory , and immunomodulatory functions [19 , 21 , 22] . Inflammatory host response induced by tick feeding is expected to create a hostile environment for foreign pathogens , but is alleviated by tick saliva [46] . The anti-inflammatory properties of tick saliva is already well-documented [47 , 48] , however the molecular identity of main anti-inflammatory molecules in A . americanum saliva is not fully known . In a previous study , we identified by LC-MS/MS over 300 tick saliva proteins that are present in unfed A . americanum ticks that were stimulated to feed on different hosts , of which AAS27 was the most abundant serpin identified in all treatments [27] . In this study , we demonstrate the functional characterization of AAS27 [26] , as an anti-inflammatory serpin that A . americanum ticks inject into the host during feeding . Our spatial-temporal expression analysis showed that both mRNA transcript and native protein of AAS27 is expressed in salivary glands , midgut , Malpighian tubes , synganglion , and carcass ( the remnants after removal of other organs ) . Of significance , both transcript and proteins of AAS27 are highly abundant at the unfed stage , suggesting this serpin is secreted into the host within 24 h of the tick starting to feed . The detection of AAS27 in internal organs suggest this serpin may also regulate other endogenous biological systems [25] . In addition to confirming that native AAS27 is present in tick saliva , consistent with our previous proteomic study [27] , here we show that antibodies raised against rAAS27 are able to recognize the native serpin that is present in tick saliva ( Fig 6 ) . Additionally , immune serum from rabbits that were repeatedly infested with A . americanum ticks recognize rAAS27 , demonstrating this protein is injected into hosts during tick feeding and is potentially one of the multiple immunogens that collectively provoke protective immunity against tick feeding in rabbits that are repeatedly infested by A . americanum ticks ( Fig 7 ) . Data showing cross-reactivity among sera from hosts infested with different tick species suggest that AAS27 homologs may be present in saliva of other ticks species , highlighting the potential use of salivary serpins in a universal anti-tick vaccine [49] to interfere with normal tick feeding and against subsequent pathogen transmission . Consistent with previous findings that some serpins are glycoproteins [50] , this study shows that rAAS27 is expressed in Pichia pastoris as a glycoprotein ( S2 Fig ) . Tertiary serpin structure typically contains three β-sheets , eight or nine α-helices , and a reactive center loop ( RCL ) [50] . The RCL is a solvent exposed flexible stretch of 21 amino acid residues positioned between β-sheets sA and sC and acts as a bait for its cognate protease [50] . Comparative modeling using human neuroserpin as a template showed that the rAAS27 predicted tertiary structure retains a typical serpin fold ( S3 Fig ) . The RCL sequences displays hypervariability due to lack of structural constraints , and the P1 residue in the RCL is critical to define the specificity of a serpin for a particular protease . The predicted P1 and P1`residues of AAS27 are Arg-Ile [26] which corresponds to typical cleavage sites for trypsin-like proteases . This prediction is in accordance with the inhibitory profile described here: rAAS27 is a trypsin and plasmin inhibitor , displaying stoichiometry of inhibition ( SI ) indices of 3 . 5 and 3 . 8 , respectively . AAS27 interacts with proteases via a classical “suicide inhibition” mechanism of serpins which involves its cleavage of RCL and formation of S4 β-strand that is inserted in the middle of β-sheet A [51] . We observed the formation of irreversible complexes between rAAS27 with these proteases ( Fig 4 ) , which is consistent with typical mechanism of inhibitory serpins . Furthermore , AAS27 inhibited trypsin with ka of 6 . 46 ± 1 . 24 x 104 M-1 s-1 , which is comparable to other tick serpins . A ka of 9 . 3 × 104 M−1 s−1 was described for the cattle tick serpin RmS-15 and thrombin [52] . Prevot et al . , ( 2006 ) [53] described second order constants for the Ixodes ricinus serpin Iris for neutrophil elastase ( 4 . 7 x 106 M-1 s-1 ) , porcine pancreatic elastase ( 2 . 2 x 105 M-1 s-1 ) , t-PA ( 2 . 9 x 105 M-1 s-1 ) , factor Xa ( 1 . 7 x 105 M-1 s-1 ) , thrombin ( 2 . 5 x 104 M-1 s-1 ) and trypsin ( 1 . 5 x 104 M-1 s-1 ) . Using higher rAAS27:protease ratios , rAAS27 also inhibits chymotrypsin , factor XIa , and rat trypsin IV ( Fig 2 ) , albeit with very high SI indices ( Fig 3 ) . The high SI index could be explained by the possibility that AAS27 requires a co-factor to enhance inhibition . It is well documented GAGs act as co-factors in order to accelerate the inhibition rate of serpins [31] . Our findings that AAS27 does not bind two of the four main classes of GAGs suggests this is not the case for this tick serpin . A typical inhibitory serpin forms a covalent complex with its cognate protease which is resistant to SDS and thermal denaturation , has a stoichiometry of inhibition ( SI ) close to 1 and a ka ≥ 105 M-1 s-1 [50] . Although desired , these features ( SI close to 1 and ka ≥ 105 M-1 s-1 ) are not always observed , even for well characterized mammalian endogenous serpins controlling important physiological proteases in mammals . In this very important review about serpins , Gettins ( 2002 ) [50] describes several serpins with important physiological functions in mammals with ka ≤ 105 M-1 s-1 . Similarly , according to the nature of serpin inhibition mechanism , the balance between substrate and inhibition reactions leads to the useful concept of a “stoichiometry of inhibition—SI” , which is defined as the ratio of mols of serpin needed to inhibit one mol of protease . However , for a given serpin/protease reaction , there are other factors that can also influence the relative rates of reactive center loop insertion and substrate cleavage and hence affect SI . These include time of incubation , temperature , pH , ionic strength , presence of known and unknown cofactors that differentially affects reaction [50] . We performed all kinetics at 30°C , following recommendations from IUBMB for enzymatic kinetics . We understand that performing the same experiment at 37°C could impact on SI and ka values observed . In the same way , longer incubation time would impact SI obtained here . For some serpins described in literature with SI close to 1 , incubation of serpin and protease were incubated for 2 and even 4 hours [34] . Taken together , we conclude that AAS27 has a physiological relevance for the inhibition of proteases described here , where we demonstrate that rAAS27 is an inhibitor of trypsin and plasmin . Plasmin inhibition by a tick salivary protein can , at a glance , be viewed as contradictory since plasmin is mostly known for its role in fibrinolysis , an activity that seems beneficial to tick feeding . However , plasmin has also been reported to participate in several processes such as pro-inflammatory cytokine release [38] , inducing monocyte and dendritic cell chemotaxis [54] , modifying IL-8 and producing a potent attractant of neutrophils [55] , tissue remodeling and wound healing [56] , all of which can negatively impact tick-feeding success . Thus , inhibition of plasmin by AAS27 seems to contribute to feeding on blood and can be claimed as an important target regarding tick feeding physiology . Similarly , although trypsin is produced predominantly by the pancreas as a means to degrade dietary proteins , trypsin-like proteases are also expressed in the nervous system and in epithelial tissues , where they are the most powerful activators of protease-activated receptor 2 ( PAR-2 ) hence are important factors in neurogenic inflammation and pain in the skin [36] . There is evidence that injection of formalin into mouse paw release locally several forms of active trypsin-like serine proteases . These proteases generate PAR-derived peptides and activates cells via PAR-2-dependent mechanism , resulting in an acute inflammatory response characterized by edema formation in the paw [36 , 37] . Formalin induces a transient paw edema formation in rats or mice through trypsin IV-induced PAR-2 activation and is inhibited by administration of serine protease inhibitors [36 , 37] . Although rAAS27 is not an efficient inhibitor of rat trypsin IV , we demonstrate that it inhibits formalin-induced acute inflammation in two different models , the paw edema and vascular permeability assays ( Fig 9 and Fig 10 ) . Thus , tick injection of AAS27 into the feeding site could interfere with serine protease-derived pro-inflammatory and algesic responses in the skin during tick feeding . The observation that a monospecific antibody against rAAS27 abolished its inhibitory activity ( Fig 8 ) is significant because it suggests the potential to design anti-tick vaccines that can neutralize functions of tick serpins at the feeding site . Host functional antibodies raised during immunization could block serpin inhibitory activity during tick feeding and interfere with modulatory functions of serpins , impairing blood meal acquisition , tick development , and pathogen transmission [57] . Inducing an efficacious immune response against tick serpins could help hosts to limit and control parasite infections and , therefore , serpins may be included as suitable antigen candidates in an anti-tick vaccine . In summary , this study reports that A . americanum ticks secrete an anti-inflammatory serpin into host during feeding . We would like to caution the reader about limitations of data reported here: the amount of native AAS27 that A . americanum ticks inject into the host is unknown . Interestingly , we have recently shown that AAS27 is among the top proteins that are secreted into tick saliva when A . americanum ticks are stimulated to feed on rabbits or humans or dogs ( 27 ) , which demonstrates that this protein is among those that regulate early tick feeding events . This work emphasizes the importance of understanding the functional roles of tick saliva proteins as a means to identify new targets for the development of novel strategies to control ticks and tick-borne diseases . Work to validate the potential of AAS27 as a target antigen for anti-tick vaccine development is currently being pursued .
All animal work was conducted and approved according to Texas A&M University Institutional Animal Care and Use Committee ( AUP 2011–0207 ) and by the Ethical Committee on Research Animal Care of the Universidade Federal do Rio Grande do Sul , Brazil ( register number 28371/2015 ) . All the procedures involving animals were carried out in accordance with the U . S . Government Principles for the Utilization and Care of Vertebrate Animals Used in Testing , Research and Training; and by the Brazilian Guide for the Care and Use of Animals for Scientific and Didactic Purposes ( DBCA-CONCEA ) . AAS27-encoding mature protein open reading frame [26] was cloned in frame with α-factor into pPICZαC in ClaI and SacII sites using forward ( 5’- TTTTTTTATCGATGCTGACAGAGAAGGAGCAGAAGCTCATC-3’ ) and reverse ( 5’AAAAAACCGCGGTCAGTGGTGGTGGTGGTGGTGGAGCTTGTTGACCTGTCCAGCAAAG-3’ ) primers with added restriction enzymes sites ( in bold ) and a hexa histidine tag ( underlined ) . Recombinant ( r ) AAS27 expression was performed as previously described [32 , 58] . Expression of rAAS27 was confirmed by resolving samples on a 12% SDS-PAGE for western blot analysis using anti-C-terminus hexa histidine tag HRP conjugated at 1:5 , 000 antibody dilution ( Life Technologies , Carlsbad , CA , USA ) and positive signal was detected using a metal enhanced DAB chromogenic substrate kit ( Thermo Scientific , Waltham , MA , USA ) . The rAAS27 was affinity-purified under native conditions using Hi-Trap Chelating HP Columns ( GE Healthcare Bio-Sciences , Pittsburgh , PA , USA ) . To evaluate purity , affinity-purified rAAS27 was resolved on a 12% SDS-PAGE and stained with Coomassie brilliant blue . Affinity-purified rAAS27 was dialyzed against 20 mM Tris-HCl , NaCl 150 mM buffer pH 7 . 4 , protein concentration determined by BCA ( Thermo Scientific , Waltham , MA , USA ) , and purified protein stored at -80°C upon use . To determine if rAAS27 was N- and/or O-glycosylated , 5 μg of affinity-purified rAAS27 was treated with deglycosylation enzyme mix according to manufacturer’s instructions ( New England Biolabs , Ipswich , MA , USA ) . Deglycosylation was verified resolving proteins on a 12% SDS-PAGE followed by western blotting analysis using an antibody directed to C-terminus hexa histidine-tag ( Life Technologies , Carlsbad , CA , USA ) at 1:5 , 000 dilution and positive signal detected using HRP chromogenic substrate ( Thermo Scientific , Waltham , MA , USA ) . Anti-serum against rAAS27 was raised in rabbits by subcutaneously inoculating with 100 μg of rAAS27 emulsified in equal volume of TiterMax Gold adjuvant ( Sigma , St . Louis , MO , USA ) . Following the first inoculation , two 50-μg boosters of rAAS27 with adjuvant were applied at 15-day intervals . Monospecific antibodies were purified using the Sepharose 4B CNBr-activated resin following manufacturer’s instructions ( Sigma-Aldrich , St . Louis , MO , USA ) . First , 2 . 3 mg of rAAS27 were coupled to 0 . 3 g of resin . Subsequently , rabbit anti-rAAS27 immune serum dialyzed in phosphate-buffered saline , pH 7 . 4 , ( PBS ) was added to rAAS27-Sepharose 4B coupled resin with end-over-end rocking at room temperature for 2 hours . The resin was washed with 30 mL of PBS and the bound monospecific anti-rAAS27 was eluted with ten 1 mL aliquots of 100 mM glycine-HCl , pH 2 . 4 , and neutralized with 50 μL of 2 M Tris base . Eluted antibody fractions were dialyzed against PBS ( pH 7 . 4 ) and were analyzed by SDS-PAGE for Coomassie blue staining and immunoblot analysis . The three-dimensional ( 3D ) structure of AAS27 was predicted using a comparative modeling approach . The native human neuroserpin structure ( 3F5N ) [59] was retrieved from the Protein Data Bank ( PDB ) ( http://www . rcsb . org ) and used as a molecular template for AAS27 modeling based on 33% and 56% sequence identity and similarity , respectively . Sequence alignments were generated using the ClustalW algorithm [60] and used as input in the Modeller 9v19 program [61] . Models generated were evaluated using QMEAN4 and PROCHECK to estimate model reliability and predict quality [62 , 63] . The electrostatic potential of AAS27 model was calculated using the Adaptive Poisson–Boltzmann Solver ( APBS ) , while protonation states were assigned using the parameters for solvation energy ( PARSE ) force field for each structure by PDB2PQR [64] . Execution of APBS and visualization of resulting electrostatic potentials were performed by using the Visual Molecular Dynamics ( VMD ) program [65] at ±5 kT/e of positive and negative contour fields . Two strategies were used to determine if putative glycosaminoglycan ( GAG ) -binding sites presents in AAS27 were functional . Firstly , to check if rAAS27 was able to bind heparin , 200 μg of affinity-purified rAAS27 was bound and eluted on a 1 mL HiTrap Heparin HP Column following manufacturer’s instructions ( GE Healthcare Bio-Sciences , Uppsala , Sweden ) . Column was equilibrated using 10 mM sodium phosphate , pH 7 . 4 as binding buffer . Elution was performed using a step-wise elution gradient of NaCl ( 0–2 M ) . Protein content of each chromatographic fraction was monitored at 280 nm and analyzed by 12% SDS-PAGE following silver staining [66] . In addition to heparin-Sepharose chromatography , a microtiter plate-based assay was performed using non-fractioned heparin , heparan sulfate , dermatan sulfate , and chondroitin sulfate . In order to prepare GAG-coated plate surfaces , 200 μL ( per well ) of a 25 μg/mL GAG solution in binding buffer ( 100 mM NaCl , 50 mM Na-acetate , 0 . 2% Tween , pH 7 . 2 ) was added to GAG-binding plate wells ( Galen Laboratories Supplies , North Haven , CT , USA ) . Plates were incubated overnight at room temperature . Next day , plates were washed with binding buffer following blocking with 250 μL 1% bovine serum albumin solution ( in PBS , pH 7 . 4 ) , following using binding buffer . rAAS27 was dissolved in binding buffer in different concentrations ( 0 , 1 , 2 , 5 , 10 and 20 μg/mL ) and incubated by two hours at 37°C . Plates were washed with binding buffer following incubation with 200 μL anti-C terminal hexa histidine tag antibody ( 1:5 , 000 dilution ) in blocking solution for 1 hour at room temperature . Following appropriate washes with binding buffer , wells were incubated with 1-Step Ultra TMB ELISA Substrate ( Thermo Scientific , Waltham , MA , USA ) for 10 min at room temperature following addition of 2 M sulfuric acid . The OD450nm was determined using the Infinite M200 Pro plate reader ( Tecan , Männedorf , Switzerland ) . A . americanum ticks were purchased from the tick laboratory at Oklahoma State University ( Stillwater , OK , USA ) . Feeding was performed as previously described [33] . Five female ticks were manually detached every 24 h for 5 days ( 24–120 h ) . Within the first hour of detachment , tick mouthparts were inspected to remove remnant tissue and washed in diethylpyrocarbonate ( DEPC ) -treated water to prepare for dissection . Dissected tick organs , salivary glands ( SG ) , midgut ( MG ) , and carcass ( CA , the remnants after removal of other organs ) were placed in 1 mL of Trizol reagent ( Life Technologies , Carlsbad , CA , USA ) and RNA was extracted according to the manufacturer’s instructions . Total RNA was re-suspended in DEPC-treated water and quantified spectrophotometrically using the Infinite M200 Pro plate reader ( Tecan , Männedorf , Switzerland ) . Up to 1 μg of total RNA was used to synthesize cDNA using the Verso cDNA Synthesis Kit following the manufacturer’s instructions ( Thermo Scientific , Waltham , MA , USA ) and stored at -80°C upon use . To prepare tick protein extracts , five ticks from unfed and manually detached at 24 , 72 and 120 h post tick attachment were rinsed in sterile PBS , pH 7 . 4 and processed for dissections . Tick organs including salivary gland ( SG ) , midgut ( MG ) , synganglion ( SYN ) , Malpighian tubule ( MT ) , ovary ( OV ) and the remnants labeled as carcass ( CA ) were isolated and placed into IP lysis buffer with protease inhibitor cocktail ( Thermo Scientific , Waltham , MA , USA ) . Protein extracts were homogenized and stored in −80°C . Total proteins were extracted from all life stages of A . americanum consisting of eggs 22 days post- oviposition ( 50 mg ) , and unfed whole ticks of larvae ( n = 100 ) , nymphs ( n = 15 ) and both female and male adults ( n = 5 each ) . Prior to extraction , each life stage was washed in 1% bleach , Milli-Q water , 70% ethanol , and Milli-Q water . Egg proteins were extracted by flash freezing in liquid nitrogen and crushing them using a sterile plastic pestle in extraction buffer ( 1x PBS , 1mM PMSF , 1mM E-64 , 10mM EDTA , pH 7 . 4 ) . To extract proteins from other life stages , cleaned ticks were placed into a sterile 1 . 5 mL tube and finely chopped using a sharp pair of shears in extraction buffer . All protein extracts were sonicated on ice at 50% amplitude , centrifuged at 10 , 000 x g for 10 min at 4°C , and the supernantant was separated and stored in -80°C . The proteins in supernatant fractions were quantified using BCA assay ( Thermo Scientific , Waltham , MA , USA ) . Transcription analysis was done by two-step quantitative RT-PCR ( qRT-PCR ) using Applied Biosystems 7300 Real Time PCR System ( Life Technologies , Carlsbad , CA , USA ) as previously described [67] . AAS27 specific qRT-PCR primers ( For: 5′- CTGCCTCTGGAGTGGTCGGC-3′ and Rev: 5′-GAAAAGCTCCTGCGTACTA-3′ ) were used to determine transcript abundance in triplicate pools of cDNA ( described above ) . Cycling conditions were the following: stage one at 50°C for 2 min , stage two at 95°C for 10 min , and stage three contained two steps with 40 cycles of 95°C for 15s and 60°C for 1 min . Reaction volumes in triplicate contained ten-fold diluted cDNAs that was originally synthesized from 1 μg total RNA , 350 nM of forward and reverse AAS27 primers , and 1X SYBR Green Master Mix ( Life Technologies , Carlsbad , CA , USA ) . For internal reference control , a forward ( 5′-GGCGCCGAGGTGAAGAA-3′ ) and reverse ( 5′-CCTTGCCGTCCACCTTGAT-3′ ) primers targeting of 40S ribosomal protein S4 ( RPS4; accession number GAGD01011247 . 1 ) which is stably expressed in Ixodes scapularis during feeding [68] was used . Relative quantification ( RQ ) of AAS27 transcript was determined using the delta delta Ct method ( 2-ΔΔCt ) [69] . The lowest expressed time point was used as a calibrator for each tissue . To relate native AAS27 protein to tick development and expression in different tissues during feeding , total protein extracts of eggs ( at 22 days post oviposition ) , unfed larvae , nymph , and adult male and female ticks as wells as dissected tick organs ( as described above ) were subjected to routine western blotting analyses using the monospecific antibody to rAAS27 . Samples were loaded and resolved on a 12% SDS-PAGE and transferred onto a PVDF membrane . Membranes were incubated with a monospecific antibody produced against yeast-expressed rAAS27 ( 0 . 30 μg/μL in a 1:500 dilution ) for 1 hour at room temperature . Purified total IgG from pre-immune serum ( 0 . 66 μg/μL in a 1:250 dilution ) was used as control . After washes , membranes were incubated with secondary Clean-Blot IP Detection Reagent HRP conjugated ( 1:500 dilution ) ( Thermo Scientific , Waltham , MA , USA ) . After washes , membranes were incubated with GE Healthcare Amersha ECL Prime Western Blotting Detection Reagent for 5 minutes at room temperature and visualized using ChemiDoc XRS+ imager ( Biorad , Hercules , CA , USA ) . To determine if native AAS27 is injected into the host during feeding , glycosylated and deglycosylated affinity-purified rAAS27 ( 500 ng ) was subjected to routine western blotting analyses using antibodies to replete-fed A . americanum tick saliva proteins ( 1:50 and 1:250 dilution ) . Antibodies to replete-fed tick saliva proteins used here were produced as previously described [70] . To analyze if rAAS27 cross-react with serum from animals infested with other ticks species , rAAS27 ( 500 ng both glycosylated as well as deglycosylated protein ) were subjected to western blotting analysis using rabbit antibodies generated to replete-fed adult and nymph I . scapularis ( 1:50 dilution ) tick saliva proteins . Antibodies to tick-saliva proteins from replete-fed I . scapularis were produced as previously published [71] . Monospecific antibody anti-rAAS27 ( 0 . 33 μg/μL , 1:2 , 000 dilution ) and rabbit pre-immune serum ( 1:50 dilution ) were used as controls . After washes , membranes were incubated with secondary Clean-Blot IP Detection Reagent HRP conjugated ( 1:500 dilution ) ( Thermo Scientific , Waltham , MA , USA ) . Membranes were incubated with GE Healthcare Amersham ECL Prime Western Blotting Detection Reagent for 5 minutes at room temperature and visualized using ChemiDoc XRS+ imager ( Biorad , Hercules , CA , USA ) . Inhibitory activity of rAAS27 was tested against a panel of 17 mammalian serine proteases related to host defense pathways against tick feeding . Mammalian proteases ( per reaction ) tested were: pancreatic bovine α-chymotrypsin ( 1 . 4 nM ) , pancreatic porcine elastase ( 61 . 8 nM ) , human neutrophil proteinase-3 ( 280 nM ) , human chymase ( 21 . 7 nM ) , pancreatic bovine trypsin ( 0 . 3 nM ) , pancreatic porcine kallikrein ( 10 nM ) ( Sigma-Aldrich , St . Louis , MO , USA ) , human neutrophil cathepsin G ( 425 . 5 nM ) ( Athens Research & Technology ) , human plasmin ( 5 . 3 nM ) , human factor XIa ( 3 . 7 nM ) , bovine factor IXa ( 314 . 4 nM ) , human factor XIIa ( 15 nM ) , human thrombin ( 19 . 2 nM ) ( Enzyme Research Laboratories ) , human neutrophil elastase ( 14 . 9 nM ) , human t-PA ( 23 . 6 nM ) , human u-PA ( 29 . 6 nM ) ( Molecular Innovations , Inc . , Novi , MI , USA ) , human factor Xa ( 10 nM ) ( New England Biolabs ) , and rat trypsin IV ( 20 nM ) ( Tirloni et al , in preparation ) . Substrates were used at 0 . 20 mM final concentration , including N-succinyl-Ala-Ala-Pro-Phe-pNA for chymase , cathepsin G and chymotrypsin , N-benzoyl-Phe-Val-Arg-pNA for thrombin , trypsin , and trypsin IV , and N-succinyl-Ala-Ala-Ala-pNA for pancreatic elastase ( Sigma-Aldrich , St . Louis , MO , USA ) . The substrate D-Pro-Phe-Arg-pNa was used for factor XIa , factor XIIa , and kallikrein , and substrate Bz-Ile-Glu ( γ-OCH3 ) -Gly-Arg-pNA for factor Xa ( Aniara Diagnostica , West Chester , OH , USA ) . Substrate H-D-Val-Leu-Lys-pNA was used for plasmin ( Chromogenix , Philadelphia , PA , USA ) . The substrate CH3SO2-D-CHG-Gly-Arg-pNA was used for factor IXa , u-PA and t-PA ( Molecular Innovations , Inc . , Novi , MI , USA ) . The substrate N-methoxysuccinyl-Ala-Ala-Pro-Val-pNA was used for neutrophil elastase and proteinase-3 ( Enzo Life Sciences , Farmingdale , NY , EUA ) . Reagents were mixed at room temperature in technical triplicates . One micromolar ( 1 μM ) of rAAS27 was pre-incubated with indicated amounts of the protease for 15 minutes at 37°C in 20 mM Tris-HCl , 150 mM NaCl , BSA 0 . 1% , pH 7 . 4 buffer . The corresponding substrate for each protease was added in a 100 μL final reaction volume and substrate hydrolysis was measured at OD405nm every 15s for 15 min at 30°C using the Synergy H1 microplate reader ( Biotek , Winooski , Vermont , EUA ) . The percent enzyme activity inhibition level was determined as previously described [32 , 58] . Data are presented as mean ± standard deviation of three independent replicate readings . We determined stoichiometry of inhibition ( SI ) indices against proteases that were inhibited by more than 80% in the PI profiling assay described above . Different molar ratios ( serpin:protease ) of rAAS27 were pre-incubated for 1 hour at 37°C with constant concentration of trypsin ( 0 . 2 nM ) , chymotrypsin ( 1 . 4 nM ) , factor XIa ( 3 . 7 nM ) , and plasmin ( 5 . 3 nM ) . The residual protease activity was measured using colorimetric substrates specific for each enzyme ( as described above ) . The data were plotted as the residual activity ( Vi/V0 ) versus the inhibitor to enzyme molar ratio . SI or the molar ration of rAAS27 to protease was determined by fitting data onto the linear regression [34] . At varying molar ratios , affinity purified rAAS27 was incubated with trypsin ( 0 . 1 μg ) and plasmin ( 0 . 2 μg ) in 20 mM Tris-HCl , 150 mM , NaCl , pH 7 . 4 buffer for 1 hour at 37°C . Denaturing sample buffer was added to the reaction mix , and incubated at 95°C for 5 min in thermocycler . Samples were subjected to 12% SDS-PAGE following silver staining [66] . A discontinuous method was used to determine the rate of inhibition ( ka ) of trypsin by rAAS27 [34] . The pseudo-first order rate constant with trypsin ( 1 . 5 nM ) and rAAS27 ( 2 . 5–25 nM ) was determined by incubation for different periods of time ( 0–15 min ) followed by measurement of residual protease activity . The pseudo-first order constant , kobs , was determined from the slope of a semi-log plot of the residual protease activity against time . The kobs values were then plotted against serpin concentration and the slope of the line of best fit gave an estimate of the second-order rate constant ka . Collection of unfed adult females A . americanum tick saliva was performed as previously described [27] . To validate presence of native AAS27 in tick saliva , monospecific antibody produced against yeast-expressed rAAS27 ( 1 . 0 μg/μL in a 1:500 dilution ) and a purified total IgG from pre-immune serum ( 0 . 50 μg/μL in a 1:250 dilution ) were used to screen western blots of pilocarpine-induced tick saliva ( 1 μg total protein ) . Secondary Clean-Blot IP Detection Reagent HRP conjugated was used at 1:500 dilution ( Thermo Scientific , Waltham , MA , USA ) . Membranes were developed with GE Healthcare Amersham ECL Prime Western Blotting Detection Reagent for 5 minutes at room temperature and visualized using ChemiDoc XRS+ imager ( Biorad , Hercules , CA , USA ) . To test trypsin inhibitory activity in crude saliva , trypsin ( 2 nM ) was incubated with of A . americanum saliva ( 1 μg ) in 20 mM Tris–HCl , 150 mM NaCl , Tween 0 . 01% , pH 7 . 4 , for 15 min at 37°C . Residual protease activity was measured by the addition of chromogenic substrate N-benzoyl-Phe-Val-Arg-pNA ( 0 . 2 mM final concentration ) in a 100 μL final reaction volume and substrate hydrolysis was measured at OD405nm every 15s for 15 min at 30°C using the Synergy H1 microplate reader ( Biotek , Winooski , Vermont , EUA ) . Data are presented as mean ± standard deviation of three independent replicate readings . To analyze if tick saliva native AAS27 was able to form covalent complex with trypsin , tick saliva ( 1 μg total protein ) was incubated with trypsin ( 0 . 1 μg ) in 20 mM Tris-HCl , 150 mM , NaCl , pH 7 . 4 buffer for 1 hour at 37°C . Denaturing sample buffer was added to the reaction mix , and incubated at 95°C for 5 min in thermocycler . Samples were subjected to 12% SDS-PAGE following western blot analysis using a monospecific antibody against rAAS27 ( 0 . 30 μg/μL in a 1:500 dilution ) for 1 hour at room temperature . After washes , membranes were incubated with secondary Clean-Blot IP Detection Reagent HRP conjugated ( 1:500 dilution ) ( Thermo Scientific , Waltham , MA , USA ) and membranes developed as described above . Purified rabbit monospecific IgG to rAAS27 was used to check if antibody-binding to serpin could block their protease inhibitory activity . rAAS27 ( 68 nM ) was pre-incubated with monospecific rabbit IgG anti-rAAS27 ( varying from 0 . 125 to 2 μM ) or purified rabbit IgG from pre-immune sera ( 2 μM ) for 30 min at 37°C before the addition of trypsin ( 2 nM ) , following a new 15 min incubation at 37°C . Substrate N-benzoyl-Phe-Val-Arg-pNA was added to a 100 μL final reaction volume ( final concentration 0 . 2 mM ) and substrate hydrolysis was measured at OD405nm every 15 s for 15 min at 30°C using the Synergy H1 microplate reader ( Biotek , Winooski , Vermont , EUA ) . The percent enzyme activity inhibition level was determined as previously described [32 , 58] . Data are representative of three independent replicate readings . Adult male Wistar rats were supplied by the Central Animal Facility ( CREAL ) , Universidade Federal do Rio Grande do Sul , UFRGS , Brazil . They were housed in plastic cages ( 4 animals per cage ) within a temperature controlled room ( 22–23°C , on a 12 h light/dark cycle ) and had free access to water and food . For paw edema assay , formalin- and compound 48/80-induced rat paw edema models were used to investigate the potential anti-inflammatory role of rAAS27 . Paw volume was measured using a digital pletismometer ( Insight , Ribeirão Preto , SP , Brazil ) . Before paw volume measurement , the paw was marked at ankle in order to immerge always at the same extent into the pletismometer . Subsequently , 50 μL of formalin ( 3% in saline ) or 50 μL of compound 48/80 ( 1 μg total in saline ) was administered by intraplantar injection in the right paw in the absence or presence of endotoxin-free rAAS27 ( 25 μg per paw in saline ) . The left paw was used as control and received the same volume of saline . As a control , each group of rats received the same volume of saline ( vehicle ) in the presence of serpin only ( 25 μg per paw ) . As an index of edema formation , paw volume ( in millimeters ) was measured at 0 , 30 , 60 , 120 , 180 and 240 min ( for formalin-induced ) and 0 , 30 , 60 and 120 min ( for compound 48/80 ) . The assessment of paw volume was performed always three times by the same operator . The increase in paw volume was calculated by subtracting average volume of the right paw by the average volume of the left paw at each time point . For vascular permeability assay [45] , male Wistar rats ( weighting between 250–400 g ) were anesthetized with intraperitoneal injection of xylazin ( 10 mg/kg ) and ketamine ( 75 mg/kg ) . Under anesthesia , rats were injected intravenously ( tail vein ) with 700 μL of Evans blue dye ( 50 mg/kg in saline ) . After 5 minutes , animals were injected intradermally on the dorsal region ( 100 μL final volume ) with: ( i ) saline , ( ii ) formalin 2% in saline , ( iii ) formalin 2% + 25 μg of rAAS27 in saline , and ( iv ) 25 μg of rAAS27 in saline . Two spots of each treatment were performed per animal ( n = 6; 12 spots per treatment ) . After 60 minutes , animals were euthanized and an area of skin that included the entire injection sites was carefully removed and photographed . Evans blue dye spots on skin were excised and the dye was extracted incubating skin with 2 . 5 mL of 50% formamide for 24 hours at 55°C . After centrifugation at 4 , 000 rpm for 10 minutes , absorbance of the supernatant was measured at 620 nm . Unpaired t-test was used for statics analysis and p ≤0 . 05 was considered statistically significant .
|
Ticks are blood-feeding arthropods that salivate while they puncture host skin in their search of blood . Tick saliva contains hundreds of compounds that have anti-coagulant , vasodilatory , anti-inflammatory , and immunomodulatory functions . While helping the vector to feed , tick saliva also modifies the site where pathogens are injected and in many cases facilitates the infection process . For this reason , tick salivary proteins can be targets to control tick and tick-borne diseases . Serpins are thought to control the tick’s evasion of the host’s serine protease-mediated defense pathways such as inflammation and blood coagulation . In this study , we report that Amblyomma americanum ticks secrete an anti-inflammatory serpin into the host during feeding . This work emphasizes the importance of understanding the functional roles of tick saliva proteins to tick feeding physiology to identify new targets in development of novel strategies for tick and tick-borne diseases control and also to search and find new potentially pharmacological active compounds .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"ixodes",
"pathology",
"and",
"laboratory",
"medicine",
"enzymes",
"immunology",
"enzymology",
"saliva",
"animals",
"serine",
"proteases",
"signs",
"and",
"symptoms",
"ticks",
"edema",
"plasmins",
"extraction",
"techniques",
"research",
"and",
"analysis",
"methods",
"infectious",
"diseases",
"inflammation",
"protein",
"extraction",
"proteins",
"disease",
"vectors",
"immune",
"response",
"arthropoda",
"biochemistry",
"arachnida",
"eukaryota",
"diagnostic",
"medicine",
"anatomy",
"physiology",
"biology",
"and",
"life",
"sciences",
"proteases",
"species",
"interactions",
"organisms"
] |
2019
|
Amblyomma americanum serpin 27 (AAS27) is a tick salivary anti-inflammatory protein secreted into the host during feeding
|
Recent work in motor control demonstrates that humans take their own motor uncertainty into account , adjusting the timing and goals of movement so as to maximize expected gain . Visual sensitivity varies dramatically with retinal location and target , and models of optimal visual search typically assume that the visual system takes retinal inhomogeneity into account in planning eye movements . Such models can then use the entire retina rather than just the fovea to speed search . Using a simple decision task , we evaluated human ability to compensate for retinal inhomogeneity . We first measured observers' sensitivity for targets , varying contrast and eccentricity . Observers then repeatedly chose between targets differing in eccentricity and contrast , selecting the one they would prefer to attempt: e . g . , a low contrast target at 2° versus a high contrast target at 10° . Observers knew they would later attempt some of their chosen targets and receive rewards for correct classifications . We evaluated performance in three ways . Equivalence: Do observers' judgments agree with their actual performance ? Do they correctly trade off eccentricity and contrast and select the more discriminable target in each pair ? Transitivity: Are observers' choices self-consistent ? Dominance: Do observers understand that increased contrast improves performance ? Decreased eccentricity ? All observers exhibited patterned failures of equivalence , and seven out of eight observers failed transitivity . There were significant but small failures of dominance . All these failures together reduced their winnings by 10%–18% .
An average human eye has as many as 4 . 6 million cones in a retinal area of 1019 mm2 [1] centered on the fovea but the distribution of cones across the retina is far from uniform ( Figure 1A ) . As a consequence of retinal inhomogeneity and post-receptoral processing [2] , observer's performance in psychophysical tasks can vary markedly with retinal eccentricity . This variation can be summarized by a retinal sensitivity curve such as the one shown in Figure 1B . The retinal sensitivity curve in Figure 1B is a plot of the probability of correct discrimination as a function of retinal eccentricity . The observer attempted to discriminate two possible configural targets ( Figure 1C ) consisting of a small circle superimposed on a square . As shown , the observer's probability of correct discrimination is close to one when the target is near the fovea and drops to chance beyond 12 degrees . Retinal scaling curves for many kinds of visual judgments have been measured [3]–[6] and researchers modeling visual search typically assume that the visual system effectively has access to estimates of visual sensitivity for different kinds of targets at different eccentricities [7]–[10] . This information is needed to correctly combine visual data from disparate retinal locations , detect the target or plan the next saccade . Since the mapping between eccentricity and visual sensitivity may differ for different kinds of targets , the amount of information needed to plan visual search well is potentially very large . We examined whether human observers have access to this information in a simple decision task . In the first part of the experiment ( calibration ) we mapped retinal scaling curves for the configural target at three contrasts , High , Medium and Low . Targets were placed along a horizontal line passing through the fovea and each target could be thought of as an ordered pair where is horizontal distance from the fovea , is contrast . In the main part of the experiment ( decision ) , the observers were asked to judge which of two configural targets , differing in contrast and in retinal eccentricity , or , was more discriminable . A judgment that is/was more discriminable than is denoted . Observers knew that , at the end of the experiment , they would be allowed to attempt to classify some of the targets they had chosen , receiving a reward for each correct response . It was therefore in their interest to select the more discriminable eccentricity-contrast pair on each trial . Unlike typical decision tasks [11] , this decision task does not involve a tradeoff between probability and value: we never varied the payoffs for success and failure . Successful performance requires only that the observer correctly orders probabilities . Performance would also be unaffected by monotone increasing transformations of probability commonly reported in the decision under risk literature [11]–[13] . The decision task is an example of a conjoint measurement task [14]–[15] . We vary contrast and eccentricity and see how these variations affect the observer's ordering of eccentricity-contrast pairs by discriminability . If the observer's judgments satisfy certain conditions that , in effect , assess their coherence or self-consistency , then the experimenter can potentially recover estimates of the observer's “subjective” retinal sensitivity curves for each contrast [14]–[15] and compare them to observers' actual performance . If one or more of the conditions fail , then we further conclude that the observer's choices are not based on a coherent model of their own retinal sensitivity . We test the observer's knowledge of his own ability to discriminate such targets in three ways , illustrated in Figure 2A . The first is a test of equivalence: Can observers correctly judge which pairs and are equally discriminable ? We can represent these pairs by indifference curves as shown in Figure 2A . The second test is transitivity: for any choice of eccentricities and contrasts , if and , then . Transitivity is a test of the self-consistency or coherence of observers' judgments . The third is a test of dominance: if , does the observer correctly judge that for any choice of eccentricity ? And if , does the observer correctly judge that for any choice of contrast ? ( Of course , we must verify experimentally that the two dominance claims are in fact true for our experimental conditions ) . Dominance is evidently the weakest of the three tests . The three tests are distinct: an observer who fails equivalence may still satisfy transitivity and dominance . This outcome would imply that , while his or her estimates of discriminability are in error , the estimates he or she has do , at least , cohere . An observer who fails transitivity cannot trade off contrast and eccentricity in any consistent way , but he or she may still know that more contrast improves performance and that performance near the fovea is better .
The experiment had been approved by the University Committee on Activities Involving Human Subjects ( UCAIHS ) of New York University and informed consent was given by the observer prior to the experiment . Stimuli were displayed on a 19-in . Sony Trinitron Multiscan G500 monitor controlled by a Dell Pentium D Optiplex 745 computer . The monitor was run at a frame rate of 100 Hz with 1280×1024 resolution in pixels . A forehead bar and chinrest were used to help the observer maintain a viewing distance of 57 cm . At that distance , the full display subtended 40 . 4°×30 . 3° . The observer viewed the display binocularly . Observers were required to fixate a fixation cross and all stimuli were presented relative to this fixation cross . We used an Eyelink II eye tracker to verify that observers did not make eye movements away from the fixation cross . At the beginning of each trial drift correction was made at the fixation cross . The criterion of eye movement was set to be a speed over 10 deg/s or an offset over 1 deg from the fixation cross . A trial would be cancelled if the fixation constraint were violated during the trial . The eye tracker was calibrated initially , drift corrected for each trail and re-calibrated after every 100 trials or when drift exceeded 5 deg . Stimuli were presented against a uniform gray ( 39 . 1 cd/m2 ) background . The fixation cross was black , spanning at the center of the screen . The target was a lighter gray ( 67 . 1 cd/m2 ) square with an even lighter gray dot of diameter at its top or bottom . The luminance of the dot could be 74 . 4 , 80 . 7 , or 91 . 4 cd/m2 , i . e . , a contrast of or relative to the square . We refer these three levels of contrast as low , medium , and high contrast . The contrast of a stimulus and the eccentricity at which it was presented , formed an eccentricity-contrast pair . The experiment consisted of two three-hour sessions completed on two successive days . Observers were advised to take a break every about 350 trials and allowed to take breaks whenever necessary . Each observer went through the two tasks in sequence: calibration , then decision . The time courses of both tasks are illustrated in Figure 2B . The calibration task allowed us to map probability correct as a function of eccentricity for each of the three contrasts . The observer's task was to decide whether the dot was at the top or at the bottom ( Figure 2B ) . Fixation was monitored . No feedback was given . For each of the three contrasts , the target could appear at any of 18 possible locations , i . e . , 18 possible eccentricities , evenly spaced from 2° to 12 . 2° on the right of the fixation cross . There were five blocks , in each of which each location of each contrast repeated for six times , half top and half bottom , randomly mixed together . Each observer completed 3 contrasts×540 trials = 1620 calibration trials . Before the experimental trials , there were 108 practice trials for the first session and 12 practice trials for the second session . To keep observers motivated , we rewarded observers with a bonus up to $10 based on their overall probability correct in the calibration task of each session . The probability correct of the calibration task was fitted against eccentricity with a Quick-Weibull psychometric function [16]–[17]: ( 1 ) where is a position parameter and is a steepness parameter . With Equation 1 , we could compute the probability correct for any eccentricity . We used these functions in the construction of decision trials . We described the decision task in the Introduction . In this task , observers chose between two targets of different combinations of contrast and retinal eccentricity . But rather than using real targets , we used a location cue and a color cue to indicate an eccentricity-contrast pair . The observer's task was to choose the target they preferred to attempt later ( Figure 2B ) . As in the calibration task , observers were required to fixate the fixation cross before the response display . There were two reasons why we did not use real targets to signify eccentricity-contrast pairs . First , if observers had seen real targets in a trial , they could base their decision on their immediate perception of the targets , in effect simulating the visual judgment . Second , with real targets , observers might mistake one contrast condition for another . Observers learned the association between targets and cues at the beginning of the experiment during the calibration task and we verified that they had learned these associations by a short “quiz” before the decision task . Observers knew that , at the end of the experiment , four of their choices would be chosen at random and that they would attempt to identify targets in the conditions corresponding to each choice . A correct response would lead to a $5 reward . Correct response for all of the four trials would result in a $20 bonus . To measure the point of subjective indifference ( equivalence ) between targets that differed in contrast , we used one-up , one-down adaptive staircase procedures . In a staircase , one target of one contrast was fixed in eccentricity and the target of the other contrast varied in eccentricity . The fixed contrast in each staircase had an eccentricity corresponding to a probability correct of 0 . 6 , 0 . 7 , 0 . 8 , or 0 . 9 separately estimated for each observer based on their calibration data . We estimated the eccentricity that the observer considered to be equally discriminable for the variable contrast . Each staircase consisted of 70 trials . There were 12 staircases ( 3 contrasts×4 probabilities ) , randomly interleaved . That is , 12 staircases×70 trials = 840 staircase trials . Based on these staircase trials , we tested equivalence and transitivity . To test dominance , we included trials in which the two targets had different eccentricities but the same contrast ( equi-contrast trials ) , or different contrasts but the same eccentricity ( equi-eccentricity trials ) . The possible contrasts were low , medium , and high . The possible eccentricities were the eccentricities corresponding to a probability correct of 0 . 75 for each of the three contrasts , computed with the functions estimated in the calibration task for the particular observer . The number of equi-contrast trials was 3 contrasts×3 eccentricity combinations×10 repetitions = 90 . The number of equi-eccentricity trials was 3 eccentricities×3 contrast combinations×10 repetitions = 90 as well . The 840 staircase trials and 180 dominance trials were mixed in a random order , divided into three blocks and completed in the second session after the calibration task . There were 24 practice trials before the formal experimental trials . Eight observers , four female and four male , participated . None of them was aware of the purpose of the experiment . All observers had normal or corrected-to-normal vision . The observers each received US $12/hour for their time and a performance-related bonus . Total payment ranged from US $87 to US $112 across observers .
For each observer , we fit the data of the calibration task to Equation 1 separately for each contrast using the maximum likelihood method . Figure 3 shows both the data and fit for each observer . From the 12 staircases of the decision task , we acquired 12 pairs of eccentricity-contrast pairs judged to be equally discriminable by the observer . Four of them had the target of low contrast in fixed eccentricity and the target of medium contrast in variable eccentricity , which we call a low-to-medium mapping . Another four staircases were medium-to-high mappings and a third set of four high-to-low mappings . For each observer , we computed the differences between the actual probabilities correct of the fixed and variable targets ( based on calibration data ) and examined whether they significantly deviated from zero . We used a bootstrap method [18] to estimate 95% confidence intervals for the probability difference of each pair for each observer ( 10 , 000 resamples ) . We tested whether differences in probability were significant at an overall level of . 05 with a Bonferroni correction for 12 tests . Figure 4A shows the differences of probability correct in each staircase separately for each observer . The vertical axis denotes probability correct . Each arrowed line points from the fixed target to the variable target . If a subjective indifference pair has identical probability correct for the fixed and variable targets , the arrowed line should be horizontal . Slanted lines correspond to differences in probability . Pairs with significant probability difference are in magenta . We noticed that observers' errors were not random in direction . In Figure 4A , the magenta lines for the same observer deviate from the vertical orientation either clockwise or counter-clockwise , but never in both ways . This pattern is an indication that the observer consistently overestimated or consistently underestimated the effect of differences in contrast on visual sensitivity . To verify this claim we computed probability difference of the lower contrast target minus the higher contrast target averaged across the 12 subjective indifference pairs for each observer . According to two-tailed Student's t-tests , all observers' mean probability difference was significantly different from zero ( p< . 05 ) . Among the eight observers , three overestimated the visual sensitivity difference and the other five underestimated it . We also measured observers' errors in eccentricity . The correct eccentricity of the variable target in a staircase was defined as the eccentricity where the variable target had the same probability correct as the fixed target . Eccentricity error of a subjective indifference pair was the actual eccentricity of the variable target minus the correct eccentricity . The absolute error averaged across the 12 pairs was 1 . 6 , 5 . 8 , 1 . 9 , 7 . 5 , 2 . 2 , 7 . 7 , 6 . 4 , and 5 . 0 degrees respectively for S1–S8 . Their median was 5 . 4 degrees . Therefore , observers' errors in the decision task were unlikely to be a byproduct of lack of ability to discriminate eccentricity . In the equivalence test , we tested whether observers made judgments consistent with their actual ability to classify stimuli differing in contrast and eccentricity . They did not do so . Next we examined whether observers' judgments , even though in error , were self-consistent by testing transitivity ( one of the necessary conditions for a conjoint measurement representation ) as follows . An observer's judgments are transitive if and only if , for all choices of eccentricities and contrasts : if and , then . We test transitivity in a slightly different form by measuring eccentricity-contrast pairs , that are judged equally discriminable . We denote equal discriminablity as . We test the following: if and , then . Suppose the function transforms any eccentricity at low contrast into an eccentricity at medium contrast that the observer judges to be equally discriminable . That is , the observer , rightly or wrongly , judges . We refer to as an equivalence transformation . From the decision task , we can estimate the equivalence transformations of low-to-medium , medium-to-high , and high-to-low contrasts , respectively denoted as , , and . The criterion for transitivity is that transitivity holds , should transform back to , that is , ( 2 ) In our transitivity test , we assume that the subjective probability correct for a particular contrast , like the true probability correct , is a function of eccentricity in the form of Equation 1 . An equivalence transformation from one contrast to another contrast would then be linear on a log scale ( see Text S1 for proof ) . ( 3 ) where is the eccentricity for contrast 1 , is the eccentricity for contrast 2 , and and are parameters to be estimated . If Equation 2 is satisfied , we should have ( 4 ) Define , . Testing for failure of transitivity requires only that we test whether either of and is significantly different from zero . For each observer , we fitted Equation 3 separately for the low-to-medium , medium-to-high , and high-to-low transformations . With the estimated 's and 's we computed and . We obtained the 95% confidence intervals ( Bonferroni corrected for two conditions ) of and using a bootstrap method [18] by resampling the staircase data for 10 , 000 times . Only one observer ( S5 ) passed the transitivity test . The remaining seven observers' mean and values were both significantly different from zero . Interesting , all the seven observers' deviations had the same direction . That is , all the 's were less than zero ( median across observers = −0 . 60 ) . All the 's were greater than zero ( median across observers = 1 . 06 ) . If , for any observer , and errors were independent and equally often positive or negative , the probability for all the seven observers to have a less-than-zero and a greater-than-zero would be . Therefore , the observed common pattern of failure of transitivity is unlikely to be the result of measurement error . Figure 4B shows a sequence of transformations . The three axes in each subplot represent the eccentricities of the low , medium , and high contrasts in the log scale . For each observer , we start from a specific eccentricity at the low contrast find the equivalent eccentricity at the medium contrast , then we pass from medium to high and then high to low . If the transformations satisfy transitivity , we should return to the same eccentricity at the low contrast axis after going through the three transformations , low-to-medium , medium-to-high and high-to-low . If transitivity holds , we stop after one set of three transformations ( low→medium→high→low ) . If it does not we continue with a second set of three transformations to make the pattern of intransitivity easier to visualize . Figure 4B illustrates the transitivity failure of seven out of eight observers and their common pattern of failure . We move from one axis to another axis in an arbitrary counter-clockwise way . Note that all the observers that failed the transitivity test had plots that tended to “corkscrew” outward . That is , when eccentricity is transformed from low contrast to medium contrast and then to high contrast , the resulting eccentricity difference between the low and high contrasts tended to be larger than when they mapped from low to high directly . Observers failed the equivalence test and , with one exception , the transitivity test . The dominance test is , in conjoint measurement terms , a test that observer's preferences form a weak order satisfying single cancellation [15] . We are asking whether observers , given two targets of equal contrast at different eccentricities , judge the target with smaller eccentricity to be more discriminable ( equi-contrast dominance ) and that , given two targets at the same eccentricity , judge the target with higher contrast to be more discriminable ( equi-eccentricity dominance ) . Figure 4C show the percentage of dominance errors for each observer . For each observer and condition , we computed the 95% confidence intervals for the percentage of errors by treating the true proportion of errors as a random variable with a beta distribution whose parameters are determined by the observed numbers of errors and non-errors . Although the percentage of errors was significantly larger than zero for most of the observers in either condition , the values were small . The medians across observers were 8 . 3% and 11% , respectively for the equi-contrast trials and equi-eccentricity trials . The upper limits of all the confidence intervals were far below 50% , the chance level .
|
Human ability to discriminate drops dramatically with increasing distance from the center of vision . If you fixate a word on a page , you likely can not read words a short distance away . Because of this retinal inhomogeneity , we need to move our eyes to search a scene . The efficiency of search depends on how well the visual system compensates for inhomogeneity in planning eye movements . We used a simple decision task to find out what the observer “knows” about his or her own retina . We first measured observers' sensitivity for targets , varying contrast and eccentricity . Observers then repeatedly chose between targets differing in eccentricity and contrast , selecting the one they would prefer to attempt: e . g . , a low contrast target at 2° versus a high contrast target at 10° . Could observers correctly trade off contrast and eccentricity and pick the more discriminable of the two targets ? We found that observers exhibited large , patterned errors in their choices , making choices that were not even self-consistent .
|
[
"Abstract",
"Introduction",
"Methods",
"Results"
] |
[
"neuroscience/experimental",
"psychology",
"neuroscience/sensory",
"systems",
"computational",
"biology/computational",
"neuroscience"
] |
2010
|
Gambling in the Visual Periphery: A Conjoint-Measurement Analysis of Human Ability to Judge Visual Uncertainty
|
An abundant literature dealing with the population genetics and taxonomy of Giardia duodenalis , Cryptosporidium spp . , Pneumocystis spp . , and Cryptococcus spp . , pathogens of high medical and veterinary relevance , has been produced in recent years . We have analyzed these data in the light of new population genetic concepts dealing with predominant clonal evolution ( PCE ) recently proposed by us . In spite of the considerable phylogenetic diversity that exists among these pathogens , we have found striking similarities among them . The two main PCE features described by us , namely highly significant linkage disequilibrium and near-clading ( stable phylogenetic clustering clouded by occasional recombination ) , are clearly observed in Cryptococcus and Giardia , and more limited indication of them is also present in Cryptosporidium and Pneumocystis . Moreover , in several cases , these features still obtain when the near-clades that subdivide the species are analyzed separately ( “Russian doll pattern” ) . Lastly , several sets of data undermine the notion that certain microbes form clonal lineages simply owing to a lack of opportunity to outcross due to low transmission rates leading to lack of multiclonal infections ( “starving sex hypothesis” ) . We propose that the divergent taxonomic and population genetic inferences advanced by various authors about these pathogens may not correspond to true evolutionary differences and could be , rather , the reflection of idiosyncratic practices among compartmentalized scientific communities . The PCE model provides an opportunity to revise the taxonomy and applied research dealing with these pathogens and others , such as viruses , bacteria , parasitic protozoa , and fungi .
The PCE model [1] , [2] defines clonal evolution as scarcity or absence of genetic recombination , a definition that is accepted by most authors working on pathogen population genetics [3] , including the species here surveyed [4]–[9] . The PCE model [3] , [10] , [11] ( i ) does not presume that recombination is absent [12] , [13] or plays a minor evolutionary role , but that it is too rare to break the prevalent pattern of clonality; ( ii ) addresses each species as a whole , and not their genetic subdivisions considered individually [14]; and ( iii ) definitely includes selfing/inbreeding/homogamy ( which lead to restrained recombination ) as particular cases of PCE , rather than as distinct evolutionary models [1]–[3] , [10] , [11] , [15] . This view is shared by many authors working on the pathogens here analyzed [12] , [16]–[19] and by others [20] . A few authors [21] , [22] prefer to limit the concept of clonality to “strict” clonality ( i . e . , mitotic propagation ) and consider that it should be distinguished from selfing/inbreeding/“unisex . ” This is a matter of definition . It is nevertheless worth noting that in the examples cited in [21] , differently from the authors of the article , all scientists working on parthenogenesis in insects , amphibians , fishes , and reptiles definitely include parthenogenesis in clonality . As we have exposed extensively [1]–[3] , biases that could lead to wrong conclusions of restrained recombination ( mainly isolation by distance and/or time or Wahlund effect ) should be carefully considered before concluding a PCE pattern . Lastly , as we have insisted in [3] , the PCE model states that restrained recombination is mainly due to built-in properties of microbes , rather than to the downstream elimination of most possible recombinants by natural selection and epistasis phenomena . If natural selection were the main factor that would maintain clonality , it would be at unacceptable costs for the organisms considered , because this would mean that most of the offspring is eliminated at each generation . Natural selection certainly acts on microbes , as it does on any organism . However , our proposal is that it cannot be the main factor responsible for PCE in organisms that would be otherwise potentially panmictic .
We have recently proposed new insights about PCE , applicable to all kinds of micropathogens ( including viruses , bacteria , parasites , and fungi ) [3] and , more specifically , to Trypanosoma and Leishmania [10] and to Plasmodium and Toxoplasma [11] . We have proposed replacing subjective and imprecise assertions such as “recombination at a high rate” [14] or “gross incongruences” [23] with a clear-cut PCE definition relying on two complementary criteria: ( i ) statistically significant linkage disequilibrium ( LD ) , or nonrandom association of genotypes occurring at different loci , and ( ii ) growing phylogenetic signal when more reliable data are added . Lastly , we have discussed the possibility of distinguishing PCE from cryptic biological speciation . We have also distinguished clonality by lack of available mating partners ( due to scarcity of multiclonal infections ) from built-in clonality . LD is the very statistic that permits one to evidence lack of recombination , the basic definition of PCE . Contrary to segregation tests , LD analysis does not require that the organism under survey is diploid , nor does it require knowledge of ploidy [3] . This is highly relevant when micropathogens are concerned [3] since widespread aneuploidy seems to be very frequent in them , including in fungi , Trypanosoma , and Leishmania [12] , which renders tests based on diploidy invalid . When a sufficient set of loci is analyzed , LD is a very powerful statistic [1] . One has to ascertain that LD cannot be explained by trivial physical obstacles ( isolation by space or time: the Wahlund effect ) [2] . It is widely used as circumstantial evidence for PCE by authors working on the pathogens here considered [7] , [24]–[26] and by others [27] , [28] . A telling consequence of LD is the spread of stable multilocus genotypes ( MLGs ) over vast time and space scales [3] . However , this pattern depends on the rate of evolution ( molecular clock ) of the marker considered and might not be observed with fast-evolving markers such as microsatellites , even in the case of strong linkage disequilibrium [3] . The criterion of a growing phylogenetic signal when more adequate data are added relies on the congruence principle [29] , which states that if the working hypothesis is correct , evidence increases as more data are considered . For example , when a set of Multilocus Sequence Typing ( MLST ) data are considered , although some discrepancies can be observed between individual gene trees , the phylogenetic signal gets stronger and stronger when more loci are included in the combined tree . Or , the genetic distances calculated from different molecular markers are strongly correlated ( the “g” test [1] ) . If the impact of recombination were stronger than clonal propagation in the long run , the contrary would obtain . This approach , relying on congruence , may not be verified when inadequate data are compared , such as , for example , markers with different molecular clocks or undergoing different selective pressures or different evolutionary tendencies . This could lead to wrong assertions of recombination [10] . The main manifestation of this growing phylogenetic signal is the existence of genetic subdivisions that are stable in space and time ( “near-clades” [3] ) . The term “clade” [26] , [30] , [31] is not adequate when micropathogens are concerned , because even when PCE obtains , some residual recombination can always occur [3] . We have differentiated PCE from cryptic speciation . It has been inferred that apparent clonality could be explained by the fact that the species under study is subdivided into discrete genetic clusters , among which recombination is inhibited while it is not within them [32] . Such a model amounts to equating these genetic subdivisions to cryptic biological species . To distinguish this case from PCE , we have proposed [10] the “Russian doll model . ” If the PCE criteria are uncovered , not only at the level of the whole species but also within its genetic subdivisions , it favors PCE rather than cryptic speciation . In this case , the genetic subdivisions of the species show a miniature picture of the whole species , with LD and lesser near-clades ( Figure 1 ) . However , this approach should be conveniently applied by selecting markers with an adequate resolution power ( molecular clock ) . As a matter of fact , when addressing lesser genetic subdivisions rather than the whole species , one changes evolutionary scales . If the resolution of the markers is not consequently adapted , lack of PCE signal could be due to a statistical type II error ( lack of resolution ) . For the same reason , the sampling size should not become too small . We have also discussed apparent clonality by lack of available mating partners in low transmission cycles . To explain apparent manifestations of clonality in Plasmodium falciparum [1] , [2] , it has been proposed that selfing/inbreeding occurred “mechanically” in low transmission areas because mixed infections of different genotypes are rare , which makes outcrossing impossible [33] . We have called this model the “starving sex hypothesis” and have shown that it was frequently at odds with the available data in P . falciparum as well as in P . vivax [11] . The alternative hypothesis [11] is that restrained recombination by selfing , inbreeding , or any other mechanism , is a built-in evolutionary strategy used by the pathogen to avoid the “recombinational load” ( break-up of favorable MLGs by recombination [34] ) , even when different MLGs are available for mating . Inbreeding/selfing , unisexual reproduction can be considered as a way to add limited phenotypic and genotypic diversity in a clonal population without breaking favorable multilocus combinations [12] , [18] . Cryptococcus and Giardia possess meiosis genes [17] , [35] . However , these genes could be associated with other functions than meiosis: “Evolution is constantly re-using old genes for new purposes” [16] . We have proposed [3] that many micropathogens could possess a “clonality/sexuality machinery” rather than meiosis genes for switching between clonal evolution and recombination to face various evolutionary challenges . Selfing could be used by them instead of outcrossing , even when mating partners are available .
Clonality in Cryptosporidium , whose cycle includes meiosis , is generally considered explainable by lack of outcrossing opportunity due to low transmission , or starving sex [53] . However , some data do not rule out the alternative hypothesis of built-in restrained recombination , even if the data are less conclusive than for Plasmodium [11] . In Ireland , Cr . parvum is considered panmictic due to high transmission rates . However , the percentage of multiclonal infections is lower in Ireland than in other European countries such as Italy , where Cr . parvum is not panmictic [54] . In the US Midwest , Cr . parvum is overall panmictic . However , it is “epidemic” ( unstable clonality [27] ) in Minnesota , where the transmission is high [55] . The C . gatti widespread genotype responsible for the Vancouver epidemics is supposed to be the result of “same sex mating” between identical MLGs [38] . This results in “meiotically-derived clones undetectable by molecular approaches” [43] . However , it cannot be inferred from the data whether same-sex mating is the result of starving sex or of built-in restrained recombination . In summary , evidence that the main PCE signs obtain is strong in G . duodenalis and the CNC . Both present striking similarities with many other pathogens , for example , Trypanosoma cruzi [10] and Toxoplasma gondii [11] , [56] , with significant LD; clearly delimited near-clades; ubiquitous , stable MLGs; and “Russian doll” patterns within the near-clades . Both Giardia and the CNC also present indications for limited recombination or hybridization , both within and between near-clades [36] , [47] , [57] , and even between species in the case of the CNC [41] . As is the case for T . cruzi [58] and Toxoplasma [56] , patterns of hybridization might be complex [41] . The case of Cryptosporidium is less clear . This apicomplexa genus is known to undergo a sexual phase during transmission cycles , as do Plasmodium and Toxoplasma . Indications for clonal evolution are present in some populations . One Cr . hominis MLG is dominant and widespread in the UK [59] . Some Cr . andersoni MLGs are widespread in North America and the Czech Republic [60] and in several Chinese regions [61] . LD evidence is strong in Cr . hominis [7] , [59] , [62] and Cr . parvum [9] , [59] . However , the impact of the Wahlund effect was not taken into account in [7] , [62] . Near-clading can be suspected in Cr . hominis [7] , Cr . parvum [13] , and Cr . muris [61] , although the evidence is less clear than for Giardia and the CNC . Lastly , panmixia was inferred in some populations of Cr . parvum [54] , [55] . It is possible that Cryptosporidium population structure is similar to that of P . falciparum and P . vivax [11] , with a continuum between panmixia and clonality and the existence of unstable near-clades . As for Plasmodium , whether clonality is due to starving sex or in-built genetic properties should be explored in depth . Obviously , the issue of Cryptosporidium population structure deserves further investigation . Lastly , some indications for clonality were found in Pn . jirovecii [45] . However , evidence is far too limited to reach any firm conclusions .
LD permits indirect typing; that is to say , the characterization of whole genotypes with only one gene , or a few genes . When LD is doubtful , indirect typing can be grossly misleading . This could be the case for Cryptosporidium subtyping with the unique gp60 gene [63] . If recombination is frequent , multilocus typing [64] is not a solution since frequent recombination makes the MLGs ephemeral . Still , the fact remains that the population structure of Cryptosporidium is far from being panmictic . Even if it is not strong enough to lead to stable near-clades , restrained recombination in these parasites constitutes a major stratification factor that should be taken into account in molecular epidemiology and all applied studies , as it should in Plasmodium [11] . When the evidence for PCE is clear , clonal MLGs and near-clades are convenient units of analysis for both molecular epidemiology and experimental evolution [3] , thanks to their stability in space and time . Near-clades can be characterized by specific markers [13] .
We have called attention to the fact that radically dissimilar taxonomical inferences could be drawn from similar sets of data [65] . Scientists working on the pathogens here surveyed have granted considerable attention to taxonomical problems and species definition and delimitation . The conclusions they have reached vary considerably . The PCE model allows reconsidering these questions . Two main species concepts are involved in these debates: the biological species concept ( BSC ) [66] and the phylogenetic species concept ( PSC ) [67] . The BSC demands two criteria: ( i ) within the species , genetic flow should have no other limitations than physical obstacles ( potential panmixia ) and ( ii ) it should be inhibited between species by built-in biological mechanisms . The PSC stipulates that species should correspond to clades , between which , by definition , gene flow is interrupted . Generally , authors propose a mix of genetic and biological characteristics to define species [68] . Some attempts have been made to apply the BSC concept to the CNC: experiments have shown that crosses within C . gattii VG II are easy , while they are difficult between II and III [31] . The authors have proposed that II and III deserve the status of biological species . This is debatable for two reasons: ( i ) experiments tell nothing about the frequency of recombination in nature [3] and ( ii ) the presence of stable genetic subdivisions ( Russian doll near-clades ) in VG II [31] , [42] clearly shows that VG II is not a potentially panmictic entity . Also , by the survey of natural populations , it has been proposed [5] to equate the CNC “genotypic groups” to biological species . Nevertheless , as shown above , many PCE manifestations are observed within these groups . The BSC has been proposed for the Cryptosporidum species [64] , although , as we have seen above , recombination is restrained in some populations of this parasite . Attributing the species status to the Giardia assemblages still is a matter of debate [4] , [6] , [16] , [69] , [70] . Lastly , as we have seen , the host-specific Pneumocystis genotypes are now considered as distinct species , although they could be equated , as well , to near-clades . We propose that the BSC is not applicable to most , if not all , micropathogens . First , even between different species , very often , some genetic exchange occurs . Second , more importantly , clonality occurring in many populations of micropathogens makes it impossible to consider them as potentially panmictic units . The PCE concept , and more specifically , the near-clade and Russian doll models , give an opportunity to apply the PSC to most pathogen species . The flexible phylogenetic approach based on the congruence principle relaxes the demands of a strict cladistic approach . The near-clades can be the starting units ( necessary , but not sufficient ) for species description based on the PSC adapted to the special case of micropathogens ( lack of strictly separated intraspecific clades ) . It would then be the decision of specialists working on the considered pathogen to decide whether the specific biological properties and medical relevance of the near-clades ( host specificity , pathogenicity , and drug resistance ) justify that they be described as new species .
We have provided clear evidence that the PCE model as it is formulated in the present study is verified in many pathogens , including viruses , bacteria , parasitic protozoa , and fungi [1]–[3] , [10] , [11] . The PCE model provides a convenient population genetics framework for all applied studies ( strain typing , vaccine and drug design , and molecular and immunological diagnosis ) dealing with the pathogens here surveyed and for experimental evolution . As a matter of fact , it provides these studies with stable , clearly defined units of analysis ( clonal MLGs , near-clades ) . Moreover , it might bring a renewal of the long-lasting controversies concerning the species status of Cryptosporidium , Giardia , Cryptococcus , and Pneumocystis .
|
Micropathogen species definition is extremely difficult , since concepts applied to higher organisms ( the biological species concept ) are inadequate . In particular , the pathogens here surveyed have given rise to long-lasting controversies about their species status and that of the genotypes that subdivide them . The population genetic approach based on the predominant clonal evolution ( PCE ) concept proposed by us could bring simple solutions to these controversies , since it permits the description of clearly defined evolutionary entities ( clonal multilocus genotypes and near-clades [incompletely isolated clades] ) that could be the basis for species description , if the concerned specialists find it justified for applied research . The PCE model also provides a convenient framework for applied studies ( molecular epidemiology , vaccine and drug design , clinical research ) dealing with these pathogens and others .
|
[
"Abstract",
"Introduction:",
"The",
"Model",
"of",
"Predominant",
"Clonal",
"Evolution",
"(PCE)",
"Recent",
"Developments",
"PCE",
"Manifestations",
"in",
"the",
"Pathogens",
"under",
"Survey",
"Implications",
"for",
"Molecular",
"Epidemiology",
"and",
"Experimental",
"Evolution",
"Taxonomical",
"Implications",
"Conclusion"
] |
[
"opinion",
"biotechnology",
"applied",
"microbiology",
"mycology",
"biology",
"and",
"life",
"sciences",
"population",
"genetics",
"microbiology",
"evolutionary",
"biology",
"evolutionary",
"systematics",
"evolutionary",
"genetics",
"parasitology"
] |
2014
|
Cryptosporidium, Giardia, Cryptococcus, Pneumocystis Genetic Variability: Cryptic Biological Species or Clonal Near-Clades?
|
Physical performance emerges from complex interactions among many physiological systems that are largely driven by the metabolic energy demanded . Quantifying metabolic demand is an essential step for revealing the many mechanisms of physical performance decrement , but accurate predictive models do not exist . The goal of this study was to investigate if a recently developed model of muscle energetics and force could be extended to reproduce the kinematics , kinetics , and metabolic demand of submaximal effort movement . Upright dynamic knee extension against various levels of ergometer load was simulated . Task energetics were estimated by combining the model of muscle contraction with validated models of lower limb musculotendon paths and segment dynamics . A genetic algorithm was used to compute the muscle excitations that reproduced the movement with the lowest energetic cost , which was determined to be an appropriate criterion for this task . Model predictions of oxygen uptake rate ( VO2 ) were well within experimental variability for the range over which the model parameters were confidently known . The model's accurate estimates of metabolic demand make it useful for assessing the likelihood and severity of physical performance decrement for a given task as well as investigating underlying physiologic mechanisms .
The ability to achieve and maintain a level of physical performance is important for carrying out activities of daily living and is especially critical for tasks related to the military , competitive sports , and rehabilitation . It is well known that many factors can limit physical performance including the physical demands of the task itself as well as trauma , disease , psychological state , and the environment; however , the underlying mechanisms are poorly understood and current models have limited predictive power . Physical performance emerges from complex interactions among many physiological systems such as cardiovascular , respiratory , and neuromuscular systems . These interactions are largely driven by the metabolic energy demanded; therefore , characterizing metabolic demand across movements is an essential step for revealing the mechanisms of physical performance decrement under the many conditions in which it can occur . Estimates of metabolic demand for a given muscle or task can indicate susceptibility to muscle fatigue and resulting degradation of performance . Higher rate of muscle energy consumption leads to larger accumulation of chemical byproducts from contraction and metabolism that interfere with active force generation ( see [1] for review ) . High metabolic demand may also exceed the capacity of the cardiorespiratory system to provide the necessary nutrients and to help clear the metabolic and contractile byproducts . A model of metabolic demand would be useful in assessing whether activity can be sustained by the cardiorespiratory system given performance limiting factors such as respiratory muscle fatigue [2 , 3] , reduced cardiac output [4] , reduced partial pressure of oxygen in the inspired air [5] and the presence of gases that may inhibit oxygen absorption [6] . Energy consumption estimates can also help quantify increases in body temperature , which is known to have a strong influence on physical performance . Contractile and metabolic processes are not perfectly efficient and therefore release heat that raises body temperature . Although increases in temperature do not affect muscle function significantly [7] , they may be sufficient in hot environments to affect cardiovascular function or to lead to reduced muscle excitation via central mechanisms [8] . Models of metabolic energy consumption for various tasks have been developed in the past , but have limited predictive capabilities . Regression analysis has been used to relate energy consumption to gross characteristics of a task ( such as running speed and size of backpack load being carried; [9] ) , whole body acceleration [10] , and joint kinematics and kinetics [11] . Energy consumption has also been related to physiological signals such as electromyographic ( EMG ) signals [11] and heart rate [12] . Because there are a large number of additional factors with strong , complex , and interactive effects on energetics , regression based models generalize poorly beyond the range of conditions from which they are derived . Muscle-based models have also been used to predict energetics at the task level [13–15] . These models , however , are limited in their ability to predict muscle activation [16–19] , hence energy related to ATP/PCr breakdown . The models are also limited in their ability to predict energy related to ATP/PCr synthesis and together these limitations lead to poor predictions of metabolic energy consumption at the task level ( see S4 Appendix ) . A muscle energetics model was developed recently and validated for individual muscles as well as for high intensity dynamic knee exercise [17] . However , it is important to validate the model across the full range of task efforts , because muscle mechanics and energetics depend strongly and complexly on activation level [17–19] , and because most physiological tasks involve low to moderate levels of muscle activation . For maximal effort tasks , it can be assumed that synergist muscles are activated at similarly high levels [17 , 20] . However , predicting energetics of submaximal effort tasks is more challenging because it requires computing the relative activation and load sharing among synergist muscles . The goal of this study was to extend the model to reproduce the kinematics , kinetics , and metabolic energy consumption for dynamic exercise across a wide range of intensities .
The modeled task is described in Andersen et al . [21] and is briefly summarized here . Subjects were instructed to repeatedly extend one knee against controlled levels of resistance while restrained in an upright sitting posture . They had to extend their knee from 90° to 170° and back in one second , and repeat this motion smoothly and continuously for 8–10 minutes . A heel cup and metal rod assembly connected their leg to the crank arm of a cycle ergometer that resisted knee extension , but allowed knee flexion to occur passively . The task was designed to isolate muscle recruitment to the knee extensor muscles ( quadriceps femoris ) to facilitate physiological measurements and analysis . These muscles have a large mass so changes in their activity and corresponding energy consumption contributed to most of the change in pulmonary oxygen uptake that was measured [21] . In a separate study [22] , blood samples were drawn from the femoral artery and vein to measure oxygen uptake of the quadriceps muscles more directly . Both pulmonary and quadriceps oxygen uptake data were used for validation . A musculoskeletal model was implemented to capture the effects of muscle excitations and external forces such as gravity and ergometer load on the resulting motion and metabolic energy consumption of the leg . The model represented an average young , healthy male with a height of 1 . 8m and 75kg body mass , which corresponds with the subjects reported in the experimental studies used for validation . With the exception of musculotendon dynamics , all other components of the musculoskeletal model were obtained from a commercially available and validated musculoskeletal model ( "Full Body Model"; SIMM , Musculographics , Inc . , Santa Rosa , California , United States ) . To account for leg dynamics , the model includes a set of rigid bodies representing the torso , thigh , shank , and foot segments that link the modeled hip , knee , and ankle joints . Segment dimensions and inertial properties as well as kinematic constraints imposed by the joints are also captured . Limb kinematics are coupled to musculotendon kinematics according to attachment points of musculotendons on the segments and various musculotendon path constraints that account for contact with neighboring soft tissue and bone . The muscle model described in Tsianos et al . [17] was used to characterize each muscle's force output and rate of metabolic energy consumption in response to neural excitation and musculotendon length . The model accounts separately for the major processes that consume ATP/PCr during a contraction such as active ion transport and cross-bridge cycling . It also accounts for the energy required to replenish ATP/PCr stores ( see Figure 11 in [17] and Table 1 in [17] for an overview of the muscle force/energy consumption formulation and for the underlying equations , respectively ) . The excitation signal in the model represents the common synaptic drive to all motoneurons and ranges between 0 and 1 , where 1 corresponds to maximal neural drive . Maximal drive is defined as the level of drive that recruits all motor units at their maximal physiologic firing rate . The excitation signal is transformed into individual motoneuron firing rates according to Henneman's size principle [28 , 29] . Muscle fascicle length and velocity is computed from musculotendon length , depending on the relative stiffness of the tendon/aponeurosis and muscle , and muscle mass . The model captures the interactive effects of firing rate , fascicle length , and fascicle velocity on force production and energy consumption . It also accounts for the effects of muscle morphometry and fiber composition . Morphometric parameters used for this study ( see Table 1 ) were obtained from Delp [30] . The tendon slack length parameter was converted to optimal tendon length ( tendon length at maximal isometric force ) , which is the units of tendon length accepted by the muscle model . Optimal tendon length was defined as 5% shorter than tendon slack length [29] . All muscles in the model were assumed to be composed of equal volumes of slow and fast twitch fiber types , which is consistent with experimental data for the knee extensors [31] , the prime movers of the modeled task . To implement the musculoskeletal model , the SIMM "Full Body Model" was imported to a modeling environment called MusculoSkeletal Modeling Software [MSMS; 27] that replaced the muscle model from the "Full Body Model" with the validated one described above . Once imported into MSMS , only the right leg was kept and musculoskeletal parameter settings were verified . All joint angles other than the knee were fixed to constant values according to the experiment . Hip flexion angle was fixed to 70° and all other joints were fixed to their neutral anatomical angles ( see Fig 1 ) . The MSMS model was then exported to Simulink ( The MathWorks , Inc . , Natick , Massachusetts , United States ) , where the differential equations that govern the musculoskeletal system's dynamics were automatically formulated . The ergometer model ( see below ) was also incorporated into the Simulink model . The aggregate set of differential equations was numerically integrated to determine knee motion and energetics in response to an arbitrary set of muscle excitation signals . A simple ergometer model was constructed based on the ergometer setup and forces recorded in Andersen et al . [21] . In the experiment , subjects were attached to a cycle ergometer through a connecting rod that linked their ankle to the ergometer's crank arm ( see Figure 1 in [21] ) . A resistive load was only applied when the knee was extending . The force acting on the ankle was approximately perpendicular to the long axis of the shank and depended on the rate of knee extension . The force was roughly proportional to the rate of knee extension , so it was defined in the model as the product of knee extension rate and a proportionality constant that depended on the power output of the ergometer: Ferg ( t ) ={P¯erg2 . 62*ω ( t ) , ω ( t ) >00 , ω ( t ) ≤0 , where P¯erg is the average ergometer power over one period of the task and ω ( t ) is knee extension velocity ( see S1 Appendix for the derivation of the proportionality constant ) . An optimization algorithm was used to compute the metabolic cost of dynamic knee extension . As explained below , it was assumed that subjects adopted muscle recruitment strategies that minimized metabolic cost , so the optimization algorithm described below was used to predict metabolic cost by computing the muscle excitation signals that minimized the metabolic cost of the dynamic knee extension task . Predictions of metabolic energy consumption were validated against two separate studies of dynamic knee extension that measured oxygen uptake rate ( VO2 ) at the level of the lungs and quadriceps muscles , respectively . The model predicted the steady state energy consumption of the leg in watts . Measuring this directly is highly invasive and complex so there is very little data in the experimental literature . Such data is available for intense dynamic knee extension [25] , which has been used previously to validate the model for dynamic exercise at maximum exertion [17] . Typically , the rate of metabolic energy consumption is inferred from VO2 measurements . Muscle cells can extract energy from nutrients to drive contraction either with or without oxygen . Nearly all energy gets extracted via a chain of chemical reactions that require oxygen ( aerobic catabolism ) as long as there is sufficient oxygen present and the rate of ATP/PCr consumption does not exceed the overall reaction rate of aerobic catabolism . The amount of energy extracted per oxygen molecule consumed has been measured and is similar across the different of types of nutrients ( carbohydrate , fat , and protein; [35] ) . For aerobic catabolism , rate of energy consumption in watts can be inferred from VO2 , measured in typical units of liters O2 consumed per minute , using the following conversion factor: 335 [watts]/ [liters O2/min] ( using 20 . 1 [kJ]/ [liters O2] derived from [36] ) . If a high amount of anaerobic catabolism is involved , then VO2 alone cannot provide a good estimate of metabolic energy consumption and this conversion factor does not apply . Andersen et al . [21] measured steady state pulmonary VO2 ( 6–8 minutes after exercise onset ) from eighteen subjects across several cycle ergometer loads . Dynamic knee extension was modeled for the following ergometer loads: 0 , 11 , 23 . 5 , 35 . 5 , and 47W . For most subjects , this range of exercise levels involved aerobic catabolism almost exclusively , as arterial lactate did not increase significantly above the resting level [21] . For 47W exercise , arterial lactate levels for some subjects were significantly greater than rest level , but anaerobic relative to aerobic catabolism across subjects appeared to be negligible ( also see [37] ) . Because anaerobic catabolism does not require oxygen , these subjects should theoretically perform the exercise at a lower VO2 . This is expected to increase variability of VO2 , but VO2 variability for 47W was small and similar to lower exercise loads . To compare model predictions against the experimental results , the energy consumption rate of the leg muscles was converted to VO2 . There are other contributors to pulmonary VO2 other than skeletal muscles of the moving leg such as the heart and lungs that help supply oxygen to the muscles , and postural muscles that help maintain balance during the exercise . In a separate study , body and leg VO2 were measured simultaneously for dynamic knee extension at low to moderate power outputs [24] . Specifically , the ergometer loads tested demanded 18 and 47W of power from the knee extensors , which translates to 0 and 29W of ergometer power given the roughly 18W of power needed to overcome gravity and inertia of the leg [23] . The difference between the measured body and leg VO2 was about 0 . 115 liters of O2 per minute for this range of exercise levels and was used to estimate the total energy consumption outside the moving leg for model validation . In a separate study , Andersen and Saltin [22] reported VO2 of the knee extensor muscles for exertion levels ranging from 10W to exhaustion . As in the other validation study , only exercise levels up to about 50W were considered and model output in watts was converted to VO2 . Experimentally measured VO2 also included a component present during rest , whose value was obtained from Krustrup et al . ( 0 . 07 liters O2/min; [24] ) and added to the model prediction of VO2 related to the exercise . A sensitivity analysis was performed to assess the robustness of model predictions , given the uncertainty of the most influential parameters . These parameters defined the task and musculoskeletal system . Specifically , hip angle and the lower limit of knee extension range were perturbed by +/-10° . The upper limit of knee extension range was perturbed by +5° and -10° , respectively . The most influential musculoskeletal parameters were simultaneously perturbed within their range of uncertainty ( defined in Table 2 ) to generate the least and most economical musculoskeletal configurations . See S3 Appendix for a detailed explanation and justification for all parameters perturbed . Sensitivity of energetic predictions was assessed against the variability of pulmonary VO2 data from Andersen et al . [21] because the data was collected from a larger and more diverse set of subjects than Andersen and Saltin [22] and is therefore more representative of the variability that could occur from natural variations in these parameters . The two highest ergometer loads ( 35 . 5 and 47W ) were excluded from the analysis because the contributions to pulmonary VO2 from sources besides the leg , such as postural muscles , were not known ( see "Methods-Validation" ) .
An optimization trial and corresponding solution are shown in Fig 4 as an example . Because the initial population of solutions was selected at random , movement error was large and energy consumption rate was highly variable . As optimization progressed , movement error decreased and ultimately converged to acceptable levels at a low energy consumption rate . The solution chosen for the validation analysis was the minimum energy solution with acceptable movement error . Other solutions often exhibited knee angle trajectories that deviated substantially from the specified task or had high energy costs , which could be due to high levels of cocontraction or due to relatively high excitation of one muscle leading to recruitment of less economical fast twitch fibers ( see Fig 4 ) . The optimization algorithm successfully computed muscle excitation signals that reproduced the kinematics of dynamic knee extension in Andersen et al . [21] . As in the experiment , primarily the knee extensor muscles ( i . e . the quadriceps ) were recruited in the model ( also see [34] ) . The activity of the rest of the muscles was low; it did not contribute significantly to task energetics or kinematic performance . For the trial shown , the timing of modeled and experimentally measured muscle activity via EMG were qualitatively similar . Recruitment patterns that emerged in the model varied substantially depending on the initial population of recruitment patterns used by the genetic algorithm and the sequence of pseudo-random numbers that affected each evolutionary step . Despite having different recruitment patterns , the converged solutions generally met the kinematic criteria at a similarly low energetic cost . Model predictions of pulmonary VO2 were near the center of the experimental range with the exception of the two highest ergometer loads tested ( Fig 5; see "Discussion-Validity of model predictions" ) . Modeled pulmonary VO2 increased almost linearly across all work rates while the slope of the experimental pulmonary VO2 relation was noticeably higher for higher work rates . Modeled and experimental knee extensor VO2 versus work rate were roughly linear . Predictions of knee extensor VO2 were within the experimental range across all work rates and were especially similar to measurements from two out of the five subjects in the study . Perturbing the hip angle by +/- 10° led to VO2 predictions that were within 3% of nominal , which is well within one standard deviation of experimental variability . Adjusting the lower limit of the knee extension range by +/- 10° resulted in VO2 predictions that were on average within 5% of nominal , which is also well within one standard deviation of experimental variability . Changing the upper limit of the knee extension range from 170° to 160° and 175° , respectively , led to deviations that were on average about 20% of nominal VO2 predictions . Model predictions in this case remained within one standard deviation of the experimental range and were more sensitive to an increase rather than a decrease in the upper limit of knee extension range ( Fig 6a ) . The sensitivity of the model's predictions to the acceptable movement error parameter was low . As shown in Fig 4 , for example , allowable movement error was chosen conservatively such that the predicted knee angle trajectory was nearly identical to the ideal trajectory ( see knee angle trajectory on the bottom left of Fig 4 ) . Even when the accuracy requirement was relaxed to the point where the simulated trajectory deviated substantially from the ideal , as in the bottom right of Fig 4 , the metabolic energy predicted remained similar and therefore lay well within experimental variability . The least and most economical musculoskeletal configurations yielded pulmonary VO2 predictions that were within one standard deviation of the experimental variability ( Fig 6b ) . Nominal predictions of pulmonary VO2 were closer to those of the least economical configuration . The range of VO2 predictions for 11 and 23 . 5W exercise was about two times larger than 0W exercise .
Model predictions fell within subject variability for both validation studies , with the exception of predicted pulmonary VO2 for the two highest work rates tested . At these high workloads , subjects are likely to increase recruitment of postural muscles to maintain balance , which could lead to the jump in energy consumption observed in the experiment [21] . Postural muscles were not included in the model , therefore , pulmonary VO2 predictions did not exhibit such a jump and continued to increase in a nearly linear fashion instead . In fact , the other study conducted by the same group [22] showed that knee extensor VO2 rose almost linearly even across these high ergometer loads as opposed to pulmonary VO2 ( Fig 5 ) . Moreover , model predictions agreed well with these experimental data across all ergometer loads tested . Varying hip angle did not have a significant effect on model predictions . This can be explained by the fact that when hip angle is altered , out of all knee extensors ( the prime movers of this task ) only the contractile behavior of the rectus femoris is affected because it is a biarticular muscle that also crosses the hip . The contribution of rectus femoris to the moment underlying dynamic knee extension and corresponding energy consumption is substantially smaller than the other extensor muscles . In the model , rectus femoris has a similar moment arm about the knee with the other knee extensors , but its peak isometric force , hence moment generating capacity was by far the lowest ( 42–63% of each of the other muscles ) . Its mass is also substantially lower so its highest possible rate of energy consumption is similarly lower . Furthermore , its fascicle length over the range of motion of the task was relatively small; it was at most 80% of optimal length at the highest hip extension tested while the vasti lengths were 90 to 110% of optimal over the majority of the range . When muscles are operating at lengths below optimal , then the smaller the length , the lower the force generating capacity and metabolic economy ( see S3 Appendix and [38] ) . Predictions of VO2 across tested knee motion ranges were within one standard deviation of the experimental mean . Changing knee extension range changes the knee gravitational and inertial moments of the task , hence the muscle moment and metabolic energy required . Varying the lower limit of knee extension range by +/- 10° had a small effect on VO2 predictions because the resulting changes in gravitational and inertial moments had opposite effects on the muscle moment required . For example , reducing the lower limit of knee angle range from 90° extension , where the shank is nearly vertical and gravitational moment is minimal , generates a gravitational moment over the added range of motion that is in the direction of knee extension . This reduces the muscle moment required to decelerate the knee during the terminal phase of knee flexion and to accelerate it during the initial phase of knee extension . At the same time , this reduction of the lower limit of knee extension increases the range of motion , hence the acceleration and inertial moment that the muscles need to overcome . Varying the upper limit of knee extension range resulted in larger effects on VO2 predictions because the resulting changes in gravitational and inertial moment both either increased or decreased the necessary muscle moment . Furthermore , increasing the upper limit of knee extension resulted in relatively larger changes in VO2 predictions than reducing the upper limit of knee extension partly because this knee angle of 175° is near the anatomical limit of knee extension where passive tension of knee flexors is maximal . Furthermore , the knee extensor lengths decrease over this added range of motion , which as explained in S3 Appendix reduces their metabolic economy . Shorter muscle lengths also reduce the force generating capacity so producing the required level of force for the task requires recruitment of additional fast twitch motor units , which would reduce metabolic economy even further . The large energy consumption and fatigability associated with this high upper limit of knee extension angle along with the possible discomfort makes it unlikely that subjects actually extended their knee this far during the experiment . Pulmonary VO2 predictions of the least and most metabolically economical musculoskeletal configurations were within one standard deviation for all work rates tested ( see Fig 6b ) . The range of VO2 predictions was substantially higher for 11 and 23 . 5W than 0W because the least economical configuration consumed substantially more energy at these higher work rates . The relatively low moment generating capacity of the least economical configuration ( Fig 7 ) and smaller percentage of slow twitch fibers required higher activation of the less economical fast twitch fibers to perform 11 and 23 . 5W exercise . For 0W exercise , the least economical musculoskeletal system could perform the task by recruiting mostly slow twitch fibers like the nominal and most economical musculoskeletal configurations . Nominal predictions were close to the center of the experimentally measured range of Andersen et al . [21] and variations in model parameters led to reasonable variations of model predictions that remained within one standard deviation of the experimental mean . This is consistent with the fact that the musculoskeletal model was designed to represent an average , young , and healthy male and that experimental measurements were made on a large number of male subjects ( 18 individuals ) with a diverse background of physical activity and a large age range ( 21–47 years old ) . The nominal predictions were not as centered on the experimental range of Andersen and Saltin [22]; they generally occupied the lower portion instead . Interestingly , the five healthy men that participated in that experimental study were not as diverse , as they had a narrower age range ( 21–29 years old ) and were all relatively fit . Clearly , the experimental range is substantially larger than the range of predictions made by the model . Experimental measurements outside the range of one standard deviation could reflect subjects with musculoskeletal economy and knee angle trajectories that both bias VO2 to either higher or lower levels . Additional parameters such as limb dimensions , inertial properties , fitness and training level also differed across experimental subjects , but were not varied in the model . The substantial variability of muscle recruitment strategies that emerged in the modeling study has also been observed across experimental subjects [39] , although total energy consumption was similar [21] . This is likely a result of having multiple knee extensor muscles with similar moment generating capacity and metabolic economy . For a musculoskeletal system like the one studied here , many different muscle recruitment strategies can lead to similar motion as well as overall metabolic energy consumption . The precise strategy adopted depends on other factors such as the subjects' experience with the task and their perception of the goal . Subjects have their own criteria for performing the task in addition to the explicit instructions given . Some subjects may be more reluctant to change the way they perform a task , in which case the recruitment strategy they end up with would depend strongly on their unique experience . In addition , some muscle recruitment strategies may involve low energy consumption but disproportionate use of muscles could lead to fatigue and associated discomfort rapidly . Subjects are less likely to adopt these muscle recruitment strategies if the duration of the task is long enough to lead to fatigue or if they have a low tolerance for discomfort . To our knowledge , existing models of muscle energetics [13–15] do not account for important physiological processes that underlie both consumption of metabolic energy to fuel contractions as well as the metabolic energy required to replenish that fuel . As shown in S4 Appendix , these physiological processes have a large influence on metabolic energy consumption and can result in substantial prediction errors if modeled improperly . The model used in this validation effort does not have these limitations and generated predictions within subject variability . Given the model's accurate predictions of metabolic energy consumption , the underlying assumption that subjects minimized energy while performing the task seems reasonable . In general , people likely minimize energy consumption to conserve fuel for subsequent movements and all other active processes in the body , unless additional factors limit performance . If the goal is to learn the new task quickly , e . g . for survival or competition , minimizing energy would be less of a concern . If peripheral fatigue is a limiting factor , then the probability of fatigue would be minimized instead . Minimizing fatigue may lead to different predictions of muscle recruitment , force , and energetics than simply minimizing energy consumption [40] . Identifying the mechanisms of physical performance decrement requires a better understanding of the interactions among the physiological systems that generate movement . The rate at which the muscular system consumes energy ( in the form of ATP ) to drive the necessary muscle contractions largely determines these interactions and can be used to quantify them . Metabolic energy consumption is captured at the muscle level by the model in Tsianos et al . [17] and the results presented here provide additional support for its validity , as the model successfully linked the forces necessary to perform the task to the metabolic energy required . The model predicts the rate of energy use required to fuel contractions , which is proportional to the rate of ATP consumption . Model estimates of ATP consumption rate can be used to determine the availability of ATP for subsequent contractions and nutrients for replenishing the ATP used . The model of ATP consumption can also be used to estimate chemical byproducts of contraction , such as inorganic phosphate , that are known to induce muscle fatigue . The type of nutrients and metabolic pathways used is a function of ATP consumption rate [41] , so the model can help predict nutrient depletion for different exercises . This also helps predict the extent of glycolytic metabolism , hence H+ levels in muscle that can contribute to fatigue . Model predictions of metabolic energy can be used to compute that amount of energy that is not converted to mechanical work , which is dissipated as heat . Estimates of muscle heat output can be used to determine increases in core body temperature that could lead to hyperthermia , hence performance decrement . ATP consumption determines the amount of nutrients and oxygen that must be supplied by the cardiovascular system . The model can therefore help determine if a given physical task can be supported by the cardiovascular system . Moreover , the amount of oxygen that would need to be absorbed from the environment would also depend on the resistance of the blood vessels supplying the working muscles , which itself is closely related to metabolic demand [42] . Maintaining blood oxygenation also depends on respiratory function that can be affected by environmental factors such as reduced concentration of oxygen in the inspired air or the presence of gases that inhibit oxygen absorption in the blood . Because the oxygen demanded for a task is closely related to the ATP required , the model can be used to assess when lung function limits performance . Metabolic demand is not monitored in many experimental studies , but can be inferred using this model to assist interpretation of the results and help expand our knowledge of the relationship between metabolic demand and other physiological processes . Even when metabolic demand is monitored , it can be highly inaccurate . Experimentalists typically measure oxygen uptake rate ( VO2 ) , which can only be used to infer the portion of energy stores consumed that was replenished via oxidative metabolism . Glycolytic metabolism is highly active during intense exercise or in hypoxic conditions [43]; therefore , estimates based on VO2 for these situations would be inaccurate . Even if blood lactate is measured , it is difficult to relate it to the extent of glycolytic metabolism because lactate is constantly absorbed by other tissues in the body that ultimately use it as a fuel source or convert it back to glucose [44] . By contrast , the model used here is based on thermodynamic experiments that characterized energy consumption related to contractile processes directly; therefore , its estimates of contractile fuel use do not depend on the type of metabolic pathways involved . This paper presents the first valid demonstration of using a muscle contraction model to make accurate predictions of metabolic energy consumption associated with submaximal effort movement . The same modeling approach will likely lead to good predictions across many other tasks and conditions because it accounts for the energetics of individual muscles , it was shown to make valid predictions using only information about the task , and its internal parameters were not tweaked to match experimental results . The results provide additional support for the validity of the muscle energetics model used in this study [17] , which is a good starting point for modeling muscle fatigue and nutrient depletion . The modeling approach presented here is useful for relating tasks to the activity of the various physiological systems that are intimately linked with metabolic demand . Using the model and the known functional capacities of the various physiological systems involved , their ability to meet the demands of nonstereotypical or untested tasks and conditions can be investigated . Such integrated analysis would provide insight into the demands placed on each system under a wide range of situations and would therefore help generate testable hypotheses of performance decrement mechanisms .
|
Muscles consume metabolic energy to generate movement . Performing a movement over a long period of time or at a high intensity strains the respiratory and cardiovascular systems that need to replenish the energy reserves in muscle . Furthermore , consuming and replenishing metabolic energy involves biochemical reactions with byproducts that cause muscle fatigue . These biochemical reactions also produce heat that increases body temperature , potentially causing central fatigue . A model of muscle metabolic demand is therefore necessary for predicting and understanding the interaction of these factors that could limit performance , but currently no model exists for arbitrary physical tasks . In this study , we developed a model of metabolic demand by integrating a recently developed and validated model of muscle energetics into a musculoskeletal model . We showed that model predictions for leg exercise over a wide range of intensities were well within the experimental variability reported in the literature . To our knowledge , the muscle energetics model is the first to make valid predictions of metabolic demand at both the muscle and task level . The model is an important step toward understanding and planning around physical performance decrement , which is particularly useful for rehabilitation , competitive sports , and the military .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"knees",
"medicine",
"and",
"health",
"sciences",
"legs",
"classical",
"mechanics",
"oxygen",
"limbs",
"(anatomy)",
"physiological",
"processes",
"optimization",
"mathematics",
"bioenergetics",
"kinematics",
"musculoskeletal",
"system",
"chemistry",
"energy",
"metabolism",
"physics",
"biochemistry",
"anatomy",
"physiology",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"metabolism",
"chemical",
"elements"
] |
2016
|
Validated Predictions of Metabolic Energy Consumption for Submaximal Effort Movement
|
Copper ( Cu ) is an important enzyme co-factor that is also extremely toxic at high intracellular concentrations , making active efflux mechanisms essential for preventing Cu accumulation . Here , we have investigated the mechanistic role of metallochaperones in regulating Cu efflux . We have constructed a computational model of Cu trafficking and efflux based on systems analysis of the Cu stress response of Halobacterium salinarum . We have validated several model predictions via assays of transcriptional dynamics and intracellular Cu levels , discovering a completely novel function for metallochaperones . We demonstrate that in addition to trafficking Cu ions , metallochaperones also function as buffers to modulate the transcriptional responsiveness and efficacy of Cu efflux . This buffering function of metallochaperones ultimately sets the upper limit for intracellular Cu levels and provides a mechanistic explanation for previously observed Cu metallochaperone mutation phenotypes .
Copper ( Cu ) is an essential trace element in nearly all biological systems [1] but highly cytotoxic when in excess [2] , [3] . Biological systems possess sophisticated trafficking systems that include ion importers to control Cu entry [4]; metallochaperones for shuttling intracellular ions to and from targets[5] , [6]; efflux pumps that export excess Cu [7] , [8]; and metalloregulators that sense internal abundance and modulate expression of all trafficking proteins [9] . There is a large body of literature on how Cu enters and exits the cell [10] , [11]; the kinetic and structural details of Cu translocation between trafficking , sensing , metabolic , and pumping proteins [12] , [13] , [14]; and phenotypes associated with defects in metalloregulatory and efflux functions [15] . Defects in Cu efflux pumps , for example , are associated with neurodegenerative disorders such as Menkes and Wilson's syndromes , and Alzheimer's disease [7] , [16] , [17] , [18] , [19] . Although we do not fully understand the mechanistic role of metallochaperones in any Cu-related disease , they are universally present [20] , [21] , [22] , [23] , [24] , [25] , [26] and known to be important in Cu-trafficking and preventing cellular damage [27] , [28] . Furthermore , deletion of the Cu metallochaperone Atox1 in immortalized human cell lines resulted in elevated intracellular Cu levels along with overexpression of Cu transporting ATP7A [19] , [29] . Mice harboring metallochaperone deletions are malformed with high mortality [30] . Combined , these data suggest that beyond their known Cu trafficking role , metallochaperones also influence the activity of metalloregulators and intracellular Cu levels via an uncharacterized mechanism . In this study we present an integrated experimental and computational analysis of transcriptional regulation of Cu efflux in Halobacterium salinarum NRC-1 and the role that metallochaperones play therein . In brief , H . salinarum possesses a post-transcriptionally regulated Cu trafficking system that was characterized by systems analysis of cellular response to growth sub-inhibitory levels of extracellular Cu . The main components of the Cu efflux network in H . salinarum include a Cu sensing metalloregulator ( VNG1179C ) that regulates transcription of a P1-type ATPase efflux pump ( VNG0700G ) , and two metallochaperones ( VNG0702H and VNG2581H ) with Cu binding sites that are highly conserved across all domains of life ( Figure S1 ) . We integrated the functional interplay between these components into a system of ordinary differential equations with iterative refinement of model parameters to fit experimental data of Cu response dynamics . Using this model we explored the consequence of deleting or overexpressing metallochaperones on activity of VNG1179C ( assayed directly by measuring VNG0700G transcript levels , or indirectly by measuring fluorescence in live cells transformed with a GFP reporter tagged to the promoter of VNG0700G ) and ultimately on intracellular Cu levels ( measured with ICP-MS ) . The three rounds of iterative experimentation and computation has revealed that each of the two metallochaperones in H . salinarum have distinct functions , and that their interactions with other components of Cu efflux acts as a buffer , setting the upper threshold of homeostatic intracellular Cu . Altering the absolute abundance of metallochaperones significantly affects sensitivity of the metalloregulator to Cu levels , efficacy of Cu efflux by VNG0700G , and ultimately results in higher level of intracellular Cu .
Glycerol stocks of H . salinarum with requisite gene knockouts were revived on solid CM ( NaCl–250 g/l , MgSO4•7H2O–20 g/l , Na·Citrate–3 g/l , KCl-2 g/l , and peptone 10 g/l ) agar ( 1 . 8% w/v ) plates incubated at 37°C for 1–2 wks . Single colonies were selected from plates and placed into liquid CM media ( typically 50 ml in a 125 ml Erlenmeyer flask unless otherwise noted ) and grown to an optical density ( measured at 600 nm ) ( OD600 ) of 0 . 6–0 . 8 to create stock cultures . Prior to experiments , stock cultures were split into replicates and diluted in fresh medium to a starting OD600 of 0 . 05 . Cu addition experiments were initiated once replicate cultures reached OD600 of 0 . 4–0 . 6 . Strains carrying GFP expression vectors required medium supplemented with 200 ng/ul mevinolin . Mutant strains harboring deletions of VNG1179C , VNG0702H , and VNG2581H were created via a two step in-frame deletion method previously described [31] , [32] using a Δura3 strain as the parent background . Strains studied were grown in liquid cultures 250 ml in volume ( 500 ml flask ) at 37°C to accommodate 4 ml samples taken at specified timepoints . Total RNA was purified from cell pellet lysates , stained , and hybridized to a spotted microarray using a previously reported dye-flip protocol [31] . Transcriptome expression data presented herein is original to this work . Metal free basal salts solution ( NaCl–250 g/l , MgSO4•7H2O–20 g/l , Na·Citrate–3 g/l , KCl-2 g/l ) and MilliQ water was created by overnight treatment with 5 g/l Chelex . For metal free basal salts solution , metal pure MgSO4 ( <0 . 001% metal impurity ) was added after Chelex treatment to avoid saturating the ion binding capacity of the resin . Prior to experiments , all glassware and sample collection tubes were washed twice in 2% nitric acid to strip any trace metals . For ICP-MS analysis , 10 ml samples were retrieved from cultures pre- and post- copper addition into 50 ml conical tubes . Cells were pelleted by centrifugation and washed three times in basal salts solution . After the final wash , cells were lysed in MilliQ water and sonicated for 10 min and stored at 4°C for analysis . Samples were analyzed by ICP-MS using minor modifications to previously published protocols [33] . GFP timeseries experiments were conducted using 50 ml cultures seeded with clonal populations of cells from solid medium colonies . Once cultures reached an OD600 of 0 . 4–0 . 6 , CuSO4•5H2O was spiked in to a final concentration of 0 . 85 mM . At selected timepoints , 500 ul samples were taken from cultures and pelleted . Cells were then fixed by resuspending in 1 ml of basal salts solution with 0 . 25% ( w/v ) paraformaldehyde and incubated at room temp for 10 min . Fixative was removed by pelleting cells and resuspending in basal salts solution and samples were stored at 4°C . GFP Cu dose experiments were conducted in 96 deep-well plates ( 1 . 6 ml culture per well ) . Cultures were started in 500 ml flasks and distributed to wells at a nominal OD600 of 0 . 05 . Plates were sealed with BreathEasy film and incubated at 37°C in a shaking incubator set to 200 rpm . Once the average OD600 of the plate reached 0 . 4–0 . 6 , a pre- Cu addition sample was taken and processed as below . Afterwards , CuSO4•5H2O was spiked in and the plate was incubated for 300 min at 37°C , shaking at 200 rpm , at which time an endpoint sample was taken . For each sample , 150 ul of culture was removed from each well and transferred to a v-bottomed 96-well plate prealiquoted with basal salts solution with 0 . 5% ( w/v ) paraformaldehyde . Cells were pelleted in a tabletop centrifuge at 2800G for 10 min at room temp . Supernatant fixative was removed and cells were resuspended in basal salts solution . Plates were then sealed with aluminum foil tape stored at 4°C until analysis . Flow cytometry analysis was performed within 2–3days of sample collection using a BD InFlux cell sorter fitted with a 100 um flow nozzle and primed with 100 g/l NaCl in MilliQ water as sheath . Prior to injection , each sample was spiked with 1 um yellow/green fluorescent beads to a final concentration of 1×107 ml−1 for use as an internal reference . 100 , 000 total events were collected and population gating was done via FlowJo . Gated cell populations were exported to ASCII files and further analyzed using the statistical analysis environment , R ( http://www . r-project . org ) . In R , fluorescence concentrations were calculated by dividing each event's GFP signal by its corresponding forward scatter value . Population concentration means resulting from this analysis are presented in the results .
When stressed with excess Cu , activation of efflux mechanisms protects the cell by restoring intracellular Cu to homeostatic levels . To determine the temporal dynamics of transcription of all genes involved in Cu efflux , we performed a global time course survey of transcript level changes in cells stressed with 0 . 85 mM CuSO4 , a previously determined growth sub-inhibitory Cu concentration [31] . To rid the cell of excess Cu , we discovered that H . salinarum relies primarily on transcriptional activation of three genes: a P1-type ATPase efflux pump yvgX ( VNG0700G ) , and two HMA-domain containing metallochaperones VNG0702H and VNG2581H . The temporal transcript changes for each of these genes demonstrated pulsed induction following Cu exposure ( Figure 1 ) . These genes were also previously determined to be regulated by VNG1179C [31] , a Lrp family transcription factor with a TRASH domain for metal sensing [36] . The expression of this regulator did not change under Cu stress suggesting that it is post-translationally activated – e . g . upon binding Cu . The pulse-like transcriptional response of yvgX was expected due to its direct role in relieving the cell of excess intracellular Cu . Conversely , the pulse in metallochaperone expression was unexpected . Absent information on transcriptional dynamics of metallochaperones , we had initially assumed that they were constitutively expressed at low levels . However , the regulated transcription of these genes in direct response to excess Cu suggested that the metallochaperones might have an important role in tuning the transcriptional dynamics of the Cu efflux network . Notably , negative feedback linking activity of YvgX to repression of VNG1179C must exist to conserve cellular resources . The mechanisms that enable such feedback are most likely indirect as YvgX is membrane bound and VNG1179C is cytoplasmic and bound to DNA . Our hypothesis was that feedback must occur via metallochaperones because of their ability to interact directly with Cu ions , VNG1179C , and YvgX . Based on existing mechanistic understanding from diverse organisms [11] , [37] , [38] , our prior work [31] , and the dynamics of the transcriptional response investigated in this study , we developed a computational model for the transcriptional regulation of Cu efflux ( Figure 2A ) . A system of ordinary differential equations that describe transcriptional , translational , and post-translational regulatory events , this model ( Model 0 ) makes two important assumptions based on known biology . First , intracellular Cu is rarely unchelated or “free” [25] , but instead is readily bound by metallothioneins [9] , [13] , [39] , glutathione [40] , [41] , and other Cu binding proteins [2] . We modeled this Cu sequestration capacity with a quota element ( Q ) whose demand is fulfilled prior to activation of Cu efflux . Second , we assumed that the two metallochaperones VNG0702H and VNG2581H were functionally indistinguishable given the high level of similarity in both their expression profiles and Cu binding motifs . Therefore , metallochaperones were simulated as a single species with two-fold higher copy number than other elements . We tested this model by performing simulations of the Cu response to a step-increase in extracellular Cu to a growth sub-inhibitory level ( 0 . 85 mM ) . The model assumes that a single cell only reacts to a shell of surrounding volume equivalent to 3× the cell volume . A concentration of 0 . 85 mM of dissolved copper therein is equivalent to ∼5×105 molecules . This value is held constant throughout simulations , representing non-changing copper supply in the bulk growth medium and presenting a boundary condition for copper at the cell membrane . All simulations were run until 300 min to give sufficient time for restoration of regulated Cu homeostasis in normal ( wild-type or wt ) cells . The simulations accurately recapitulated known dynamics of transcriptional induction of yvgX and metallochaperones with transcript and protein levels peaking at ∼18 and 200 min , respectively ( Figures 2B and 2C ) . Based on these encouraging results we proceeded to explore the functional and mechanistic role of metallochaperones in regulation of Cu efflux . First , we investigated whether changes in abundance of metallochaperones had any consequence on expression of yvgX and , ultimately , on intracellular Cu concentration . The model predicted that the metallochaperones had to be within an optimal range of 100–1000 molecules per cell to produce 100–200 copies of YvgX for maintaining low levels of intracellular Cu ( Figure 2D ) . Interestingly , increasing or decreasing the concentration of metallochaperones outside this optimal range had significantly different effects on steady state levels of YvgX and intracellular Cu . Lowering the abundance of metallochaperones below 100 molecules per cell resulted in increased levels of both intracellular Cu and YvgX . In contrast , increasing the metallochaperones to above 1000 molecules per cell resulted in Cu accumulation with undetectable YvgX levels . Next , we investigated the consequence of increasing or decreasing metallochaperone abundance on sensitivity of the Cu efflux response over a wide range of extracellular Cu concentrations . The model predicted significant differences in the threshold concentrations of Cu that were necessary to activate expression of YvgX in presence of low , normal , and high abundance of metallochaperone ( Figure 2E ) . Depletion of metallochaperones was predicted to significantly increase the sensitivity of the response , with steady state YvgX levels rising to 1000 copies per cell in the presence of micromolar quantities of external Cu . In contrast , overexpression of metallochaperones was predicted to repress YvgX expression over almost the entire range of extracellular Cu . Thus , the model predicted that metallochaperones tune responsiveness of the metalloregulator , modulate the absolute abundance of the efflux pump , and , ultimately , set the homeostatic level of Cu . In Model 0 we made the assumption that both metallochaperones had identical functions and , therefore , provided equivalent Cu trafficking and buffering capacity . Alternatively , there could be differences in the specific roles of each metallochaperone . Eukaryotes make use of several Cu-specific metallochaperones: ATOX1 , CCS1 , and COX17 . ATOX1 , the eukaryotic ortholog of metallochaperones in H . salinarum , trafficks Cu to P1-type ATPases ATP7A and ATP7B [29] while CCS1 delivers Cu to Cu/Zn dismutases and COX17 delivers Cu to cytochrome oxidases [27] . This raises the possibility that there might be similar distinction in Cu trafficking by the two metallochaperones in H . salinarum . To investigate if this was indeed the case , we revised the model to include both VNG0702H and VNG2581H as independent elements . We first incorporated subtle differences in trafficking functions ( Model 1 . 1 , Figure 4A ) . Specifically , both metallochaperones were capable of directly binding intracellular Cu , distributing Cu to Q , activating VNG1179C by allocating excess Cu to the metalloregulator , and trafficking excess Cu to YvgX . However , only one metallochaperone was ideally suited for each task , while the other was 10-fold less efficient , resulting in “strong” and “weak” interactions , respectively . In a revised version of this new model , we incorporated distinct functions for each metallochaperone ( Model 1 . 2 ) ( Figure 4B ) . In this model , Cu trafficking by VNG0702H is restricted to YvgX , while VNG2581H is responsible for Cu allocation to all remaining targets including VNG1179C and Q . Importantly , VNG2581H was modeled as the only metallochaperone that actively binds free intracellular Cu ions , thus production of Cu-bound VNG0702H requires a hand-off of Cu from VNG2581H , an inter-metallochaperone interaction that has been previously observed in eukaryotes [27] . Thus , Models 1 . 1 and 1 . 2 represent alternate extremes for functions of the two metallochaperones . Eliminating one of the two models would ascertain whether the two metallochaperones have interchangeable ( Model 1 . 1 ) or distinct ( Model 1 . 2 ) functions in intracellular Cu trafficking and buffering . The two models made significantly different predictions of intracellular Cu levels at steady state under Cu stress . Model 1 . 1 predicted increased Cu levels in all single and double chaperone mutant backgrounds ( overexpression and deletion ) , except Δ0702Δ2581 , in which intracellular Cu was predicted to be at wt levels ( Figure 4C ) . In contrast , Model 1 . 2 predicted increased Cu levels in all mutants except when only VNG0702H is overexpressed ( Figure 4D ) .
In absence of metallochaperones , trafficking of Cu is completely disrupted . When subjected to Cu stress , intracellular Cu concentration increases and eventually overcomes diffusional limitations to activate VNG1179C and increase transcription of yvgX . Despite its increased levels , YvgX is unable to receive Cu efficiently to perform its efflux function . Consequently , Cu levels are perpetually increased and VNG1179C remains locked in an activated state to constitutively drive the expression of yvgX . Importantly , it is the disrupted trafficking of Cu to YvgX that is ultimately responsible for similar consequences in both the single and double metallochaperone deletion mutants . At the other end of the spectrum , when abundance of metallochaperones is increased , activation and deactivation of VNG1179C proceeds normally , and as a result we do not observe significant differences in transcriptional dynamics of yvgX . However , intracellular Cu level rises because of the increased number of Cu-binding sites in the overexpressed metallochaperones . Importantly , we observe this only with overexpression of VNG2581H; as increased VNG0702H expression also increases Cu efflux by trafficking to YvgX . Thus , the interplay between metallochaperones with distinct trafficking roles is critical for modulating transcriptional responsiveness and efficacy of Cu efflux . We have demonstrated that this system of interactions among metallochaperones and their targets sets an upper threshold for intracellular Cu levels . As a result , biological systems are under stringent selection pressure to maintain a fine balance in the activity of metallochaperones and their abundance . Changes to either can significantly affect responsiveness of the metalloregulator to modulate transcriptional dynamics of the efflux pump , and , ultimately , alter the homeostatic intracellular level of Cu . In conclusion , while mathematical modeling of Cu trafficking has been performed previously [45] , what is unique about this study is that it incorporated three iterations of experimentation and computation to refine model architecture and parameters ( Figure 5 ) . The modeling incorporated actual experimental measurements , recapitulated known dynamics , and predicted new dynamics , properties , and functions that were experimentally validated . Similarly , the experimental validations included microarray analysis to assay global transcriptional dynamics of the Cu response , GFP-based reporter assays to measure high resolution transcriptional dynamics of the Cu efflux pump , and ICP-MS measurements of intracellular Cu levels . This iterative computation and experimentation strongly supports a novel buffering role for metallochaperones to mechanistically explain the cause for elevated intracellular Cu levels and overexpression of the ATP7A efflux pump in cell lines harboring ATOX1 mutations [19] . Ultimately , we have presented a quantitative model that explicitly demonstrates the role of metallochaperones in regulating intracellular Cu , a contribution that is novel to the field of metal biology . Indeed , additional iterations of experimentation and computation are necessary to further refine this model and reveal new insights .
|
Copper ( Cu ) toxicity is a problem of medical , agricultural , and environmental significance . Cu toxicity severely inhibits growth of plant roots significantly affecting their morphology; Cu overload also accounts for some of the most common metal-metabolism abnormalities and neuropsychiatric problems including Wilson's and Menkes diseases . There is a large body of literature on how Cu enters and exits the cell; the kinetic and structural details of Cu translocation between trafficking , sensing , metabolic , and pumping proteins; and phenotypes associated with defects in metalloregulatory and efflux functions . Although the role of metallochaperones in Cu-cytotoxicity has been poorly studied , it has been observed that in animals deletion of metallochaperones results in elevated intracellular Cu levels along with overexpression of the P1-type ATPase efflux pump , ultimately causing malformation with high mortality . These observations are mechanistically explained by a predictive model of the Cu circuit in Halobacterium salinarum , which serves as an excellent model system for Cu trafficking and regulation in organisms with multiple chaperones . Constructed through iterative modeling and experimentation , this model accurately recapitulates known dynamical properties of the Cu circuit and predicts that intracellular Cu-buffering emerges as a consequence of the interplay of paralogous metallochaperones that traffic and allocate Cu to distinct targets .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"systems",
"biology",
"biochemical",
"simulations",
"microbial",
"physiology",
"regulatory",
"networks",
"biology",
"computational",
"biology",
"microbiology"
] |
2013
|
Metallochaperones Regulate Intracellular Copper Levels
|
Few experimental studies have examined the role that sexual recombination plays in bacterial evolution , including the effects of horizontal gene transfer on genome structure . To address this limitation , we analyzed genomes from an experiment in which Escherichia coli K-12 Hfr ( high frequency recombination ) donors were periodically introduced into 12 evolving populations of E . coli B and allowed to conjugate repeatedly over the course of 1000 generations . Previous analyses of the evolved strains from this experiment showed that recombination did not accelerate adaptation , despite increasing genetic variation relative to asexual controls . However , the resolution in that previous work was limited to only a few genetic markers . We sought to clarify and understand these puzzling results by sequencing complete genomes from each population . The effects of recombination were highly variable: one lineage was mostly derived from the donors , while another acquired almost no donor DNA . In most lineages , some regions showed repeated introgression and others almost none . Regions with high introgression tended to be near the donors’ origin of transfer sites . To determine whether introgressed alleles imposed a genetic load , we extended the experiment for 200 generations without recombination and sequenced whole-population samples . Beneficial alleles in the recipient populations were occasionally driven extinct by maladaptive donor-derived alleles . On balance , our analyses indicate that the plasmid-mediated recombination was sufficiently frequent to drive donor alleles to fixation without providing much , if any , selective advantage .
An open question in microbial evolution is why some bacterial taxa seem to have extensive intergenomic recombination [1] while others seem to have little [2] . Some symbiotic bacteria appear to be entirely clonal owing to their tight associations with hosts that preclude contact with other lineages [3] , but otherwise the reasons for differences in recombination rates across bacterial taxa are unclear . Intergenomic recombination can break the linkage between particular beneficial or deleterious mutations and the rest of the genome . Under conditions of high recombination and strong selection , individual genes , rather than entire genomes , can thus go to fixation . When recombination is infrequent or absent but selection is strong , highly beneficial mutations can drive large genomic regions or even whole genomes to fixation . Recent work shows that both gene-specific and genome-wide selective sweeps occur in microbial communities [4] . In contrast to meiotic recombination , however , bacterial recombination replaces a recipient allele with a donor allele , rather than swapping homologous regions between chromosomes . Thus , recombination in bacteria does not necessarily break up linkage disequilibrium across distant sites on the recipient chromosome; instead , it may preserve a “clonal frame” over most of the chromosome , interrupted by stretches of DNA introduced by horizontal gene transfer [5] . Horizontally transmitted viruses and conjugative plasmids mediate recombination in many species of bacteria [6] . In this context , recombination is a special kind of evolutionary process . Like mutation rates , intergenomic recombination rates can evolve because many gene products involved in DNA replication and repair affect the rates of mutation and recombination [7 , 8] . But unlike mutation rates , recombination rates in bacteria are also subject to coevolution owing to the association with plasmids and viruses [9] . Intergenomic recombination qualitatively changes evolutionary dynamics [10 , 11] and can speed up adaptive evolution compared to asexual controls under some circumstances [12 , 13] . However , recombination—especially as it occurs in bacteria—may have originated as a byproduct of the spread of selfish elements rather than as a means of increasing the efficiency of natural selection in the genome as a whole [14] . The conjugative element relevant to our study is called the F plasmid . It encodes proteins that form a pilus between an F+ donor cell and an F−recipient cell . F also encodes proteins that transfer the plasmid ( and sometimes host DNA ) to the recipient after cell-cell contact has been made . Strains that have the F plasmid integrated into their chromosome are called Hfr ( high frequency of recombination ) strains . The F plasmid can sometimes spontaneously excise from the Hfr chromosome; if a piece of the host chromosome is excised along with the F plasmid itself , then those cells are called F’ to indicate that the plasmid also contains bacterial DNA . The location and orientation of the oriT site in the chromosome of an Hfr strain determines the order and timing of the transfer of donor DNA into the recipient cell . Sequences close to oriT and in the proper orientation are transferred first , whereas sequences far from oriT are transferred only later if at all [15 , 16] . In this paper , we revisit an evolution experiment in which sexual recombination did not appear to increase the efficiency of natural selection , nor did it speed up adaptation , contrary to some hypotheses about the evolutionary advantages associated with sex . Souza , Turner , and Lenski [17] conducted an experiment in which they periodically introduced an equal mixture of 4 Hfr strains of Escherichia coli K-12 that could donate genetic material but not grow ( owing to mutations that caused nutritional deficiencies ) into 12 recipient populations of E . coli B , each founded by single clones that had previously evolved in and adapted to a glucose-limited minimal medium for 7000 generations . Each Hfr strain had an F plasmid inserted at a different position and orientation in its chromosome , and the same mix of 4 Hfr strains was used for all 12 recipient populations throughout the experiment . We refer to the recombination treatment in that study as the Souza-Turner-Lenski experiment ( STLE ) and to the experiment that generated the asexual recipients as the long-term evolution experiment ( LTEE ) . The stated goal of the STLE was to test the hypothesis that recombination would increase the rate of adaptation by increasing the genetic variation available to natural selection [17] . In the absence of complex selection dynamics ( such as frequency-dependent selection ) , the expected rate of adaptation of a population is proportional to the genetic variance in fitness in a population [18] . The outcome of the STLE was unexpected in that the recombination treatment increased genetic variation ( as determined by tracking ~10 genetic markers available to the authors at that time ) , but it had no significant effect on the rate of adaptation compared to control populations that evolved without recombination . Souza et al . ( 1997 ) proposed three hypotheses that could explain their puzzling results . These hypotheses are not mutually exclusive . According to one , the recombination treatment was so effective that it acted like a strong mutational force , replacing many neutral alleles ( or even overwhelming selection , if donor alleles were maladaptive ) through the sheer flux of donor DNA into the recipient genomes . According to another , some donor alleles hitchhiked to high frequency with beneficial ‘driver’ mutations that arose in the recipient populations . Lastly , the interactions between donor and recipient genes , and the ecological context in which those interactions occurred , might have somehow generated strong frequency-dependent selection , such that the assays used to measure fitness gains were undermined . In fact , one recombinant population of the STLE actually appeared to decline substantially in fitness , an effect that was shown to reflect an evolved frequency-dependent interaction [19] . However , this effect was tested in only one population , and no evidence bearing on the other two hypotheses was available . In this study , we characterize the genomic evolution that occurred during the STLE , with the aim of resolving the puzzling results of the STLE . On balance , we find substantial evidence that the recombination treatment acted like a greatly increased mutational force .
Fig 1 summarizes the rich and complex information on genomic changes that occurred during the 1000-generation STLE . Two clones were sampled at random from each of the 12 populations in the recombination treatment , and their genomes were sequenced and analyzed along with those of the donors and LTEE-derived recipients . The odd-numbered clones from each population are shown in Fig 1; the even-numbered clones are shown in S2 Fig . The genomic sites marked in blue show mutations that distinguish the 12 clones that were used as recipients in the STLE from the ancestor of the LTEE , and which were still present in the genome of a recombinant clone from the STLE . These mutations thus arose during the 7000 generations of the LTEE that preceded the start of the STLE , and they persisted for the 1000 generations of the STLE . The clones from populations Ara+3 , Ara+6 , and Ara–2 have many more such mutations than the clones from the other nine STLE populations , because the recipient clones used to start these populations came from populations that had evolved hypermutable phenotypes during the early generations of the LTEE [20 , 21] . The genomic sites marked in yellow are mutations shared by the K-12 donor strains and the sequenced recombinant clone , but which were not present in the recipient clone used to start that population . These K-12 alleles were thus introduced by intergenomic recombination during the STLE . For comparison , Fig 2 shows the location and direction of the Hfr origins of transfer of the four K-12 donor strains , and the location of their auxotrophy mutations , which together explain much of the overall pattern of K-12 introgression . These differentially marked sites reveal several features . First , the majority of sites in most genomes derive from the recipients , not from the donor strains . In fact , an individual clone from population Ara–6 ( Fig 1 ) appears to lack any DNA regions that derive from the donors , and both clones from population Ara+2 only have one very short donor segment ( ~1 kbp in length , barely visible near –1 . 3 Mbp in Fig 1 ) . Second , there is one striking exception to the above pattern: the genomes of both clones from population Ara–3 are largely comprised of donor-derived DNA ( Fig 1 ) . Although similar to one another at a coarse-grained level , these clones still differ by more than 400 K-12 alleles . We also sequenced the genomes of two other clones ( REL4397 and REL4398 ) from this same population that were used in a previous study of frequency-dependent selection that demonstrated ecological differences between these clones [19] . They , too , are predominantly K-12 in origin , but with many small regions that descend from the LTEE recipient clone ( S3 Fig ) . Third , in most STLE populations , the pattern of introgression of donor DNA is very similar in the two recombinant clones we sequenced . However , there are differences in several cases including Ara–6 where , as noted above , one clone appears to lack any donor DNA; Ara+3 , where only one clone has any sizable regions of donor DNA; and Ara+4 and Ara–5 , where the pairs of recombinant clones share some regions of donor DNA but not others ( Fig 1 and S2 Fig ) . Fourth , there is an almost complete absence of donor DNA in all of the recombinant populations ( except Ara–3 , which has mostly donor DNA ) in a span of over 2 Mbp ( between about +1 . 8 to –1 . 0 Mbp on the circular genomic map ) . Fig 2A shows this point clearly as the sum of the number of introgressions of donor DNA into the odd-numbered clone from each STLE population , excluding the aberrant Ara–3 . This region does not reflect a paucity of mutations that could distinguish the donor-derived and recipient-derived DNA , which are as abundant there as elsewhere in the genome ( S1 Fig ) . Fig 2A also shows that donor DNA appears to be concentrated in two regions of the recombinant genomes . One region is centered at about –0 . 5 Mbp on the map , falling off more or less symmetrically on either side; the other donor-rich region peaks at ~1 . 1 Mbp to ~1 . 2 Mbp and extends farther to the left . In addition to yellow and blue marks that indicate donor- and LTEE-derived alleles , respectively , Fig 1 also has some black , light-purple , and red marks . The black marks indicate mutations that do not exist in either the donor pool or the recipient clone that was used to start a given population . These marks therefore indicate new mutations that arose during the 1000 generations of the STLE . Not surprisingly , there are many more new mutations in the three populations founded by the hypermutable recipient clones . The light-purple marks indicate regions that were deleted in the recombinant genomes . The red marks indicate mutations present in the LTEE-derived recipient clone but absent from the STLE-derived recombinant clone . These marks imply that the mutations that evolved at those sites during the LTEE were effectively “erased” , being replaced by donor alleles during the STLE . Most of these replaced mutations are surrounded by yellow , showing that they resulted from recombination . However , some of them are not surrounded by yellow; for example , the even-numbered Ara+3 clone has several red marks but no yellow marks ( S2 Fig ) . Nonetheless , these isolated red marks also probably reflect recombination , not mutation , given the experimental treatment with Hfr strains . Note that if there were no donor-specific markers closely adjacent to a small region of introgressed DNA , then we could not unambiguously assign a segment as donor-derived . Fig 1 also has symbols and , in some cases , labels showing the names of certain genes that underwent changes in a recombinant clone . ( S2 Fig shows the same information for the other recombinant clone from each population . ) The mutations are marked by symbols that are colored in a similar manner to the lines: blue symbols indicate mutations that were present in the LTEE-derived recipient and retained by the recombinant clone; black symbols are new mutations in the recombinant that were not present in either the recipient or the donor; and red symbols are mutations that were present in the recipient but absent from the recombinant , because they were replaced by donor DNA . A total of 32 different genes are labeled in one or more recombinant populations . They have been called out because they were probable targets of positive selection under the conditions of the LTEE . Table 1 provides additional information on each of them . These genes were previously identified ( along with others that did not undergo any changes in the recombinant clones in our study ) by sequencing a total of 264 genomes from the 12 LTEE populations at various times through 50 , 000 generations , and finding that they had accumulated an unexpectedly large number of independent nonsynonymous mutations in lineages that had not become hypermutable [21] . The G scores shown in Table 1 indicate the strength of the evidence for excessive parallelism in a gene , relative to the length of its coding sequence . For some of them , genetic manipulations and competition assays have directly confirmed that mutations indeed improve fitness under the conditions of the LTEE [22 , 23] . The STLE’s 1000-generation duration is short relative to the LTEE , and so we might not expect to see many new beneficial mutations rising to high frequency in these genes . Furthermore , the starting clones of the STLE had already evolved in and adapted to LTEE conditions for 7000 generations . However , we see many examples including four in STLE population Ara+1 in the fabF , trkH , hslU , and iclR genes and three in population Ara–4 in the topA , pykF , and hslU genes ( Fig 1 ) . These are also the two non-mutator populations that underwent the most replacements of LTEE-evolved mutations by donor alleles ( red hash marks ) , although we do not know whether this relation is coincidental or meaningful . In the next section , we consider the fate of the presumptively beneficial mutations that were present in the LTEE-derived recipients at the start of the STLE . What causes the variation across the genome in the extent of introgression of the Hfr donors’ DNA into the recombinant populations ? There are several distinct hypotheses that rely either on differences in the propensity for genomic regions to be transferred by the donors or on the fitness effects of integrating different regions into the recipient’s chromosome . These hypotheses are not mutually exclusive , and so two or more of them may contribute to the observed patterns of introgression ( Fig 1 and Fig 2 ) . Hypothesis 1: Some regions of donor DNA were transferred more often than other regions , leading to overrepresentation of the former regions in the recombinant genomes . Hypothesis 2: Some regions of donor DNA contained alleles that were beneficial to the recipient , leading to overrepresentation of those regions in the recombinant genomes . Hypothesis 3: Some regions of donor DNA contained alleles that were deleterious to the recipient , leading to underrepresentation of those regions in the recombinant genomes . This hypothesis can be subdivided into two variant hypotheses . According to Hypothesis 3A , the donor alleles were maladaptive regardless of the beneficial mutations that arose during the LTEE . According to Hypothesis 3B , the donor alleles were maladaptive specifically because the recipient genomes had acquired beneficial mutations in those regions during the 7000 generations of the LTEE that preceded the STLE . In addition to the hypotheses above , sequence divergence and mismatch repair are known to reduce recombination efficiency [24] . However , E . coli K-12 ( the strain that gave rise to the Hfr donors ) and B ( the strain from which the recipients derive ) are fairly closely related , at least as far as E . coli strains go . These two source strains were independently isolated from nature many years ago [25] . They differ by ~33 , 000 mutations ( using REL606 as a reference genome ) , which equals ~8 mutations per kilobase ( S1 Fig ) . Still , about half of their shared genes encode proteins that have identical amino-acid sequences [26] . On the other hand , several hundred genes are present in only one or the other strain , including so-called “genomic islands” that are thought to have been acquired by horizontal gene transfer in the phylogenetic networks leading to one or the other strain [26] . In addition to these more or less ancient differences , the four K-12 donors were deliberately modified by transposon mutagenesis to make them auxotrophic for different amino acids and by introducing the F plasmid at different locations into their chromosomes to make them Hfr strains; and , as described above , the B-derived recipients accumulated beneficial mutations during the LTEE . Overall , regions containing introgressed K-12 alleles in the STLE-evolved genomes were no more divergent than regions without K-12 alleles ( Kruskal-Wallis test , P = 0 . 9235 ) . However , we saw a positive correlation between sequence divergence and the location of recombination breakpoints ( Kruskal-Wallis test , P = 0 . 0199 ) ( S5 Fig ) . Therefore , it appears that sequence divergence had a weak effect on the introgression of K-12 alleles into the STLE populations , which mostly influenced the fine-scale mosaic structure of recombinant regions . Hypothesis 2 was , in essence , the original motivation for the STLE , with Souza et al . ( 1997 ) suggesting that intergenomic recombination with the K-12 donors might increase the rate of adaptation ( relative to control populations that evolved asexually ) by providing an additional source of genetic variation to the LTEE-derived populations . We lack a priori information about what sites in the K-12 donor genomes could provide beneficial alleles to the recombinant populations . However , if such sites exist , then we would expect them to be in those regions where the introgression scores are high ( Fig 2 ) . On the other hand , Hypothesis 2 seems unlikely , because Souza et al . ( 1997 ) found that fitness gains were not greater in the recombinant populations than in the control populations , which implies that intergenomic recombination did not increase the supply of beneficial alleles . Fig 2 shows the inferred location and direction of the Hfr origins of transfer of the four K-12 donor strains as well as the location of their auxotrophy mutations , which bear on Hypotheses 1 and 3A , respectively . With respect to Hypothesis 1 , Hfr strains transfer their DNA in a unidirectional manner , and the probability that donor genes are transferred to recipients is expected to decline from about 10−2 or 10−3 at a distance of 100 kbps from the transfer origin to ~10−6 at a distance of 2 . 3 Mbps from the transfer origin [16] . In the STLE , the cultures in which the donors and recipients were mixed were placed in a non-shaking incubator at 37°C for one hour [17]; that duration would , in principle , allow the transfer of ~60% of the entire chromosome if a conjugative mating began immediately after the strains were mixed . However , not all matings would begin immediately , shaking may disrupt conjugation , and the efficiency of DNA transfer declines with distance from the transfer origin . The peak in the introgression scores between about –1 and 0 Mbp fits very well with the locations and directions of the oriT transfer origin sites for two of the Hfr donors: REL288 and REL298 have oriT sites near the edges of this peak that point inward from opposite directions . Most of the second , less-defined peak in introgression scores seems to fit moderately well with the other two Hfr donors , REL296 and REL291 , whose oriT sites are at about 1 . 3 and 0 . 8 Mbp , respectively , with the former transferring in the direction of the peak introgression scores and the latter transferring in the same direction toward the broad shoulder between about 0 . 1 and 1 Mbp . However , the other shoulder of the second , less defined peak—from about 1 . 2 to 1 . 5 Mbp—is not explained by the logic of Hfr donor transfer . We also find donor-specific markers in some recombinant clones near the various donor-specific auxotrophic mutations ( Fig 2B ) , but almost all of these nearby introgressions involved a different donor . Nonetheless , the near absence of introgression along the circular chromosome between approximately 1 . 8 and –1 Mbp—representing over 40% of the genome—fits quite well with the Hfr donor oriT sites and directionality . On balance , then , patterns of introgression provide strong , albeit imperfect , support for Hypothesis 1 . We also found compelling evidence of strong purifying selection at the sites of the auxotrophy mutations in the K-12 Hfr donors . Recall that these mutations mean that the cells cannot produce essential amino acids and so cannot grow and persist in the minimal medium used for the STLE . Therefore , purifying selection should remove any recombinant cells that acquired any of the donors’ auxotrophy mutations . The two donors with transfer properties that can account well for the introgression peak between about –1 and 0 Mbp have auxotrophic mutations located at positions that would sharpen the peak by limiting introgression at each edge . In particular , REL288 has an auxotrophy mutation in the ilvD gene that lies just beyond the oriT site for REL298; and REL298 has an auxotrophy mutation in the argA gene that lies a short distance past the oriT site for REL288 . The other two Hfr donors , REL291 and REL296 , have auxotrophy mutations in the argE and leuB genes , respectively , that would contribute to the observed decline in introgression scores on the broader shoulder of the less defined peak from about 0 to 1 . 5 Mbp on the circular map . ( REL298 also has a second auxotrophy mutation in leuB , but this gene is very far from its oriT site and thus probably not relevant to the observed patterns of introgression . ) On balance , we also find support for Hypothesis 3A , whereby selection against the effectively lethal auxotrophy mutations in the donor strains reinforces and sharpens the patterns of introgression generated by the mechanics of gene transfer according to Hypothesis 1 . Hypothesis 3B offers a different selection-based explanation for the patterns of introgression . It rests on the idea that selection should also act against donor alleles in those genes where beneficial mutations arose in the LTEE and were present in a particular recipient at the start of the STLE . If this hypothesis were correct , then we would expect to see few , if any cases , where these beneficial mutations were removed and replaced by donor DNA . The evidence in support of this hypothesis is ambiguous , at best , because of the considerable variation among the recombinant clones , in terms of both the proportion of their DNA that comes from the donor strains and the extent to which the LTEE-derived beneficial mutations have been retained or replaced . Also , replacements of LTEE-derived beneficial mutations might simply reflect the recent introgression of donor alleles into the recipients ( i . e . , shortly before the STLE ended ) —alleles that would eventually go extinct if conjugation were stopped . In STLE population Ara–1 , only 1 of the 9 presumed beneficial mutations present in the recipient was replaced by donor DNA ( in both recombinant clones ) , but ~22% of the recombinant genomes was donor DNA ( Fig 1 ) . This pattern is consistent with Hypothesis 3B . By contrast , consider population Ara+1: 4 of the 5 presumed beneficial mutations in the recipient were replaced , but only ~32% of the two recombinant genomes came from the donors ( Fig 1 ) . Across the 12 populations , there is a slight tendency for these presumed beneficial mutations to have been replaced by donor alleles more often than the average genomic site , contrary to this hypothesis . On balance , the evidence does not support Hypothesis 3B . We can also exclude a hypothetical scenario in which beneficial LTEE-derived alleles in the recipients were generally replaced by K-12 alleles that were as or more beneficial . We examined the recombinant clone sequences to determine whether these replacements reverted the gene to its pre-LTEE ancestral state ( i . e . , the corresponding sequence in REL606 , including the case in which that sequence is identical in REL606 and K-12 ) or , alternatively , introduced a different allele . As usual , we summarize the results for the odd-numbered final clones , but the results do not differ substantively for the even-numbered clones . We examined alignments of 60 proteins ( from both non-mutator and hypermutator clones ) containing LTEE-evolved alleles in the recipients that were replaced by recombination with the donors . Of those proteins , 11 changed to the K-12 donor state that differs from the pre-LTEE REL606 state; 37 changed to the K-12 donor state that is identical to the pre-LTEE REL606 state; 8 changed back to the pre-LTEE REL606 state that differs from the K-12 donor state; and 4 changed to new states comprising combinations of K-12 , REL606 , and new mutations . Fig 3 illustrates the four cases in which new alleles emerged . The yghJ gene in population Ara–1 was evidently affected by at least three recombination events: the whole gene is derived from K-12 except for two regions , ( spanning residues 803–824 and 1068–1212 in the alignment ) that contain REL606 markers . In an hslU allele from Ara+1 and a pykF allele from Ara–4 , recombination events reverted alleles with evolved mutations to their ancestral states , and the new mutations presumably arose later . As seen in the yghJ gene from Ara–1 , the nfrA gene from Ara+3 contains a hybrid allele generated by intragenic recombination ( Fig 3 ) . The recombination event in nfrA reverted a W289* nonsense mutation to its ancestral state , but left unchanged a C144R substitution . The K-12 markers present at amino acid 364 and beyond were not introduced , and so this recombination event introduced a segment that was at most 364–144 = 220 amino acids , or 660 bp , in length . However , it is also possible that this reversion occurred by a point mutation , given the hypermutability of the Ara+3 recipient . ( Fig 1 ) . Sequence alignments of all replaced alleles are provided in S1 Dataset . In general , it is difficult to discern which replaced LTEE alleles in the hypermutator STLE clones ( those isolated from populations Ara–2 , Ara+3 , Ara–6 ) were beneficial driver mutations , owing to the large number of quasi-neutral passenger mutations that hitchhiked to high frequency in those populations [21] . In contrast , we are confident that LTEE-derived alleles in genes where mutations repeatedly reached high frequency in populations with the ancestral mutation rate were under positive selection ( Table 1 ) . We therefore closely examined the alignments of the 30 proteins containing LTEE-evolved mutations in the recipients that were replaced by recombination in the nine odd-numbered non-mutator clones . Recombination with the K-12 donors changed 5 LTEE-evolved alleles into the donor state that differs from the pre-LTEE state; changed 18 LTEE-evolved alleles into the donor state that is identical to the pre-LTEE state ( i . e . , the same sequence is present in REL606 and K-12 ) ; changed 4 LTEE-evolved alleles back to the pre-LTEE state that differs from the donor state; and changed 3 LTEE-derived alleles into new alleles ( Table 2 ) . Eleven of the 22 cases where an LTEE-evolved allele went back to its pre-LTEE ancestral state occurred in genes under strong positive selection in the LTEE [21 , 27] , indicating that many beneficial mutations were removed by recombination with the donors . The new alleles that evolved after recombination had effectively reconstructed the ancestral states in hslU in Ara–1 and pykF in Ara–4 imply that these reversions of beneficial LTEE-derived mutations by recombination were not adaptive . The following analyses focus , for simplicity , on the odd-numbered recombinant clones from the STLE populations that were not hypermutable; however , the even-numbered clones are similar . We noticed that these recombinant clones often had more new mutations than did typical LTEE clones that had evolved for 1000 generations [21] . When we looked for evidence of parallel evolution among these new mutations , we found strong but spurious signals in two genes , nohB and waaQ; in particular , these genes had multiple identical mutations in multiple lineages . The likely explanation for these parallel changes is gene conversion , in which recombination occurred between non-orthologous genes in the K-12 donors and B recipients , such as between diverged paralogs or perhaps even non-homologous sequences . To investigate further the possibility of gene conversion , we scored all genes that had three or more new mutations in the same recombinant genome as putative gene conversion events ( Table 3 ) . Because so many apparently multi-mutation events occurred , and usually in multiple lineages , we think each case is best explained by a single gene-conversion event , not by multiple mutations in the same gene . We also used data on positive selection on specific genes in the LTEE [21] to ask whether new nonsynonymous mutations in the STLE tended to occur in the same genes . Genes affected by nonsynonymous mutations resulting from putative gene conversions had a mean G-score of 0 , while the genes harboring all other new nonsynonymous mutations had a mean G-score of 33 . 76 . This difference , though only marginally significant ( two-sided Welch’s t-test , p-value = 0 . 035 ) , suggests that nonsynonymous mutations that arose during the STLE were under stronger positive selection than those that occurred by non-homologous recombination . One of our most puzzling findings is that many LTEE-derived mutations , including some that were almost certainly beneficial , were lost in the recombinant clones from the Ara+1 and Ara–4 STLE populations ( Fig 1 ) . These genes were among those under strong positive selection for new mutations in the LTEE [21] , and the STLE environment was almost the same as the LTEE . The Ara+1 and Ara–4 STLE populations account for 12 of 30 nonsynonymous replacements , and 14 of 16 new nonsynonymous mutations after excluding the multisite gene-conversion events . This association suggests that the loss of beneficial mutations to recombination in these lineages also led to stronger selection for new beneficial mutations elsewhere in the genome . In contrast , the Ara–3 STLE population had 8 nonsynonymous replacements , but no new nonsynonymous mutations to compensate . The Ara–3 recombinants are the only ones with genomes that derive primarily from the K-12 donor strains ( Fig 1 , S2 Fig ) . Also , this population underwent unexpected changes in its ecology , which led to a substantial decline in its fitness relative to the common competitor used in the STLE [17] and the emergence of a negative frequency-dependent interaction between different recombinant genotypes , probably caused by a crossfeeding interaction [19] . These striking changes in genetic background and ecological context may well have altered the genetic targets of selection in this population . We examined the distributions of recombinant segment lengths to see whether conjugation left a consistent signature in this respect . The left column of Fig 4 shows the distribution of lengths of DNA segments derived from the K-12 donor in the recombinant clones , and the right column shows the length distribution of the B-derived segments ( using the odd-numbered clones ) . To explore the possibility of characteristic segment lengths , we excluded Ara–3 ( which was almost entirely donor-derived ) , Ara–6 and Ara+2 ( which had little or no donor DNA ) , and the three mutator lineages ( Ara–2 , Ara+3 , Ara+6 ) because DNA repair processes also affect recombination . Even using only the remaining six lineages , the recombinants show significant heterogeneity in the length distribution of their donor-derived segments ( Kruskal-Wallis rank sum test , chi-squared = 27 . 297 , df = 5 , p < 10−4 ) . As noted before , K-12 specific markers occur densely over the REL606 reference genome ( S1 Fig ) , and so the distribution of markers cannot account for this heterogeneity . It is unclear what accounts for this variability across the recombinant lineages . It might reflect differences in the initial recipient genotypes , including their receptivity to conjugation and recombination , or early events in the history of the STLE populations that affected their subsequent receptivity to these processes . Table 4 compares the number of synonymous changes introduced by recombination to those caused by mutation . These data exclude probably spurious inferences of synonymous changes introduced by gene conversion events ( Table 3 ) . We divided new synonymous changes based on whether they were found in recipient- or donor-derived segments . Even if we exclude the synonymous changes in the donor-derived segments , the rate at which the non-hypermutable recombinant lineages accumulated synonymous changes over the 1000 generations of the STLE was several times higher than the corresponding rate during the LTEE [21 , 28–30] . This higher rate presumably reflects some mutagenic effect of recombination , such as error-prone repair of double-strand breaks during integration of donor DNA into a recombinant genome . The fact that there are almost as many new synonymous mutations in the donor-derived as in the recipient-derived segments , despite the smaller cumulative target size of the former regions , also supports that interpretation . In any case , the vast majority of synonymous changes that arose during the STLE were introduced through recombination , not by mutation . Twenty of the 24 sequenced recombinant clones acquired hundreds , thousands , or even tens of thousands of synonymous changes by recombination , whereas no clone , even in the mutator lineages , had as many as 20 synonymous mutations ( Table 4 ) . The four exceptional recombinant clones are those that , as described earlier , acquired little or no donor DNA ( including both clones from STLE population Ara+2 and single clones from Ara+3 and Ara–6 ) . Excluding those four atypical clones and others from hypermutable populations ( Ara+3 , Ara+6 , and Ara–2 ) , the ratio of synonymous changes introduced by recombination and mutation is generally well over 1000 ( Table 4 ) . For the three hypermutable populations , the ratio of synonymous changes introduced by recombination and mutation is still high at ~70–90 . A recent estimate of the ratio of changes observed in recombinant regions relative to spontaneous mutations in natural E . coli populations is ~10 [31] , and so the intergenomic recombination rate in the STLE was clearly much greater than the recombination rate in nature . One unexpected finding is the predominantly donor-derived ancestry of the recombinant clones from STLE population Ara–3 ( Fig 1 , S2 Fig and S3 Fig ) . We considered the possibility that this ancestry might reflect the reversion of an auxotrophy mutation in one of the K-12 donors , which might have allowed it to persist and perhaps rise to dominance . If that were the case , then we would expect its ancestry to stem from just one of the four donor strains . Instead , the recombinant clones from Ara–3 contain genetic markers from all four donors as well as the B-derived recipient . In fact , all of the STLE populations ( except Ara+2 ) contain markers derived from at least two donors ( Fig 5 ) . In any case , the Ara–3 population does not show evidence of being descended from a single donor strain that somehow survived and displaced the recipient population . If the F plasmid persisted in this population , either in a donor-derived lineage or by the conversion of a recipient into a secondary donor , then conjugation might have occurred above and beyond the recombination treatment imposed every fifth day during the STLE . Although no donor-derived lineage became established , it is possible that a recipient was converted to an F plasmid-carrying donor , despite the shaking that occurred after 1 hour of the recombination treatment , which was expected to interrupt conjugation [17] . Indeed , more recent studies have found that F plasmid-mediated conjugation readily occurs in E . coli at the 120 rpm shaking speed of the STLE , albeit under different conditions [32 , 33] . We found sequencing reads that map to the entire F plasmid in both Ara–3 clones , but not in any other clones we sequenced ( S4 Fig ) . We do not know whether these Ara–3 clones contained free copies of the plasmid or , alternatively , had integrated the plasmid into the chromosome , which would make them potential Hfr donors . The F-plasmid contains repetitive sequences and insertion elements with homology to multiple locations on the E . coli chromosome , making integration a possibility . Souza et al . ( 1997 ) reported that the Ara–3 clones had become resistant to tetracycline , like the Hfr donors and unlike the recipients or any of the other recombinants they tested . However , we saw no reads that mapped to the TetR gene , and we do not know the reason for this discrepancy . One potentially confounding factor in our analysis is that some donor segments might have been introduced during one of the final rounds of the conjugation treatment of the STLE , and hence they could be deleterious variants that are present only transiently and destined for extinction . To test that possibility , we restarted the 12 STLE populations from their final samples and propagated them for an additional 30 days ( 200 generations ) without further addition of the donor strains . This period would thus allow time for recently generated maladapted recombinants to be outcompeted by more-fit members of the respective populations . We then sequenced whole-population samples from the starting and final time points ( S2 Table ) . If the introgressed K-12 alleles were deleterious , then we would expect their frequency to decrease over time . By measuring how the frequency of K-12 donor alleles changed after stopping conjugation , we assessed whether these alleles were usually evolutionary dead-ends or , instead , persisted by positive selection on introgressed alleles or linked beneficial mutations . Donor-derived alleles increased in frequency in several populations during the STLE continuation ( Fig 6 ) , although some populations , most notably Ara+5 , showed a mixture of directional changes . Therefore , the introgressed donor-derived alleles do not appear to impose a consistent fitness burden on the recombinant populations . However , we cannot rule out the possibility that some transferred alleles impose a fitness cost , but nevertheless hitchhiked with beneficial mutations elsewhere in the recombinant genomes [34] . Also , many donor-derived alleles might have increased in frequency as neutral hitchhikers; if a given allele was neutral , then the probability that it would hitchhike to fixation ( assuming the STLE continuation was of sufficient duration ) should be equal to its initial frequency . Some K-12 donor alleles might even have conferred a selective advantage; the existence of at least a few such alleles is suggested by the tendency for clusters of points to lie above the diagonal . Even though the continuation experiment shows that donor alleles on the whole did not impose a large fitness burden on the recombinant populations , it does not exclude the possibility that recurrent bouts of recombination during the STLE helped drive some maladaptive alleles to fixation . The continuation experiment and associated metagenomic sequencing also allowed us to infer haplotypes that had fixed before the end of the STLE ( Fig 7 ) . Specifically , we identified derived alleles with frequency equal to 1 at the start and finish of the continuation experiment ( corresponding to generations 1000 and 1200 , respectively , with respect to the STLE ) that were also present in both recombinant clones isolated from a given population . We saw that reversions of beneficial LTEE-evolved mutations went to fixation in several of the STLE populations . In the three hypermutator populations ( Ara+3 , Ara+6 , and Ara–2 ) , the introgression of donor alleles might have simultaneously removed some deleterious mutations that arose during the LTEE [29] . Nonetheless , it is puzzling to see the recombination-mediated reversion of some clearly beneficial mutations even in non-mutator populations . In some cases ( hslU in Ara+1 , and pykF in Ara–4 ) , new mutations became established , presumably after the replacement of the beneficial LTEE-evolved mutations . In other cases ( nadR in Ara+1 and spoT in Ara+4 and Ara–4 ) , beneficial mutations reverted to their pre-LTEE state , and these reversions went to fixation . In general , the locations of parallel introgression events in the inferred LCA of the STLE closely follow the results based on the sequenced clones ( S6 Fig ) . Given that the F plasmid was found in the Ara–3 STLE clones , we also looked for it in the whole-population metagenome samples taken at the start and end of the continuation experiment . The F plasmid was present in the final samples from the continuation experiment in two populations , Ara+1 as well as Ara–3 ( S7 Fig ) . To get a sense of the frequency of the plasmid in these two populations , and how it changed during the continuation experiment , we compared sequence read coverage of the plasmid and on the chromosome in the initial and final samples . In population Ara–3 , the estimated plasmid frequency fell from 75% to 49% during the continuation experiment , whereas in Ara+1 it increased from <1% to 36% ( Table 5 ) . Even excluding these two populations , donor-derived alleles tended to increase in frequency in several of the other continuation populations ( Fig 6 ) , indicating that plasmid persistence was not necessary for that outcome . Overall , these results indicate that introgressed K-12 alleles did not generally impose a sufficient fitness cost to drive down their frequency , even in the absence of further conjugation . Recurrent conjugation over a sufficiently long period means that some neutral , and perhaps deleterious , donor alleles will almost certainly fix in an adaptively evolving recipient population through a ratchet-like hitchhiking process . Suppose that a neutral donor allele is introduced into a genetic background that contains a beneficial mutation undergoing a selective sweep; depending on when the donor allele was introduced relative to the sweeping mutation , the donor allele will reach some frequency p . Then , the probability that the next beneficial sweep drives that neutral donor-derived allele to fixation is p . With probability 1 –p the next beneficial mutation occurs on a different genetic background , thereby driving the donor allele to extinction . Nonetheless , the process repeats again and again with each successful transfer of a neutral allele . We suggest that this ratchet-like process might explain the some of the unexpected dynamics observed during the STLE .
In this study , we sequenced and analyzed donor , recipient , and recombinant genomes from the Souza-Turner-Lenski experiment ( STLE ) . Two main results are clear . First , we found parallel evolution in the genomic structure of recombinant E . coli . This pattern appears to be largely explained by the biases caused by the molecular biology of conjugation , coupled with selection against the effectively lethal auxotrophy mutations carried by the donor strains . Second , there was substantial introgression of donor DNA into most of the recipient populations . Indeed , the rate of recombination was sufficiently high that many beneficial mutations , which had previously evolved during 7 , 000 generations of asexual evolution in the same environment , were erased by recombination . We estimate that the effective recombination rate , expressed relative to the rate of genomic change by new mutations , was at least 100-fold higher in the STLE than previously estimated in nature for E . coli [31] . However , if the majority of synonymous mutations in natural isolates of E . coli arose during periods of transient hypermutability , then the appropriate comparison would be to the hypermutator mutator populations of the STLE . In that case , the effective recombination rate in the STLE would be ~10-fold higher than the rate for nature . In addition , and to our surprise , we found that one STLE population , designated Ara–3 , had predominantly K-12 donor ancestry . The Ara–3 recombinant clones lack all of the LTEE-derived mutations present in their ancestral recipient , but they still have small segments that derive from the E . coli B progenitor used to start the LTEE . The effect of recombination was so strong in this population that highly beneficial alleles in the recipient clone at the pykF , malT , hslU , and topA loci were erased ( Fig 7 ) . Moreover , the entire F plasmid was present in both of the sequenced Ara–3 recombinant clones , whereas it was absent , as expected , from the sequenced recombinant clones from all other populations . ( The F plasmid was found at a much lower frequency at the end of the STLE in one other population . ) The F plasmid clearly became established in the Ara–3 population , but it is unclear whether it did so as a free plasmid or was integrated into a recipient chromosome . Several explanations for the F plasmid’s persistence are possible , and they are not mutually exclusive . First , experiments have shown that F+ strains can convert F−cells into F+ cells during a quasi-epidemic of plasmid transmission [35] . If this occurred in Ara–3 , then newly infected F+ recipients could transmit the plasmid , along with any donor genes that might hitchhike , to further recipients . Second , a B recipient might have been converted into an Hfr donor and delivered small B segments to a K-12 donor strain that then survived . Third , recombination between the K-12 donor and B recipient genomes might have activated an otherwise latent prophage , leading to virus-mediated transduction in the opposite direction to conjugation . Fourth , a K-12 donor strain might have reverted its auxotrophy mutation , allowing it to grow and persist in the minimal medium of the STLE . It is known that the Tn10-transposon mutagenesis used to construct the donor strains [36] yields unstable genotypes , in which the transposons can excise or move to other locations in the genome . Fifth , one K-12 donor might have recombined with a second K-12 strain ( which had perhaps lost its F plasmid and thereby become a recipient ) in such a way as to repair the nutritional defect . Sixth , some mutation or mutations in the Ara–3 recipient genome may have allowed for vastly more efficient conjugation and DNA incorporation . However , the two recipient strains with defects in their mismatch repair ( Ara+3 and Ara–2 ) did not have more K-12 ancestry than the other recipients , even though previous research has shown that E . coli strains with defective mismatch repair have relaxed homology requirements for molecular recombination [7] . Even excluding Ara–3 , we observed a great deal of heterogeneity in the amount of introgressed DNA across the STLE populations and in the lengths of donor tracts ( Fig 4 ) . Researchers studying natural transformation in other bacterial species have reported that donor segments often cluster into complex mosaic patterns , perhaps generated by long stretches of DNA being disrupted after their uptake or as the result of heteroduplex segregation and correction [37–40] . Our results accord with these previous reports of fine-scale mosaicism of donor- and recipient-derived regions in recombinant genomes . The lengths of many recombinant segments in the STLE are also consistent with the pervasive transfer of genome fragments ranging from ~40 to ~115 kbp reported in natural populations of E . coli [31] . Both generalized transduction and conjugation can produce such long tracts of introgressed DNA , although the relative contribution of these mechanisms to horizontal gene transfer in nature is unknown . Experiments have shown that the spatial separation of donor and recipient strains during growth on surfaces can suppress conjugation [41] , whereas conjugation can more readily spread genetic material in the well-mixed liquid environment of the STLE . Evolution experiments with both bacteria and yeast have shown that intergenomic recombination can sometimes speed up the process of adaptation by natural selection [12 , 13] . In contrast , the STLE shows that recombination can sometimes act in a manner more analogous to an extremely elevated mutation rate , leading to neutral and even maladaptive changes . Such an effect is not without precedent; for example , plant populations that have evolved resistance to heavy metals found in patchily distributed mine tailings have also evolved selfing to avoid the genetic load of pollen from nearby metal-sensitive populations [42] . The high density of the introduced donors relative to recipients , the high effective rate of recombination , and the fact that the recipients but not the donors had adapted to the STLE environment appear to have created a similar situation , in which non-adapted donor genes “rained down” on the locally adapted , LTEE-derived recipients . The most striking evidence that recombination could have maladaptive effects in the STLE was the finding that many beneficial mutations were effectively “erased” by replacement with donor alleles that were the same as the LTEE ancestral state , especially in populations Ara+1 , Ara–3 , and Ara–4 . If most donor alleles were neutral or maladaptive in the environment of the STLE , then it is not surprising that the recombination treatment did not speed up adaptation [17] . What is surprising , though , is the extent to which those alleles could evidently invade and replace better-adapted recipient alleles . These results support the first hypothesis proposed by Souza et al . ( 1997 ) , which postulated that recurrent bouts of conjugation were sufficient to drive recombinant genotypes to high frequency . On the other hand , the fact that most STLE populations did not decline in fitness , despite having some beneficial mutations erased , leaves open the possibility that some other donor-derived segments harbored beneficial alleles that offset the removal of LTEE-derived beneficial mutations . Also , by allowing the STLE populations to evolve for an additional 200 generations without the conjugation treatment , we showed that haplotypes containing donor-derived segments often increased in frequency ( Fig 6 ) , contradicting the hypothesis that they could only persist by ongoing recombination . This result supports the second hypothesis proposed by Souza et al . ( 1997 ) , which postulated that donor alleles hitchhiked to high frequency along with beneficial alleles . However , we observed too many linked donor alleles to distinguish between highly beneficial ‘driver’ mutations and their hitchhiking ‘passengers’ . This dynamic is common in large asexual populations , where cohorts of mutations track closely in frequency and go to fixation or extinction together [43–45] . In any case , it appears that donor genes often replaced homologous genes in the recipient populations as a consequence , at least in part , of the transmission advantage produced by horizontal gene transfer .
The experiments performed by Souza et al . ( 1997 ) are described fully in that paper . In brief , 12 recombinant populations and 12 control populations were started from clones isolated after 7000 generations of the LTEE ( S1 Table ) . These populations were propagated daily for 1000 generations ( 150 days ) following the same transfer regime and using the same DM25 medium and temperature as the LTEE [46] . However , on day 0 and every fifth day thereafter ( ~33 generations ) , a mixture of four K-12 Hfr donor strains ( REL288 , REL291 , REL296 , and REL298 ) was added to the recombination treatment populations and allowed to conjugate for 1 h without shaking . The ratio of donor to recipient cells during the treatment was ~4:1 . All four donors were auxotrophic for one or more essential amino acids ( arginine , leucine , or isoleucine-valine ) , so they could transfer their genetic material but not grow and persist in the population . Samples of all 12 recombination-treatment populations from generation 1000 of the STLE ( S1 Table ) were revived as follows: 100 μL from each frozen stock were pipetted into 10 mL of LB medium , grown for 24 h , diluted and grown in DM25 for two more 24 h cycles , and then spread on LB agar plates . The two colonies that grew closest to randomly placed marks were re-streaked on LB agar plates and then grown in LB medium . These 24 STLE-derived recombinant clones were stored at –80°C ( S1 Table ) . We sometimes refer to these clones as “odd” and “even” ( based on the freezer identification numbers for each pair ) when presenting data for just one clone from each population . We estimate that the growth in LB and two cycles in DM25 added roughly 25 generations . Given the very low mutation rate in nonmutator cells [28] and the small fraction of mutations that have any discernible effect on fitness [29] , there is little opportunity for meaningful de novo evolution during the isolation of clones . Owing to these procedures , including the unavoidable freezing and thawing of samples , it is also possible that we did not sample clones representative of the diversity present in the STLE populations at generation 1000 . However , that concern falls away because our conclusions do not change if we restrict our analysis of the clones to those mutations that fixed in their respective populations by 1000 generations ( Fig 7 ) . The 4 Hfr donor strains ( REL288 , REL291 , REL296 , and REL298 ) , 12 LTEE-derived recipient clones used as ancestors in the STLE , and 24 STLE-derived recombinant clones ( two per population ) were thawed and grown in LB medium , and samples of genomic DNA were isolated from each one . The genomic DNA was then sequenced on an Illumina MiSeq at the MSU RTSF Genomics Core Facility . We used the breseq ( version 0 . 31 ) pipeline [47] to analyze the genomes , with the ancestral LTEE strain REL606 as the reference genome unless otherwise specified . We used the gdtools utility in breseq to compute a table that lists the union of all the mutations found in the K-12 donor genomes in comparison to REL606 , and to identify mutations specific to each of the donor strains . A custom python program called label_mutations . py was written that performs several tasks . First , it labels all of the “mutations” ( i . e . , all genetic differences including those that result from recombination ) found in the STLE-derived recombinant genomes relative to the REL606 genome by looking at the same site in the donor , recipient , and recombinant genomes . Nine distinct labels are used , as follows . ( i ) Mutations that are present in both a recombinant clone and its parent recipient clone ( but not in REL606 or the union of donor strains ) are labeled as “LTEE mutations” . ( ii ) Mutations found in both a recombinant clone and the union of K-12 donors are labeled as “K-12 mutations”; they represent horizontally transferred alleles . ( iii ) Any mutations that are present in a recombinant clone , but not found in either the donors or recipient clone , are labeled as “new mutations”; these are mutations that occurred during the STLE , and which do not appear to involve a donor . ( iv ) Mutations present in the union of K-12 donors that are not found in the recipient clone are labeled “REL606 mutations;” more precisely , these sites are genetic markers that distinguish the E . coli B-derived REL606 strain used to start the LTEE from K-12 . S1 Fig shows the distribution of these markers along the E . coli B chromosome . ( v ) . Mutations found in a recipient , but not in its derived recombinant , are labeled as “deleted mutations”; these mutations were removed by recombination with the donors or otherwise lost during the STLE . ( vi–ix ) . Mutations specific to each of the four donor strains that are found in the recombinants are labeled as “donor-specific mutations”; these mutations are included in the set of “K-12 mutations” in most of our analyses . The program ignores all sites that are identical between the recombinant , recipient , and donors because they provide no useful information . Our analysis also ignores genomic “islands” that are present in K-12 but not REL606 . As a second task , the label_mutations . py program produces a table of the genetic markers that distinguish K-12 and REL606 ( see label iv above ) . Third , this program generates a table of the LTEE-specific mutations found in the recipient genomes ( see labels i and v above ) . An R script called dissertation-analysis . R makes figures and performs statistical tests using the tables of labeled mutations that label_mutations . py produces . Each donor strain also has two special features: the mutations that make it an amino-acid auxotroph , and the Hfr transfer origin and orientation ( i . e . , direction of transfer ) . The breseq analysis found all the auxotrophy mutations present in the donor strains REL288 , REL291 , REL296 , and REL298 . These mutations were generated and annotated by Wanner et al . ( 1986 ) , and the strains were confirmed to be auxotrophs by Souza et al . ( 1997 ) . We checked the genomic locations of the auxotrophy mutations returned by breseq against the original strain annotations and a fine-grained traditional linkage map for E . coli K-12 [48] from the Coli Genetic Stock Center ( https://cgsc2 . biology . yale . edu/Workingmap . php , last accessed 7/13/17 ) . We note that the leuB auxotrophy mutation in REL298 is a S286L ( TCG→TTG ) mutation in an NAD-binding region of the protein ( http://www . uniprot . org/uniprot/P30125 , last accessed 7/31/17 ) , rather than an amber nonsense mutation as originally annotated . We did not resolve the position and orientations of the Hfr transfer origins , because the F plasmid contains repetitive sequences and insertion elements that map to multiple locations on the E . coli chromosome . The genomes of K-12 and REL606 are largely syntenic [26] , and so we used the K-12 linkage map to place the Hfr oriT transfer origin sites in the K-12 donors with respect to their homologous genes in REL606 . This mapping of K-12 elements to the REL606 genome is only approximate , but it appears to perform reasonably well . Our annotations using breseq and the K-12 linkage map along with cross-references to annotations from the Coli Genetic Stock Center are in a file called donor_hand_annotations . csv . Because the F plasmid contains repeats and insertion elements that also map to the E . coli chromosome , as noted above , we could not resolve whether the recombinant clones and population samples that contained the F element had it integrated in the chromosome or , alternatively , it existed as a free plasmid . Recombination breakpoints occur somewhere in the interval between donor-specific and recipient-specific markers . A minimum estimate of the length of a donor segment would place the recombination breakpoints at the donor markers on each end , while a maximum estimate would place the breakpoints at the flanking recipient markers . In fact , the true breakpoints cannot be known exactly . Our approach uses the minimal estimate on the left , but the maximal estimate on the right , and so it will produce intermediate values that should tend to an overall average length similar to what would be obtained by averaging the minimum and maximum segment lengths . In particular , our algorithm alternates between K-12 and B-derived segments along the genome , switching states when it reaches the alternate marker type . This algorithm is described in detail in S1 Text . We performed the following experiment to determine the fate of introgressed K-12 alleles in the absence of the recombination treatment . We revived the 12 populations of the STLE recombination treatment from the samples that were frozen at 1000 generations . We then propagated them daily for 200 generations ( 30 days ) following the same transfer regime and using the same DM25 medium and other conditions as the LTEE [46] . Every 33 generations ( 5 days ) we froze glycerol stocks of all 12 populations . Finally , we isolated genomic DNA from the initial and final population samples , and sequenced the whole-population metagenomes to see how the genetic variation changed during the 200 additional generations without recombination . When isolating genomic DNA from these population samples , we took 125 μL of each glycerol stock , then washed and centrifuged the cells twice in DM0 ( Davis Minimal medium without glucose ) to remove residual glycerol . We then added 100 μL of the washed and resuspended cells into a flask containing 9 . 9 mL of DM100 ( the same medium used in the LTEE , except with a glucose concentration of 100 μg/mL to yield more cells ) , and we plated the remaining 25 μL onto a tetrazolium arabinose agar plate to make sure that the washing steps did not cause any unexpected population bottleneck .
|
Bacteria often transfer genes encoding antibiotic resistance as well as other important traits , but the extent of intergenomic recombination—in effect , sex—is highly variable across bacterial species . Why ? A better understanding of how and why bacteria exchange genes would help people combat the spread of infectious disease as well as shed light on the evolutionary origins of sex . Here , we sequenced genomes from an evolution experiment with Escherichia coli in which recombination was extensive but , unexpectedly , did not speed up the rate of adaptation . In this experiment , the effective rate of chromosomal recombination was much higher than previously inferred for natural E . coli populations . In fact , the rate was so high that introduced genes sometimes drove established beneficial alleles to extinction in the experimental populations . In effect , genes that were physically linked to the genes causing recombination had a strong transmission advantage , whether or not they provided any selective advantage to the recipient cells .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"cloning",
"alleles",
"mutation",
"hemorrhagic",
"fever",
"with",
"renal",
"syndrome",
"nonsense",
"mutation",
"dna",
"recombination",
"molecular",
"biology",
"techniques",
"dna",
"research",
"and",
"analysis",
"methods",
"infectious",
"diseases",
"molecular",
"biology",
"genetic",
"loci",
"biochemistry",
"point",
"mutation",
"nucleic",
"acids",
"genetics",
"biology",
"and",
"life",
"sciences",
"viral",
"diseases",
"introgression",
"evolutionary",
"biology",
"evolutionary",
"processes"
] |
2018
|
Analysis of bacterial genomes from an evolution experiment with horizontal gene transfer shows that recombination can sometimes overwhelm selection
|
Many duplicate genes maintain functional overlap despite divergence over long evolutionary time scales . Deleting one member of a paralogous pair often has no phenotypic effect , unless its paralog is also deleted . It has been suggested that this functional compensation might be mediated by active up-regulation of expression of a gene in response to deletion of its paralog . However , it is not clear how prevalent such paralog responsiveness is , nor whether it is hardwired or dependent on feedback from environmental conditions . Here , we address these questions at the genomic scale using high-throughput flow cytometry of single-cell protein levels in differentially labeled cocultures of wild-type and paralog-knockout Saccharomyces cerevisiae strains . We find that only a modest fraction of proteins ( 22 out of 202 ) show significant up-regulation to deletion of their duplicate genes . However , these paralog-responsive proteins match almost exclusively duplicate pairs whose overlapping function is required for growth . Moreover , media conditions that add or remove requirements for the function of a duplicate gene pair specifically eliminate or create paralog responsiveness . Together , our results suggest that paralog responsiveness in yeast is need-based: it appears only in conditions in which the gene function is required . Physiologically , such need-based responsiveness could provide an adaptive mechanism for compensation of genetic , environmental , or stochastic perturbations in protein abundance .
Gene duplication is a primary mechanism for the origin of new genes , providing raw material for functional innovation [1]–[8] . Small-scale duplication of individual genes as well as whole-genome duplication shape the genome of organisms from ciliates [9] and yeasts [10]–[12] to plants [13]–[15] and chordates [16] , [17] . Following duplication , paralogous genes may assume different fates , including loss of one of the duplicates , divergence and functional differentiation , or maintenance of partially overlapping functions [7] . Although most paralogs are lost [18] , some are retained . In the yeast Saccharomyces cerevisiae , genes that encode enzymes , transporters , and transcription factors have often survived in duplicate after a whole-genome duplication event that occurred 100 million years ago [7] , [19] , [20] . Furthermore , many surviving paralogs maintain overlapping functions despite divergence through long evolutionary time scales [21]–[24] . This functional overlap between duplicate genes manifests as synthetic aggravating interactions between paralogs; a double knockout of both duplicate genes shows a large phenotypic effect [21]–[24] despite the fact that each of the single knockouts shows a neutral or very weak phenotypic effect [21] , [25] . In addition to functional overlap between the duplicates , the phenotypic buffering of an individual knockout requires expression of its paralogous gene . Analysis of transcriptional expression profiles has suggested the existence of “responsive backup circuits” that up-regulate a duplicate gene when its paralog is absent [26] , [27] . Although several specific examples of gene dosage compensation between duplicate genes have been revealed in different organisms and biological processes [28]–[31] , the genome-wide extent of such paralog-responsive backup circuits is unclear [32] . In principle , the ability of a gene to compensate for the absence of its paralog may be based on its basal protein expression level and not necessarily require its up-regulation . By comparing single-cell levels of yeast proteins fused to the green fluorescent protein ( GFP ) in the wild-type and in the paralog-deleted background in S . cerevisiae , we systematically identified changes in protein levels for approximately 200 duplicate genes in response to deletion of their paralogs and revealed the environmental requirement for paralog responsiveness .
To quantify the effect of deletion of a gene , X2 , on the protein abundance of its paralog , X1 , we used high-throughput flow cytometry to measure the level of X1-GFP fusion protein expressed at its endogenous locus [33] , [34] in wild type and Δx2 haploid background strains ( Figure 1 ) . We constitutively expressed a marker fluorescent protein ( cerulean [CFP] in the wild type strain and mCherry [RFP] in the Δx2 strain , or vice versa as a “dye swap” control ) , to provide a method for distinguishing mixed cells of the two strains . This allowed us to coculture the two strains , thereby ensuring that they were grown under identical environmental conditions , and to use flow cytometry to identify wild-type and knockout cells on a cell-by-cell basis while measuring each cell's GFP signal ( Materials and Methods ) . From this data , we defined the paralog responsiveness , R , of X1 as the log2 of the ratio of its mean expression level in the Δx2 background ( ) over the wild-type background ( ) , . We concentrated our analysis on 1 , 054 duplicate genes present in the yeast genome as two-member paralogous pairs [35] . Of this set of genes , 749 are available as protein fusions from the GFP-tagged yeast expression library [33] , and for 92% of them , the corresponding paralog knockouts are present as viable strains in the yeast deletion collection [36] . Using two rounds of mating and haploid selection [37] , we generated a total of 687 pairs of strains of GFP fusions in the paralog-deleted and wild-type backgrounds ( Table S1 ) . All ribosomal protein genes ( 54 ) were later removed from our collection to avoid potential complications due to aneuploidy , resulting in a total of 633 pairs of strains [38] . The libraries were constructed in quadruplicate—two replicates expressing CFP , and two replicates expressing mCherry ( Materials and Methods; Figure S1 ) . We measured the GFP fluorescence of each protein fusion X1-GFP in mid-log phase in rich medium ( YPD ) , in a 1∶1 coculture of wild-type and paralog-deletion strains ( WT , Δx2 ) in duplicate for each of the quadruplicate libraries ( eight total replicates ) . After autofluorescence correction and spectral unmixing , GFP signal was detected for ∼50% of the X1-GFP protein fusions in both the wild-type and deletion backgrounds . Our results are restricted to the highest two thirds of these strains to ensure an accurate measurement of responsiveness , giving a total of 202 strains ( Materials and Methods; Table S2 ) . To help remove nonspecific gene regulation of X1 due to the physiological effect of X2 deletion , we measured the effect of X2 deletion on the expression of a housekeeping gene RPL41B . To this end , we generated a control library of Rpl41b-GFP fusions in each of the 633 deletion backgrounds discussed above , and in the wild-type background , respectively , tagged with CFP and RFP ( and a “dye swap” control ) . Measuring the expression of Rpl41b-GFP in cocultures of each deletion strain and the wild type , we determined that 17 strains showed significant abnormalities in Rpl41b-GFP expression . Although these genes are interesting in their own right , we eliminated them from further analysis in this study ( highlighted genes in Table S2 ) . We found that only ∼15% ( 29 ) of the detectable duplicate genes are significantly up- or down-regulated in the paralog-deletion strain grown in rich medium ( Figure 2A ) . Significance was determined using 95% confidence intervals derived by bootstrapping the set of measurements assuming no paralog responsiveness ( R = 0 ) and using the measured noise in R ( Figure 2A , gray band; Materials and Methods ) with the actual distribution we observed . Noise in R was estimated from the variability in the replicate measurements of each gene ( Figure 2B , Figure S2 ) . We then constructed a control “random library” of X1-GFP fusions combined in random ( nonparalogous ) to the paralog-deletion backgrounds with a nonrelated deletion background . A total of 121 fusions in this set of strains had detectable GFP signal , and their responsiveness to the random deletion showed no significant deviation from the expected null distribution ( Figure 2A , black crosses are inside the gray band ) . These controls indicate that the responsiveness we detected is specific to the deletion of the paralogous gene . The majority ( 23 out of 29 ) of the paralog-responsive genes show positive responsiveness ( R>0 , up-regulation of gene in response to deletion of its paralog ) and only few ( six out of 29 ) showed negative responsiveness ( Figure 2B ) . Following the backup hypothesis , we focus the rest of our analysis on the positively responding genes . We note though that negative responsiveness may also be an adaptive behavior , for example related to stochiometric regulation of protein complexes; indeed , we found that three out of the six negatively responding genes are known to interact physically with their paralogs ( FPR3 , FPR4 , and PYC2 ) [39] . In the positively responding genes , we observed significant up-regulation from 1 . 13-fold to over 20-fold ( median value 1 . 7-fold; Figure 2B; Table S2 ) . For 78 GFP tagged proteins , we had data for both paralogs ( 39 pairs ) , and 11 genes responded positively within this set , including three pairs of mutually responding paralogs ( SAM1-SAM2 , IMD3-IMD4 , and HSP82-HSC82; Figure S3 ) . In the asymmetric cases—gene pairs in which one protein responds to deletion of its paralogous gene , but not vice versa—the responding protein can be either the high or the low expressed member of the pair ( Figure S3 ) . Because previous backup circuit studies examined mRNA levels rather than protein levels , we asked whether the protein level responsiveness we observe occurs at the transcriptional or post-transcriptional level ( Figure 2C ) . In analogy to the protein-level responsiveness R , we define the transcriptional responsiveness of a paralog X1 as the log2 of the ratio of its mRNA expression levels in the Δx2 and the wild-type backgrounds , . mRNA levels in the wild-type and paralog deleted backgrounds were measured by real-time PCR for most of the protein-responsive genes as well as for some nonresponsive controls ( Materials and Methods; Table S3 ) . The majority ( 25 out of 32 ) of the tested genes are consistent with transcription being the sole source of responsiveness ( Figure 2C ) . Seven genes are interesting exceptions: GIN4 , IMD4 , HOR2 , HXK1 , EMI2 , MMF1 , and IMD3 , which show significant difference between their mRNA and protein levels suggesting posttranscriptional control ( Figure 2C , red circles ) . Strong translational up-regulation in the absence of transcriptional control has been previously observed for HOR2 during osmotic stress [40] , [41] . For GIN4 , IMD3 , and MMF1 , there is significant opposing transcriptional and posttranscriptional regulation . Are there any special features of paralog-responsive genes ? We find that responsiveness is enriched in gene pairs that have similar expression profiles , regulatory motifs , and amino acid sequences ( Figure S4 ) . The functions of proteins that show responsiveness are very diverse . They include metabolic enzymes ( e . g . , Sam1 , Ade17 , Pgm2 , Hxk1 ) , cell-cycle proteins ( Gin4 , Pph22 , Vhs2 ) , Golgi proteins ( Gga1 , Sro7 ) , and heat-shock proteins ( Hsp82 , Hsc82 ) ( Figure 2B; Table S2 ) . Amongst these , paralog-responsiveness is enriched in genes with metabolic function ( p = 0 . 037 , Fisher exact test ) . Further , paralog responsiveness is more likely to occur in genes expressed at high levels in the wild type ( p = 0 . 01 , Figure S5 ) . Although high expression is correlated with metabolism [20] , [42] , [43] , enrichment for high expression is significant even when accounting for a bias towards metabolic genes in the responsive set ( Figure S5 ) . This enrichment for highly expressed proteins raises the hypothesis that genes that contribute more to viability may show greater paralog responsiveness . Indeed , it has been suggested that responsiveness of functionally overlapping essential genes could provide a mechanism for compensation for perturbations in protein abundance [27] . If responsiveness is related to viability , it should appear preferentially in paralogs that have overlapping essential functions in a given growth condition . Such paralogs with overlapping essential function should show synthetic interactions , i . e . , deletion of both paralogs should have a much larger effect than expected from the effects of the single knock-outs . To test this idea , we compared our list of paralog-responsive genes in rich medium with a catalog of the phenotypes of single and double knockouts of duplicate genes characterized in the same conditions [22] . We categorized gene pairs into two classes: noninteracting ( neutral ) and synthetic sick/lethal interactions ( SSL ) , according to whether the double-mutant growth rate is equal to or more severe than expected based on the growth rates of the two corresponding single mutants . We found that paralog responsiveness is strongly enriched in gene pairs with SSL interactions ( Figure 3; p = 0 . 004 , Fisher exact test ) , and very rarely observed in genes with neutral genetic interactions ( Table S2; the only exceptions are VHS2 and CUE4 , which show marginally significant paralog responsiveness ) . If responsiveness is enriched in gene pairs important for viability , one might expect to observe more paralog-responsive genes in a more metabolically challenging environment . To test this , we measured responsiveness in a nitrogen-poor minimal medium , using the entire set of paralog-deleted strains , and repeated the analysis of paralog responsiveness described for rich medium ( Figure S6 ) . We observed a new set of paralog-responsive genes specific to this medium ( Figure 4 , magenta dots ) . These genes include three functional classes: mitochondrial proteins with roles in iron regulation/function ( Mrs4 , Isu1 , and Isu2 ) ; vesicular transport/regulation proteins ( Yap1802 , Gga1 , Sna3 , Sds24 ) ; and proteins involved in amino acid biosynthesis and glycosis ( Ser33 , Asn2 , Pyc2 , Pgm1 , Eno2 , and Lys20 ) . Other genes are responsive in both conditions , or specific to rich medium , and the majority of genes do not respond in either condition ( Figure 4 , black , cyan , and gray dots ) . We compared the paralog-responsive genes in minimal medium to quantitative data of SSL interactions between the paralogs under this condition [21] . Reinforcing the correlation observed in rich medium ( Figure 3 ) , we find that 50% of SSL gene pairs are paralog responsive , whereas none of the nonresponsive genes are SSL under these conditions ( Figure S6; p = 0 . 001 , Fisher exact test ) . This exclusiveness of paralog responsiveness to gene pairs with overlapping function critical for growth , together with the observation of amino acid biosynthetic genes showing paralog responsiveness specific to minimal media , indicate that responsiveness may be need-based , appearing only in conditions in which the gene's function is required . To test the need-based responsiveness hypothesis more directly , we asked three questions: ( 1 ) Is the responsiveness of amino acid biosynthesis genes in minimal medium specific to environments that lack the amino acid ? Likewise , ( 2 ) do genes that respond in both rich and nitrogen-poor conditions cease to respond in a condition that eliminates the need for their function ? and finally , ( 3 ) do genes that do not respond in either condition respond in conditions in which their function becomes needed ? We concentrated on several genes for which we could identify conditions that specifically generate or remove their functional need and measured their paralog-responsiveness under these conditions ( see Text S1 for a detailed description of this set of genes ) . For minimal-medium–specific responsive proteins , we concentrated on the amino acid biosynthesis enzymes Lys20 , Asn2 , and Ser33 . We tested whether the responsiveness of these genes disappears when their respective amino acid is provided ( Figure 5A–5C ) . Double mutants of LYS20-LYS21 , ASN1-ASN2 , or SER3-SER33 are synthetic lethal in minimal medium , but viable if the relevant amino acid ( lysine , asparagine , or serine ) is added [44]–[47] . Thus , adding these amino acids removes the need for the corresponding gene pair . Indeed , we find that paralog responsiveness of Lys20-GFP , Asn2-GFP , and Ser33-GFP is specifically eliminated in the presence of lysine , asparagine , and serine , respectively ( Figure 5A–5C ) . This loss of response upon complementation of the function appears in all three genes independently of their roles as the main or secondary isoform , and despite their different wild-type regulation by their cognate amino acid . Further , paralog responsiveness disappeared only upon the addition of the corresponding amino acid and not when any of the other amino acids was added ( Figure S7; see legend for discussion of one exception ) . We conclude that paralog responsiveness of the amino acid biosynthesis genes is specific to an environment lacking the corresponding amino acid , namely to an environment in which the gene function is needed . We then examined HXK1 as an example of a gene that responded strongly in both rich and minimal media ( Figure 4 ) , and considered a new condition that would eliminate the need for its function . HXK1 encodes hexokinase isoenzyme 1 , which catalyzes the first irreversible step of glycolysis . This function will not be needed when cells are grown under a nonfermentable carbon source , such as ethanol . We find that the strong responsiveness of Hxk1-GFP seen in minimal glucose medium is completely abolished when cells are grown on ethanol as a source of carbon ( Figure 5D ) ; again , paralog responsiveness disappears when the gene's function is not needed . Finally , we asked whether we could find conditions that would induce responsiveness in genes that do not respond in either rich or minimal medium ( Figure 4 , gray dots ) . We analyzed two nonresponding enzymes in glycerol biosynthesis pathway , Rhr2 and Gpd2 , which are known to play a role in protection against osmotic stress . Although both Rhr2-GFP and Gpd2-GFP do not respond to deletion of their paralogs ( HOR2 and GPD1 , respectively ) in rich and synthetic complete media , they show strong paralog responsiveness in osmotic stress ( 0 . 5 M KCl; Figure 5E and 5F ) . Interestingly , this need-based response to paralog deletion occurs in GPD2 despite the fact that it is not up-regulated by osmotic stress in the wild type ( see [48] and Figure 5F , histograms ) . These results , therefore , reinforce our hypothesis that paralog responsiveness is specific to the conditions in which the gene function is needed .
Our quantitative protein-level measurements show that , in any given growth condition , responsiveness to paralog deletion is restricted to a small number of genes . Responsiveness occurs at both the transcriptional and posttranscriptional level . With almost no exceptions , such paralog responsiveness occurs only when the genes are synthetic lethal , namely , when they have an overlapping biochemical function that is critical for growth in the tested conditions . Removing or adding the need of a function , either by supplying its end product or by shifting to conditions in which its product is not required , specifically determines whether or not a given gene will respond to deletion of its paralog . The mechanisms underlying need-based responsiveness are most likely complex . In principle , responsiveness of a gene to deletion of its paralog could reflect either a direct response to the absence of the paralogous protein ( similar to supply control ) , or an indirect response to the absence of its function ( similar to demand control [49] ) ( Figure S8A ) [27] . A simple mathematical model of a metabolic pathway exemplify that indirect responsiveness should depend on the presence of the product of the pathway in the environment ( Figure S8B and S8C; Text S2 ) . Indeed , we found that for the amino acid biosynthetic genes , the addition of the amino acid end product eliminates paralog responsiveness ( Figure 5A–5C ) , suggesting that responsiveness is not due to the absence of the paralogous protein but rather to the absence of its function . Such paralog responsiveness may therefore reflect a simple end-product regulation of genes . This supports the demand strategies previous identified in glycolysis [49]–[52] . Indeed , feedback regulation often occurs in the first committed step of a pathway , and these metabolic branching points are known to be enriched for duplicated genes [53] , [54] . This logical argument is based on the notion that addition of the end product of a pathway supplements its biosynthetic function . The argument , therefore , does not apply to conditions that instead of supplying the end product simply remove the need of the function . For example , yeast cells need to accumulate glycerol only in osmotic stress; removing the osmotic stress relieves the need for the glycerol biosynthetic pathway not by externally supplying its end product , glycerol , but rather by generating conditions in which this end product is not needed . This is in contrast to the case of the amino acid biosynthetic genes; we therefore cannot conclude from our data that the mechanism underlying responsiveness of Hxk1 , Rhr2 , and Gpd2 is indirect . Indeed , the responsiveness of Hxk1 may be mediated by direct regulation of its paralog; nuclear Hxk2 is involved in repression of HXK1 and expression of its own gene , HXK2 [55] , [56] . In agreement with these observations , we find that either the absence of glucose or the absence of HXK2 results in Hxk1 up-regulation ( Figure 5D ) . These differences in the underlying mechanisms of responsiveness underscore the breadth of its functional roles and suggest that in some cases , responsiveness to paralog deletion could even depend on the presence of other ( nonparalogous ) genes [57] . Genetic redundancy is a salient feature of living organisms . It has long been discussed under what circumstances genetic redundancy is evolutionary stable [58]–[60] and how redundancy can contribute to genetic robustness [61]–[63] . Interestingly , we uncovered a set of genes that are not up-regulated under a specific condition unless their paralogs are deleted . This and other cases of need-based responsiveness of genes to the absence of their paralogs could play an adaptive role in the compensation of functions that are compromised by genetic , environmental , or stochastic perturbations .
Deletion strains were from the yeast deletion collection [36] , xxxΔ::KANMX4 in the S288C derivative BY4741 background ( MATa his3Δ1 leu2Δ0 ura3Δ0 met15Δ0 ) . GFP protein fusions were obtained from the GFP library[33] , XXX-GFP ( S65T ) ::SpHIS5MX6 in the same BY4741 background . Fluorescent starter strains Y8205-RFP and Y8205-CFP were generated by direct PCR-based gene replacement of the neutral HO locus with the pFA6a cassettes mCherry-NATMX4 ( RFP ) and yECerulean-NATMX4 ( CFP ) , respectively , in the Y8205 strain ( MATα can1Δ::STE2pr-SpHIS5 lyp1Δ::STE3pr-LEU2 his3Δ1 leu2Δ0 ura3Δ0 met15Δ0 ) [37]; strong constitutive expression of fluorescent proteins is driven by the TDH3 promoter . The following growth media were used: ( 1 ) rich medium: yeast extract peptone dextrose ( YPD ) ; ( 2 ) minimal nitrogen-poor medium ( MM ) : yeast nitrogen base without amino acids and ammonium sulfate with 2% glucose , 0 . 2% proline as a nitrogen source , and supplemental methionine ( 25 mg/l ) ; ( 3 ) minimal nitrogen-poor medium with 1 mg/l lysine ( MM+Lys ) , 1 mg/l asparagine ( MM+Arg ) , or 1 mg/l serine ( MM+Ser ) ; ( 4 ) SD: synthetic complete medium with 2% glucose; ( 5 ) SC-EtOH: synthetic complete medium with 2% ethanol; or ( 6 ) SC+KCl: synthetic complete with 2% glucose and 0 . 5 M KCl . All strains in this study are prototrophic except for methionine production . To confirm that supplied methionine levels were not having a major effect on our results , we examined responsive under two different methionine concentrations 25 mg/l ( the amount used in the standard growth medium for logarithmic growth [64] ) and 100 mg/l ( the amount needed for maximal yield of cells at saturation [65] ) . Our results were largely unaltered by changing methionine levels ( Figure S9 ) . Arrays of GFP-tagged proteins in wild-type and knockout backgrounds were generated by two rounds of synthetic genetic array methodology ( SGA ) [37] . Briefly , the RFP-tagged SGA starter strains were mated to an array of 687 deletion strains , Δx2 . This mating step was followed by diploid selection , sporulation , and three rounds of haploid selection ( −LEU for alpha mating type , +G418 for knockout , and +clonNAT for fluorescence marker selection ) . In a second SGA round , the resulting arrays were crossed to their paralogous corresponding strains X1-GFP from the GFP library [33] , and the diploids were selected ( −LEU −HIS +G418 +clonNAT selection ) . To obtain the same X1-GFP fusion in a wild-type background with a different color tags , the CFP-tagged starter strain was mated to a strain with a neutral KANMX4 insertion at the his3Δ1 locus . Dye swaps ( deletion in CFP and wild-type in RFP ) were also generated as described above . The libraries were constructed in quadruplicate—two replicates of the two dye swaps . Colony arrays were transferred manually with a 384-head pin tool ( V&P Scientific , VP384F ) ; antibiotic concentrations used for selection were 200 µg/ml G418 ( Invitrogen ) , 100 µg/ml clonNAT ( Werner BioAgents ) . A schematic of the entire strain generation procedure is shown in Figure S1 . Quality control testing of the strain arrays included: ( 1 ) fluorescence intensity of the entire library by flow cytometry and correlation with data from the literature [34]; ( 2 ) verification of GFP subcellular localization by microscopy of 50 random strains based on the reported protein localization [33]; and ( 3 ) PCR verification of the insertion site for one eighth of the rearrayed deletion library . These tests indicated that one of the four replicates was systematically inconsistent for one half of the arrays ( X1-GFP not matching its corresponding Δx2 ) . These strains were eliminated for further analysis , leaving three replicates instead of four for approximately one half of the data . Ninety percent to 95% of the remaining strains were confirmed as correct for GFP fluorescence intensity and localization , and for deletion site . Finally , two control libraries were generated following the SGA steps described above . The first control library contained a constant GFP fusion of the ribosomal protein RPL41B in either a wild-type background or one of the 687 deletions described above . A second control library of 364 GFP-fusions with random ( nonparalogous ) deletion backgrounds was constructed by crossing an array of GFP fusion strains to the inverted corresponding array of deletion collection strains . As for the main X1-GFP Δx2 library , two replicates of the two dye swaps were generated for these control libraries . Each library was grown individually to saturation in 96-well plate format . Medium ( 600 µl ) was dispensed with a MicroFill Microplate Dispenser ( BioTek ) onto 1 . 0-ml polypropylene plates ( Nunc 260251 ) , and cultures were incubated in a Multitron Infors platform shaker at 30°C with shaking at 999 rpm . Each experimental run involves coculturing two libraries; one constitutively expressing CFP and the other constitutively expressing RFP . The two libraries were mixed in one 96-well plate by combining equal volumes of liquid from the saturated library plates described above . A 96-pin tool ( V&P Scientific , VP 407 ) was then used to inoculate a fresh plate in the medium of interest . Strains were then grown to mid-log phase ( ∼10 h in YPD or ∼14 h in MM ) . To analyze the libraries , cells were first transferred into 100 µl of TE ( 10 mM Tris and 1 mM EDTA [pH 8] ) , by two rounds of centrifugation at 3 , 000 g for 3 min , followed by liquid removal and resuspension in 600 µl of TE . Each pair of X1-GFP X2 and X1-GFP Δx2 was measured six to eight times ( two replicates of three to four independently constructed strains ) . A flow cytometer with a high-throughput autosampler ( LSRII with a HTS , Becton Dickinson ) was used to record fluorescence from GFP , CFP , and RFP fluorophores . GFP was excited with a 488-nm laser , and fluorescence was collected through a 525/50 band-pass and 550LP emission filter . CFP was excited with a 405-nM laser , and fluorescence was collected through a 450/50 band-pass filter and a 505LP emission filter . RFP was excited with a 593 . 5-nm laser , and fluorescence was collected through a 630/20 band-pass and a 640LP emission filter . Cells were measured in high-throughput mode at a flow rate of 0 . 5 µl/s for 8 s . Data analysis was performed largely as described by Newman et al . [34] with the exception of using a trimmed mean and a less stringent size cutoff . Custom Perl and Matlab scripts using FCSread . m ( Robert Hanson , available at Matlab central ) were written to import the FCS raw data ( Graw , GFP; Craw , CFP; Rraw , RFP ) . For each well , analysis followed the following steps: ( 1 ) Remove cell debris and aggregates based on the forward and side scatter ( an approximation of cell size ) . ( 2 ) Correct for crosstalk between fluorophores: C = Craw − Graw /10 . ( 3 ) Classify the cells into RFP expressing ( if Rraw /C >20 ) or CFP expressing ( if C/Rraw >20 ) , and record the GFP level and from these two population , respectively . This classification eliminates dead cells ( no fluorescence in either channel ) and doublets ( fluorescence in both channels; appeared at rate of less than 1% ) . ( 4 ) Eliminate the 10% outlier values of and ( 5% strongest and 5% weakest ) . ( 5 ) Calculate the mean ( , ) and standard deviation ( , ) of the GFP fluorescence of each population . ( 6 ) Correct for autofluorescence and crosstalk: and , where and are the mean GFP fluorescence of 40 control strains expressing only the RFP or CFP , but not GFP . Any strain that did not have GFP fluorescence in both the wild-type and deletion strains greater than 50% above the background fluorescence or a GFP fluorescence greater than twice the background in either of the strains was eliminated . This eliminated ∼66% of the strains . This is a more stringent cutoff than previous metrics , which solely tried to determine the number of strains above background and were able to detect 50% of all strains [34] . The responsiveness was calculated as R = log2 ( GRFP/GCFP ) , for mutant RFP and wild-type CFP , or R = log2 ( GCFP/GRFP ) for the reverse “dye swap . ” Multiple lines of evidence support the use of GFP fusion proteins to accurately reflect responsiveness of the endogenous proteins . First , based on tagging of essential and nonessential proteins , most GFP-fusions are believed to generate functional proteins [33] , [34]: i . e . , genes missing from the GFP and TAP fusion collections are not enriched for essential genes . Second , protein levels determined by mass spectrometry give similar protein levels as those determined by flow cytometry of GFP fusions [66] . Third , our method is ratiometric . Even if the GFP fusion affected the protein levels ( e . g . , through stability or translatability ) , our method would only erroneously detect responsiveness if such presumed artificial effect of the GFP fusion was altered by the presence or absence of the paralog of the gene . Finally , independent measurements of responsiveness of tagged and untagged proteins for several genes by Western blot give very similar results to the GFP fluorescence measurements ( Figure S10 ) . The median and standard deviation of the responsiveness metric was calculated from the six to eight replicates of measurements of responsiveness of each gene . For each strain , we calculated the “local error” ΔRL as the standard deviation of R of that strain over its six to eight replicate measurements . As seen in Figure S2A , this value is influenced by the total fluorescence of the strain . Due to the inaccuracy of calculating the standard deviation with six to eight measurements , we also calculated a global error , ΔRG , which is a moving-window median of the local error of 41 adjacent measurements sorted by total fluorescence ( Figure S2A , dashed line ) . The total error that we then used for statistics was ΔRT , defined by ( ΔRT ) 2 = ( ΔRL ) 2 + ( ΔRG ) 2 . The replicate measurements within the same dye-swap had much smaller variance compared to the difference between the dye-swaps . Therefore , we used 2 as the effective number of independent measurements and calculated the standard deviation of the mean as ΔRT/√2 . A null hypothesis was then generated by simulating the experiment ( global and local error for each strain ) by randomly sampling a normalized Gaussian distribution . This was repeated 100 , 000 times and the 95% confidence interval determined from this simulated dataset . We measured mRNA levels of our GFP fusion proteins using quantitative PCR ( qPCR ) . Wild-type X1-GFP and Δx2 X1-GFP strains were separately grown in 30 ml of YPD and harvested at mid-log phase after 10 h of growth . Total RNA was extracted and cDNA was obtained from each sample using reverse transcriptase ( Superscript III RT , Invitrogen ) , which was used as a template for real-time PCR using primer pairs to amplify GFP and a control gene ACT1 from each sample . Because each gene in our study was GFP tagged , a universal set of GFP primers could be used . To normalize for variations in mRNA extraction , the X1-GFP mRNA level was defined relative to the ACT1 level , , where E is the PCR efficiency and T is the product detection time in number of qPCR cycles . Paralog responsiveness at the mRNA level was then calculated as . Table S3 contains the qPCR data . Expression levels were obtained from at least three technical qPCR replicates . To obtain an estimate for the experimental variation in our measurement , Rtranscript was measured in duplicate for Cot1 , Hxk1 , and Sam1 , and in triplicate for Sam2 ( see Table S3 ) . The standard deviation of log2 ( mRNA ) was 0 . 25 , yielding standard deviation of 0 . 4 in Rtranscript . We used a significance cutoff of two standard deviation ( 95% confidence interval ) , or 0 . 8 , for Rtranscript ( gray shaded area in Figure 2C ) . Anti-yeast hexokinase antibodies ( ABCAM ab34588 ) were used to detect Hxk1 and Hxk2; Lys20 and Lys21 were detected with Lys 20p + 21p antibody ( ABCAM ab4574 ) . Lys20 and Lys21 can be separated by electrophoretic mobility . We could not electrophoretically separate Hxk1 and Hxk2 . To monitor the untagged version of Hxk1 , we therefore monitored its level in the absence or presence of Hxk2-GFP . Hxk2-GFP is electrophoretically separable from Hxk1 and hence does not interfere with the measurement of the untagged Hxk1 . We similarly examined Hxk2 in an Hxk1-GFP background . Samples were lysed in boiling 2× Laemlli buffer in the presence of a protease inhibitor cocktail ( PMSF PLUS Roche #11836153001 ) . Samples were run on precast NuPage ( NP0321BOX ) gels and transferred to nitrocellulose membranes . The Odyssey protocol was followed . Goat anti-mouse 680 ( Alexa Fluor A-21057 , 1∶5 , 000 ) and goat anti-rabbit 680 ( Alexa Fluor A-21076 , 1∶5 , 000 ) secondary antibodies were used . The fluorescence was quantified by Odyssey system ( Li-COR ) . All measurements were made in duplicate or triplicate . The linearity of each antibody was confirmed by titrating both the primary antibody concentration and the substrate concentration . The working dilutions were 1∶2 , 000 and 1∶500 for the Hxk1/2 and Lys20/21 antibodies , respectively . The hexokinase antibody also reacted with a nonspecific band that was unaffected by medium and genetic background . Hxk1/2 antibody was used to detect this background band ( C , control ) for quantification in Figure S10 . We also used a CEP3 and ACT1 antibody to control for loading , but the standard deviation of all our replicate measurements was lowest when normalized against the background band detected with the Hxk1/2 antibody .
|
Despite sequence divergence over long evolutionary times , many genes that have undergone duplication can still compensate for the loss of their duplicates . This compensation depends , not only on functional overlap between the paralogous genes , but also on overlap in their expression patterns . It has been proposed that compensation might therefore involve active up-regulation of a gene in response to deletion of its paralog . To test for such paralog responsiveness in the yeast Saccharomyces cerevisiae , we systematically measured changes in single-cell protein levels for approximately 200 duplicate genes in the presence or absence of their paralogs . Only a small fraction ( ∼11% ) of proteins increased in level in response to deletion of their paralog , but this set matched almost exclusively the subset of paralogs whose overlapping function is required for viability . Further , when we examined yeast grown in different media , we found that genes had either gained or lost paralog responsiveness exactly according to their importance for growth in the tested conditions . Responsiveness , therefore , is need-based: it appears only in conditions in which the function of one or both paralogs is required . We propose that such need-based responsiveness of duplicate genes could play an important adaptive role , not just in the artificial event of paralog deletion , but also in the maintenance of functions that are compromised by natural genetic , environmental , or stochastic perturbations .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/functional",
"genomics",
"genetics",
"and",
"genomics/gene",
"expression",
"microbiology/microbial",
"evolution",
"and",
"genomics",
"evolutionary",
"biology/genomics",
"genetics",
"and",
"genomics"
] |
2010
|
Need-Based Up-Regulation of Protein Levels in Response to Deletion of Their Duplicate Genes
|
Coxiella burnetii is an intracellular bacterial pathogen that infects alveolar macrophages and replicates within a unique lysosome-derived vacuole . When Coxiella is trafficked to a host cell lysosome the essential Dot/Icm type IV secretion system is activated allowing over 130 bacterial effector proteins to be translocated into the host cytosol . This cohort of effectors is believed to manipulate host cell functions to facilitate Coxiella-containing vacuole ( CCV ) biogenesis and bacterial replication . Transposon mutagenesis has demonstrated that the Dot/Icm effector Cig57 is required for CCV development and intracellular replication of Coxiella . Here , we demonstrate a role for Cig57 in subverting clathrin-mediated traffic through its interaction with FCHO2 , an accessory protein of clathrin coated pits . A yeast two-hybrid screen identified FCHO2 as a binding partner of Cig57 and this interaction was confirmed during infection using immunoprecipitation experiments . The interaction between Cig57 and FCHO2 is dependent on one of three endocytic sorting motif encoded by Cig57 . Importantly , complementation analysis demonstrated that this endocytic sorting motif is required for full function of Cig57 . Consistent with the intracellular growth defect in cig57-disrupted Coxiella , siRNA gene silencing of FCHO2 or clathrin ( CLTC ) inhibits Coxiella growth and CCV biogenesis . Clathrin is recruited to the replicative CCV in a manner that is dependent on the interaction between Cig57 and FCHO2 . Creation of an FCHO2 knockout cell line confirmed the importance of this protein for CCV expansion , intracellular replication of Coxiella and clathrin recruitment to the CCV . Collectively , these results reveal Cig57 to be a significant virulence factor that co-opts clathrin-mediated trafficking , via interaction with FCHO2 , to facilitate the biogenesis of the fusogenic Coxiella replicative vacuole and enable intracellular success of this human pathogen .
The intracellular bacterial pathogen Coxiella burnetii is the causative agent of human Q fever , a zoonotic disease with the potential to cause life-threatening complications . Transmission to humans occurs via inhalation of contaminated aerosols . Human infection can lead to an acute , pneumonia-like illness , or proceed to a chronic disease state in which endocarditis can manifest [1] . During natural infection , Coxiella predominantly invades alveolar macrophages , and in order to replicate intracellularly , a spacious and fusogenic lysosome-derived vacuole , termed the Coxiella-containing vacuole ( CCV ) , is established by the pathogen . After internalization , Coxiella passively traffics through the endolysosomal pathway [2 , 3] . The developing vacuole obtains markers typical of early and late endosomes , such as EEA1 and Rab7 , and finally matures to a lysosome [4 , 5] . Here , with an internal pH of approximately 4 . 8 , and in the presence of proteolytic and degradative enzymes , Coxiella becomes metabolically active and will direct the expansion of the CCV before replicating to large numbers [6] . The active form of Coxiella is the replicative large cell variant ( LCV ) , distinct from the environmentally stable small cell variant ( SCV ) [7] . The exact requirements that render the CCV permissive for replication are unknown , however recent mutagenesis studies have demonstrated that a Dot/Icm type IVB secretion system is essential for CCV biogenesis and intracellular replication [8 , 9] . This secretion system is activated by the lysosomal environment [10] and more than 130 Coxiella effector proteins are known to be translocated from the pathogen into the host cell [8 , 11–18] . Multiple mutagenesis studies have identified a small cohort of Dot/Icm effectors that play important roles in CCV biogenesis and intracellular replication of Coxiella [14 , 16 , 19 , 20] . However , the function of most of these effectors and why they are required for intracellular success of Coxiella remains to be elucidated . Using HeLa cells as an important model for infection , various host cell vesicular trafficking pathways have been shown to facilitate CCV development and contribute to the infection cycle of Coxiella . The retromer trafficking process , required for retrograde transport from endosomes to the trans-Golgi network [21] , has been shown to contribute to the maturation of the CCV with retromer subunits VPS35 and VPS29 and sorting nexins all required for expansion of the CCV [22] . In addition , v-SNAREs VAMP3 , VAMP7 and VAMP8 are found on the CCV membrane and have been shown , via siRNA experiments , to aide fusion events with vesicles in the endolysosomal pathway [23] . The autophagic SNARE , syntaxin-17 , is required for the homotypic fusion of CCVs [22 , 23] . Indeed , perturbation of autophagy , through both enzymatic inhibition and siRNA treatment of key proteins required for autophagy , results in a multi-vacuole CCV phenotype [20] . Coxiella , does not appear to induce host cell autophagy but interaction of the CCV with autophagosomes is demonstrated by the accumulation of LC3 inside the CCV , and Rab27 on the vacuole membrane [20 , 24] . This indicates that successful CCVs are most accurately described as autolysosomes [24] . Recently , host cell clathrin-mediated trafficking was also shown to be important for intracellular replication of Coxiella [25] . Clathrin is important for endocytosis as well as trafficking events within cells . Clathrin-mediated endocytosis is the process by which host cells internalize material , termed cargo , into clathrin-coated pits , to then be sorted to their subcellular compartments ( For a review see [26] ) . Larson and colleagues showed that siRNA gene silencing of CLTC ( clathrin ) and AP2B1/AP2M1 ( AP-2 ) , but not AP1 , significantly impeded the intracellular replication of Coxiella . The adaptor complex AP-2 acts at the plasma membrane during clathrin-mediated endocytosis , and both AP-1 and AP-3 facilitate clathrin-mediated trafficking from the trans-Golgi network [27] . These key proteins , required for endocytosis events at the plasma membrane , facilitate the normal growth of Coxiella [25] . Thus , the CCV interacts with multiple host vesicular trafficking processes for successful CCV expansion and intracellular replication . These host pathways are likely manipulated and controlled by complex interactions with Coxiella Dot/Icm effector proteins . For example , initial studies linked the multi-vacuolar CCV phenotype seen in cig2::Tn mutants with the multi-vacuolar phenotype seen when silencing host autophagy components [20] . More recently , the effector Cig2 was found to bind phosphoinositide PI ( 3 ) P , and this was required for recruitment of autophagy machinery components to facilitate homotypic fusion of CCVs [24 , 28] . Additionally , the effector CvpA was found to modulate the association of Coxiella with the clathrin trafficking pathway through interaction with AP-2 . CvpA is required for intracellular replication of Coxiella and this is believed to be linked to the acquisition of endolysosomal lipids and proteins through subversion of the clathrin transport pathway . Cig57 ( CBU1751 , a 48 . 8 kDa protein , 420 amino acids ) , initially identified as an effector because the promoter region contains a PmrA binding region that indicates that the gene is co-regulated with icm genes [29] , is also required for intracellular replication of Coxiella [20] . A transposon-mutagenesis screen to identify novel factors that influence CCV morphology identified multiple transposon insertions , disrupting cig57 , that caused a significant bacterial growth defect and small vacuole phenotype [20] . Hence this effector plays a vital role in establishing the Coxiella intracellular replicative niche . Similar to CvpA , Cig57 contains endocytic sorting motifs that mimic those recognised by adaptor protein complexes in the host cell . In Cig57 there are three endocytic sorting motifs , two dileucine motifs ( [DERQ]xxxL[LI] ) , and one tyrosine motif ( YxxΦ ) , where x represents any amino acid and Φ is a bulky hydrophobic residue [30 , 31] . Adaptor protein complexes bind to these motifs usually found on transmembrane proteins such as the transferrin receptor ( TfR ) , to facilitate their selection and uptake into clathrin-coated vesicles ( For a review see [30] ) . Herein , we examine the function of Cig57 by identifying and examining its interaction with the host protein FCHO2 . FCHO2 ( 88 . 9 kDa , 810 amino acids ) is part of the muniscin subfamily of the EFC domain ( extended Fes-CIP4 homology ) proteins . Muniscins act at the early stages of clathrin-mediated endocytosis and have been implicated in the initiation of clathrin-coated pits [32 , 33] . Specifically , the N-terminal EFC domain of FCHO2 dimerizes and binds the inner plasma membrane , binding to PI ( 4 , 5 ) P2 enriched membranes [33 , 34] , to enable the curvature seen in the neck of clathrin-coated vesicles . We show FCHO2 is required for optimal CCV formation , and establish that Cig57 , interacting with FCHO2 via a tyrosine-based endocytic sorting motif , subverts clathrin to the CCV to facilitate normal vacuole biogenesis and intracellular replication of Coxiella .
To identify potential host protein targets of Cig57 , a yeast-two-hybrid ( Y2H ) assay was performed using the full length Cig57 as bait and a HeLa cDNA library as prey . Cig57 alone did not activate reporter gene expression , as Saccharomyces cerevisiae ( Y2H Gold , Clontech ) carrying pGBKT7-cig57 could not grow on quadruple dropout ( QDO ) yeast minimal media ( YMM ) plates ( -Trp , -Leu , -His , -Ade ) . After screening , only one positive prey clone was identified from the cDNA library , and the insertion was sequenced . This clone encoded amino acids 1 to 433 of the human protein FER/CIP 4 homology only protein 2 ( FCHO2 ) . The interaction between Cig57 and FCHO2 1–433 was confirmed by re-transformation of both pGBKT7-cig57 and pGADT7-FCHO2 ( 1–433 ) into a different S . cerevisiae strain ( AH109 ) . S . cerevisiae harbouring both pGBKT7-cig57 and pGADT7-FCHO2 grew on both double-dropout ( DDO , -Trp , -Leu ) and QDO YMM agar plates ( Fig 1A ) . No interaction was observed with either pGADT7 or pGBKT7 empty vectors . We next sought to confirm this interaction within mammalian cells using an immunoprecipitation method . Importantly , this was done in the context of infection to address whether the interaction between Cig57 and FCHO2 occurs during infection . HEK 293T cells were infected with Coxiella transposon mutant cig57::Tn expressing 3xFLAG-Cig57 from a plasmid or WT Coxiella as a negative control . These cells were then transfected to express either GFP or GFP-FCHO2 . Lysates of infected and transfected cells were incubated with beads that bind GFP , and protein bound to the beads were probed with anti-GFP or anti-FLAG antibodies by Western blot . We detected FLAG-Cig57 in the immunoprecipitate of cells expressing GFP-FCHO2 , but not in cells expressing GFP alone , validating the interaction between FCHO2 and Cig57 ( Fig 1B ) . We next sought to determine the intracellular localization of Cig57 . In WT infected cells , we transfected mCherry or mCherry-Cig57 , and observed that while mCherry had diffuse localization in cells , mCherry-Cig57 is enriched at the CCV and in punctate structures in the cytoplasm ( Fig 1C ) . Because of the interaction with FCHO2 , we wanted to determine the relative localization of Cig57 and FCHO2 during infection . We engineered a HeLa cell line that constitutively expresses GFP-FCHO2 , and transfected these cells with mCherry-Cig57 . FCHO2 was not observed to be enriched at the CCV membrane , however FCHO2 and Cig57 co-localize at punctate structures in the cytoplasm ( Fig 1D ) . Endocytic sorting motifs are typically present on transmembrane receptor proteins , and are recognised by adaptor proteins for selection and sorting of cargo molecules for clathrin-mediated endocytosis [31 , 35] . Cig57 contains three predicted endocytic sorting motifs , two of a dileucine type , and one of the tyrosine type . To evaluate the importance of these motifs in Cig57 , we created an endocytic sorting motif mutant ( ΔESM ) construct with mutations in key residues of the predicted Cig57 endocytic sorting motifs ( LI82 , 83AA , LL275 , 276AA , Y365A ) , ( Cig57ΔESM ) , and evaluated whether this form of Cig57 could bind FCHO2 . To investigate whether FCHO2 can recognise these motifs , we transformed yeast with pGADT7-FCHO2 ( 1–433 ) and pGBKT7-cig57 containing individual mutations in the endocytic sorting motifs , or mutations in combination with each other ( Fig 2 ) . We observed growth of all transformants on DDO plates , which indicates yeast viability and successful plasmid uptake , and found no growth on QDO where pGADT7-FCHO2 ( 1–433 ) was transformed alongside pGBKT7-cig57ΔESM , indicating that one or more of the endocytic sorting motifs are involved in the Cig57-FCHO2 interaction ( Fig 2A , segment 8 ) . Individual mutations were also assessed for binding to FCHO2 , and we showed that while the dileucine motifs were not important for binding FCHO2 , the tyrosine residue Y365 , part of the endocytic sorting motif YRKF , is essential for binding to FCHO2 ( Fig 2A , segment 2 ) . Hence , we have been able to identify a Cig57 residue essential for this interaction and that FCHO2 might have the capacity to recognise tyrosine-based endocytic sorting motifs . Inability of the proteins to bind to each other was not due to lack of protein expression in the yeast , as all proteins were expressed from the pGAD ( anti-HA ) and pGBKT ( anti-c myc ) plasmids ( Fig 2B ) . Given the importance of the endocytic sorting motifs , particularly Y365 , for binding FCHO2 , we examined whether this mutated form of Cig57 could complement the lack of growth seen for the cig57::Tn strain . An intracellular growth curve was performed over five days ( Fig 3A ) which demonstrated the inability of cig57::Tn Coxiella expressing pFLAG-Cig57ΔESM or pFLAG-Cig57Y365A to grow to similar levels as WT Coxiella or cig57::Tn pFLAG-Cig57 . Interestingly , the ΔESM and Y365A versions of Cig57 are not completely inactive , as growth is not decreased to the same level as the cig57::Tn mutant . This phenotype was also observed visually by quantifying and comparing the CCV areas at 72h post-infection from three independent experiments ( Fig 3B ) . Using this measure , vacuole areas formed by the cig57::Tn pFLAG-Cig57ΔESM strain ( 28 . 5±4 . 6 μm2 ) are significantly smaller ( P = 0 . 0001 ) than those produced by WT ( 146 . 8±6 . 7 μm2 ) and the complemented mutant ( 143 . 1±7 . 5 μm2 ) ( P = 0 . 0002 ) . Interestingly , the vacuole sizes of cig57::Tn pFLAG-Cig57Y365A ( 46 . 9±1 . 8 μm2 ) were significantly larger than cig57::Tn pFLAG-Cig57ΔESM ( P = 0 . 020 ) which may suggest an additional role for the dileucine endocytic sorting motifs . Importantly , both Cig57Y365A and Cig57ΔESM shows significantly ( P = 0 . 0001 and P = 0 . 02 respectively ) larger vacuoles than cig57::Tn mutant ( 11 . 0±1 . 5 μm2 ) , indicating that during expression of Cig57ΔESM , vacuoles are of an intermediate size ( Fig 2C ) . Analysis of a representative experiment allowed us to visualize that indeed the distribution of vacuole sizes differed between the different strains ( Fig 3D ) . Thus Cig57 , and the Cig57 endocytic sorting motifs , particularly the Y365 residue , are important for both intracellular replication of Coxiella and expansion of the CCV . To illustrate that all of our Coxiella strains were equally as infective , we plotted raw genome equivalent ( GE ) values for Coxiella recovered at timepoint 0h ( 4 hours post infection ) , and show that there is no significant difference between the values obtained for each of the strains ( Fig 3C ) . The distribution of clathrin normally includes plasma membrane and cytosolic puncta throughout the cell [36] . During infection with Coxiella , clathrin has been observed around the CCV and this has been shown to require the AP-2 binding effector CvpA [25] . Given that Cig57 interacts with the clathrin related protein FCHO2 we sought to assess the contribution of Cig57 to the accumulation of clathrin on the CCV . HeLa cells were infected and stained for clathrin heavy chain 72 h after infection . Confocal micrographs showed an increased density of clathrin surrounding the CCV in cells infected with WT Coxiella and the cig57::Tn pFLAG-Cig57 strain ( Fig 4A ) . Importantly , during infection with the cig57::Tn mutant strain , or the mutant strain complemented with either pFLAG-Cig57ΔESM or pFLAG-Cig57Y365A clathrin is no longer recruited to the CCV ( Fig 4A ) . When the clathrin signal was quantified , the ratio of signal on the CCV compared to cytoplasmic signal was approximately 2 in WT ( 2 . 1±0 . 1 ) and the cig57::Tn pFLAG-Cig57 strain ( 2 . 0±0 . 1 ) , which was significantly higher than during infection with the mutant ( 1 . 1±0 . 1 ) , the ΔESM complemented mutant ( 1 . 3±0 . 05 ) , or the Y365A complemented mutant ( 1 . 2±0 . 04 ) ( Fig 4B ) . Datapoints from a representative experiment demonstrate the range of clathrin intensity ratios in a representative infection ( Fig 4C ) . In order to establish whether clathrin recruitment to the CCV was linked to the size of the CCV we examined the relationship between the area of WT Coxiella CCVs and clathrin intensity around the CCV , and found no correlation ( Fig 4D ) , with a R2 value of 0 . 029 and a non-significant slope ( P = 0 . 199 ) deviation from zero . These data implicate Cig57 , and its activity mediated by the endocytic sorting motifs , as required for clathrin recruitment to the CCV . To further validate that clathrin is recruited to CCV membranes , we co-stained infected HeLa cells with LAMP1 and clathrin , and show that when LAMP1 signal is high on the vacuole membrane , clathrin intensity likewise increases ( S1 Fig ) . However this is only true for WT CCVs . Clathrin on cig57::Tn vacuoles did not increase at the CCV membrane , denoted by high LAMP1 signal . Given the CCV localization of clathrin , we next utilized our GFP-FCHO2 stable HeLa cell line to explore the localization of FCHO2 . In uninfected cells , FCHO2 localized to the perinuclear region , and the plasma membrane . Likewise , in cells infected with WT Coxiella , FCHO2 localization did not change ( Fig 5A ) . Since clathrin was discovered to be on the CCV membrane , we asked whether FCHO2 is also recruited to the CCV . As shown in Fig 5A , and as quantified in Fig 5B , we saw no significant increase in FCHO2 signal on the membrane of the WT CCV . This is despite there being an increased signal of FCHO2 within the region of the CCV , yet there was also an increased FCHO2 signal around the nucleus , thus implying that the FCHO2 signal is not specific to the CCV . To further illustrate that FCHO2 is not recruited to CCVs , we stained infected GFP-FCHO2 cells with clathrin or LAMP1 , and note that while clathrin and LAMP1 intensity increases on the CCV membrane , FCHO2 signal does not ( Fig 5C and 5D ) . FCHO2 and clathrin are important for normal clathrin-mediated endocytosis . Without clathrin , clathrin-mediated trafficking is blocked . In the absence of FCHO2 , endocytosis still progresses , yet clathrin-coated pits are abnormally arranged , with AP-2-positive structures appearing enlarged and clustered [37] . We assessed what the impact of disrupting these genes has on the intracellular replication and CCV formation of Coxiella . HeLa cells were transfected with siRNA against clathrin heavy chain ( CLTC ) , FCHO2 , or OnTarget Plus ( OTP ) non-targeting ( OTP-NT ) . Cells were infected with Coxiella 2 days post-siRNA transfection ( day 0 post-infection ) and lysates were collected for immunoblotting to gauge the level of protein depletion at 0 , 2 , 4 and 6 days post infection ( Fig 6A ) . The level of knockdown achieved was quantified by measuring band intensities compared to that of the β-actin band intensity . Coxiella replication was measured by collecting cell lysates at days 2 , 4 and 6 post-infection , and performing qPCR analysis against the C . burnetii ompA gene to calculate GE . As expected , and described previously , the depletion of cellular clathrin significantly inhibits Coxiella growth ( Fig 6B and [25] ) . We compared the level of bacterial growth during silencing of FCHO2 also , and though not statistically significant ( P = 0 . 28 ) , there is a trend towards less bacterial growth when the FCHO2 gene is silenced ( Fig 6B ) . At day 4 post-infection , samples were fixed for immunofluorescence analysis ( Fig 6C ) to examine vacuole size during depletion of these key transcripts in clathrin-mediated endocytosis . The area of individual CCVs was quantified and plotted in Fig 6D , in which we show significantly smaller vacuoles when silencing either clathrin ( 43 . 4±3 . 9 μm2 , P = 0 . 0005 ) or FCHO2 ( 154 . 5±22 . 5 μm2 , P = 0 . 012 ) , compared to non-targeting conditions ( 303 . 4±25 . 5 μm2 ) . Individual datapoints from one representative experiment are plotted in Fig 6E , and show that during silencing of clathrin , vacuoles are uniformly small , and that there is a shift towards smaller vacuoles in FCHO2 silenced cells as noted by the population of smaller CCV areas . These data indicate that FCHO2 , as well as clathrin , is required for normal CCV biogenesis . While measuring vacuole sizes , we noted a multi-vacuolar phenotype during silencing of clathrin . Approximately 50% of cells displayed more than one CCV per cell during treatment with CLTC siRNA , compared to approximately 15% in non-targeting OTP siRNA ( Fig 6F ) . Uptake of Coxiella is not affected by silencing the clathrin pathway [25] . To corroborate this observation , we evaluated whether the disruption of the clathrin pathway affects the early stages of Coxiella infection . HeLa cells were treated with siRNA against CLTC or FCHO2 , and infected for four hours , at which time the cells were fixed and differentially stained for intracellular and extracellular bacteria . There was no difference in the number of intracellular bacteria recovered in either the OTP-NT or silencing conditions ( S2 Fig ) . This indicates that the entry of Coxiella is not disrupted in the absence of clathrin or FCHO2 . Using siRNA , clathrin was efficiently depleted from the cells until Day 6 however silencing of FCHO2 at days 4 and 6 post infection was of poor efficiency ( Fig 6 ) . This may contribute to the lack of a significant inhibition of Coxiella replication and a partial defect in CCV expansion ( Fig 6 ) . To overcome the problem of inefficient removal of FCHO2 , a HeLa cell line was created which is completely devoid of FCHO2 . Using the CRISPR-Cas9 genome editing system , HeLa cells were co-transfected with constructs targeting exon 1 and exon 5 of the FCHO2 gene , resulting in stable loss of protein production and a FCHO2 knockout ( KO ) cell line ( Fig 7A ) . These cells , alongside the HeLa parent cell line , were infected with Coxiella for 5 days , and the Coxiella GE were measured by ompA qPCR ( Fig 7B ) . There is a shift towards lower Coxiella replication at day 5 post infection in the FCHO2 KO cells compared to the wild-type parent HeLa cell line . At day 3 post-infection , samples were fixed and stained for Coxiella and LAMP1 to visualize CCV size ( Fig 7C ) . When quantified , vacuoles are significantly ( P = 0 . 0059 ) smaller during infection of our FCHO2 KO cell line ( 73 . 9±3 . 5 μm2 ) than when infecting the parental HeLa cell line ( 251 . 1±32 . 9 μm2 ) ( Fig 7D ) . We plotted individual vacuole sizes for one of the three experiments ( Fig 7E ) , to show the distribution of CCV sizes . We next asked the question whether clathrin recruitment to the CCV was still able to progress as previously observed in HeLa cells in our FCHO2 KO cells . Using WT Coxiella , we infected parental HeLa cells and our FCHO2 KO cell line for three days , and stained for clathrin . As expected , clathrin was found to surround WT vacuoles in HeLa cells , however we observed a lower proportion of FCHO2 KO cells harboured vacuoles that were positive for clathrin ( Fig 8A ) . Again , we measured LAMP1 intensity at a cross section of the vacuole , and show that clathrin increases at the CCV membrane , corresponding to high LAMP1 signal , on HeLa parent CCVs , CCVs formed in FCHO2 KO cells no longer show an increased clathrin intensity at the CCV membrane , where LAMP1 signal is increased ( S1 Fig ) . As in Fig 3 , clathrin intensity was approximately double the intensity at the CCV compared to the cytoplasm of wild-type HeLa cells ( ratio of 2 . 2±0 . 1 ) , however in FCHO2 KO cells , the ratio of clathrin intensity in the CCV compared to the cytoplasm was 1 . 3±0 . 3 ( Fig 8B ) . It is to be noted that this phenotype is not as substantial a difference as observed for the CCV clathrin intensity during infection with the cig57::Tn mutant , in which the ratio was 1 . 1±0 . 1 . Indeed , over one representative experiment , we observed a greater range of CCV/cytoplasm ratios during infection of the FCHO2 KO cells ( Fig 8C ) .
The identification and characterization of Dot/Icm effector proteins in Coxiella is an active field of research that has been significantly bolstered by the recent advances in axenic culture and genetic manipulation of Coxiella . However , ascribing functions to these unique effectors remains challenging . Here , we have identified a host factor and pathway , namely FCHO2 and clathrin-mediated endocytosis , that are targeted by the effector protein Cig57 . Clathrin-mediated endocytosis plays an essential role in all nucleated cells . Uptake and recycling of a variety of molecules , from plasma membrane receptors to iron is dependent upon the clathrin pathway . Additionally , clathrin is responsible for the trafficking of early endosomal vesicles to and from the trans-Golgi network playing an important role in delivering cargo proteins to their destination organelles [38 , 39] . It is known that clathrin dependent trafficking is essential for the intracellular Coxiella lifecycle , as silencing of clathrin results in diminished intracellular Coxiella replication ( [25] and validated in Fig 6 ) . CvpA , an effector required for intracellular replication of Coxiella , was shown to bind AP-2 , and now we have shown that another essential effector , Cig57 interacts with a different component of clathrin-coated vesicles , FCHO2 [25] . FCHO2 belongs to the muniscin family of proteins , which also includes FCHO1 and SGIP1 [40] . FCHO1 and 2 are thought to be involved in initiating clathrin-mediated endocytosis , though this is debated in the field as clathrin-mediated endocytosis still occurs in the host in the absence of FCHO1/2 albeit with abnormal morphology [33 , 37 , 41] . Nevertheless , FCHO2 arrives early to the site of clathrin-mediated endocytosis and aids in sculpting the plasma membrane to form the spherical clathrin-coated vesicles [32 , 34] . These proteins contain an N-terminal EFC domain responsible for membrane binding , dimerization and induction of membrane curvature , alongside a linker region , followed by a C-terminal μ-homology domain . The μ-homology domain is an interaction hub , facilitating binding to the clathrin accessory proteins EPS15 and intersectin [33] . The interaction we observed using the yeast two hybrid system with FCHO2 was restricted to the N-terminal region ( amino acids 1–433 ) , indicating that Cig57 must bind within the EFC domain or the linker region . Binding the EFC domain may mean Cig57 is altering the membrane-binding capacity of FCHO2 , by either blocking its ability to dimerize or bend membranes , or possibly even post-translationally modifying the protein in this region . We have shown that FCHO2 is required for normal CCV biogenesis during infection with Coxiella . CCVs are significantly smaller in FCHO2 KO HeLa cells and there is a trend towards reduced replication of Coxiella in these cells . Additionally , the Coxiella growth defect in the absence of FCHO2 is not as severe as the growth defect in the absence of clathrin . Taken together , this indicates a level of dependence on the clathrin-mediated pathway for growth of Coxiella , where the extent of intracellular bacterial growth may be proportional to the amount of endocytosis exhibited . FCHO1 is a close homologue of FCHO2 . They share approximately 50% homology overall at the protein level . However , the amount of FCHO1 present in HeLa cells is very low , such that endogenous FCHO1 is undetectable by Western Blot analysis [32 , 37 , 41] . This study has not determined whether Cig57 has the same affinity for FCHO1 as it does FCHO2 , but if it does , there is a potential for there to be a greater growth defect in the absence of both FCHO1 and FCHO2 . We did not pursue this in our study as it has been previously shown that a FCHO1/2 double knockout is indistinguishable from a FCHO2 knockout [37] . The formation of clustered and abnormal clathrin-coated vesicles is equivalent in both cases . Interaction with FCHO2 occurs through the Cig57 tyrosine-based endocytic sorting motif . To our knowledge , this is the first report of the ability of FCHO2 to recognise such motifs , as they are normally recognised by adaptor protein complexes such as AP-2 . Indeed , CvpA also contains endocytic sorting motifs and these mediate interaction with AP-2 [25] . Whether Cig57 is also able to simultaneously bind AP-2 will be an interesting line of further research . Complementation of cig57::Tn with cig57 encoding mutated endocytic sorting motifs leads to CCVs that phenocopy the small vacuole phenotype observed in FCHO2 KO cells ( Fig 7 ) . Hence , the inability of the Cig57ΔESM and Cig57Y365A to complement the cig57::Tn mutant is likely due to the inability of this modified effector to bind FCHO2 . Importantly , these strains were not attenuated to the same levels as the cig57::Tn mutant which may indicate that Cig57 possesses other activity and perhaps the interaction between Cig57 and FCHO2 acts to facilitate Cig57 activity on other components of the clathrin machinery . Interestingly , our results show that the Y365A mutation of Cig57 is not as attenuating as the ΔESM combined mutations , particular in relation to the expansion of the CCV . This may suggest a further role for the dileucine endocytic sorting motifs in full Cig57 function . Clathrin-coats are formed at the plasma membrane , at the trans-Golgi network or on endosomes , and associate with adaptor protein complexes for selection of protein or lipid cargo and specificity of the final destination . Vesicle budding and receptor sorting are facilitated by the formation of a clathrin coat at a membrane . During infection with Coxiella the final destination of some of the clathrin-coated vesicles appears to be the CCV , as evidenced by the accumulation of clathrin on the CCV ( Fig 5 and [25] ) . This would offer advantages to Coxiella to sequester extra membrane from the clathrin-coated vesicles , and to enable the delivery of nutrients in the form of cargo proteins and lipids from the vesicles to the CCV . The recruitment of clathrin-coated vesicles is dependent on the interaction between Cig57 and FCHO2 . Coxiella lacking Cig57 cannot recruit clathrin to the CCV and similarly , clathrin recruitment is diminished in the absence of FCHO2 . Overall this leads to biogenesis of a CCV that has reduced ability to expand and support Coxiella replication . Despite the requirement for FCHO2 to promote the recruitment of clathrin on CCV membranes , we did not observe recruitment or enrichment of FCHO2 on the CCV . This does not rule out the possibility that this host protein is dynamically cycling on and off the CCV membrane . Importantly , FCHO2 is also not observed on mature clathrin-coated vesicles as it acts early to in the initiation of endocytosis . Thus , the Cig57-FCHO2 interaction may occur at the plasma membrane or other compartments in the cell where FCHO2 has been observed , likely due to the affinity of FCHO2 to particular phospholipids [32] . We therefore cannot discount that Cig57 is taking advantage of an as yet undiscovered role that FCHO2 has in the host . Further investigation of the biochemical function of Cig57 and investigating the functional outcome of the Cig57-FCHO2 interaction will be an exciting area of future study . This line of research will likely reveal the complex mechanisms employed by Coxiella to establish a unique intracellular niche to support replication and virulence within the human host .
Bacterial strains and plasmids used in this study are listed in S1 File . Coxiella burnetii Nine Mile Phase II ( NMII ) , strain RSA439 was cultured axenically in liquid ACCM-2 as previously described [42] . Kanamycin and/or chloramphenicol were added to ACCM-2 at 300μg/ml and 3μg/ml respectively when required . E . coli XL1-Blue or DH5α were cultured in Luria-Bertani medium . Yeast strains were grown at 30°C in YPD ( yeast extract/peptone/dextrose ) or YMM ( yeast minimal media ) supplemented with 2% glucose and amino acids including methionine ( 20 μg/ml ) , adenine ( 20 μg/ml ) , histidine ( 20 μg/ml ) , uracil ( 20 μg/ml ) tryptophan ( 20 μg/ml ) and leucine ( 30 μg/ml ) when necessary . HeLa human cervical epithelial cells ( CCL-2; ATCC , Manassas , VA ) , and human embryonic kidney ( HEK ) 293T cells ( a gift from Elizabeth Hartland’s laboratory , University of Melbourne ) were cultured in Dulbecco’s Modified Eagle’s Media ( DMEM ) GlutaMAX ( Gibco ) supplemented with 10% heat inactivated foetal bovine serum ( FBS ) at 37°C with 5% CO2 Plasmid DNA was isolated using the QIAprep spin miniprep kit ( Qiagen ) , and bacterial or HeLa gDNA was isolated using the Zymo gDNA extraction kit . Oligonucleotides to amplify gene products were obtained from Sigma , and are listed in S1 File . DNA modifying enzymes were obtained from NEB and used according to standard procedures . pSpCas9 ( BB ) -2A-Puro ( pX459 ) V2 . 0 was a gift from Feng Zhang ( Addgene plasmid #62988 ) . Guide RNA specific for regions within FCHO2 ( designed using http://crispr . mit . edu/ , see S1 File ) were cloned into pX459 as previously described [43] . HeLa CCL2 cells were seeded at 2 . 5 × 105 in 6-well dishes and the following day co-transfected with a total of 2500 ng DNA specific to two exons using Lipofectamine 3000 according to the manufacturer’s protocol . Cells were selected with puromycin ( 5 μg/ml ) and clonally selected in 96-well plates . Modification of FCHO2 resulting in no protein production was confirmed by Western Blot analysis as below . FUGW was a gift from David Baltimore ( Addgene plasmid #14883 ) . HEK 293T cells were seeded into a 10cm dish and transfected with 7 . 5 μg pFUGW-GFP-FCHO2 , 3 . 5 μg pPAX and 2 . 5 μg pVSV-G for 48 hours with Lipofectamine 3000 . Lentiviral particles were collected , filtered through 0 . 45 μm and used to infect HeLa cells in 6-well plates for 48 hours as previously described [44] . For screening , Cig57 from Coxiella was used as bait . The Matchmaker pre-transformed HeLa cDNA library ( Clontech ) was mated with S . cerevisiae carrying pGBKT7-cig57 according to manufacturer’s protocols ( Clontech PT3183-1 manual ) . The Y2H Gold or AH109 strains were co-transformed with the relevant pGBKT7 or pGADT7 plasmids using the lithium acetate method and plated on DDO ( -Trp , -Leu ) or QDO ( -Trp , -Leu , -His , -Ade ) YMM plates . Samples were suspended in 4x NuPAGE LDS sample buffer ( Life Technologies ) containing 50μM DTT . Proteins were separated with NuPAGE 4–12% Bis-Tris gels ( Life Technologies ) and transferred to PVDF membranes using the iBLOT-2 ( Life Technologies ) . Membranes were blocked in 5% skim milk in Tris buffered saline containing 0 . 1% Tween 20 ( TBST ) and antibodies were diluted in 5% BSA or skim milk in TBST . Cells were fixed with 4% paraformaldehyde for 20 minutes at room temperature and permeabilized with 0 . 05% saponin and 2% BSA in PBS ( blocking solution ) . Primary and secondary antibodies were diluted in blocking solution and DAPI was diluted in PBS before coverslips were mounted using Prolong Gold Antifade ( Invitrogen ) . Images were acquired with a Zeiss LSM700 or LSM710 confocal laser scanning microscope and processed using Fiji [45] . To measure clathrin intensity , five measurements of intensity were taken at the CCV or in the cytoplasm and averaged . For immunofluorescence microscopy ( IF ) and western blotting ( WB ) the following antibodies were used at the designated dilutions: α-clathrin heavy chain ( Abcam , WB = 1:2000 , IF = 1:1000 ) , α-FCHO2 ( ThermoFisher , WB = 1:1000 ) , α-β-actin ( Perkin Elmer , WB = 1:5000 ) , polyclonal α-Coxiella ( IF = 1:10000 ) , α-LAMP1 ( DHSB , IF = 1:250 ) , HRP-conjugated goat α-mouse ( BioRad , WB = 1:3000 ) , HRP-conjugated goat α-rabbit ( Perkin Elmer , WB = 1:3000 ) , AlexaFluor 488 ( ThermoFisher , IF = 1:2000 ) , AlexaFluor 568 ( ThermoFisher , IF = 1:2000 ) . For clathrin localization studies , HeLa cells were seeded at 2 . 5 × 104 per well in 24 well plates with coverslips and the following day infected at an MOI of 100 . Cells were incubated for 72 hours and processed as described above . In 24-well plates , HeLa cells were seeded at 5×104 per well and infected with the relevant Coxiella strains the following day at an MOI of 50 . To calculate the MOI , Coxiella was quantified using the Quanti-iT PicoGreen dsDNA Assay kit ( Life Technologies ) [46] . Cells were incubated for 4 hours at 37°C before being washed once with PBS and the media replaced with DMEM containing 5% FBS . At this time ( day 0 ) , as well as 1 , 3 and 5 days post infection , cells were lysed in dH2O and pelleted by centrifugation . Genomic DNA was extracted from the samples and genome equivalents ( GE ) were quantified by qPCR specific for the Coxiella ompA gene [47] . Samples were collected for immunofluorescence analysis at day 3 as described above . HeLa cells were transfected with small-interfering RNA ( siRNA ) using siGenome SMARTpools ( Dharmacon , GE Life Sciences ) against human CLTC ( M-004001-00 ) and FCHO2 ( M-024508-01 ) or with ON-TARGETplus ( OTP ) Non-targeting pool ( D-001810-10-05 ) using Dharmafect-1 ( Dharmacon , GE Life Sciences ) . Cells were seeded at a density of 2 . 5×105 in 6-well plates with a final concentration of 50 nM siRNA . After a two-day incubation , cells were replated at a density of 1 . 0×104 in 24-well plates , and simultaneously infected with Coxiella NMII at an MOI of 10 by centrifugation at 500 × g for 30 minutes . Cells were washed once with PBS and media replaced with DMEM containing 10% FBS . This timepoint was designated day 0 . Cells were processed for western blot analysis , genome quantification and immunofluorescence as described above at days 2 , 4 and 6 post infection . Persistently infected HEK 293T cells in 10 cm dishes were transfected for 48 hours with GFP constructs using FuGENE6 before lysing in lysis buffer ( 10mM Tris ( pH 7 . 5 ) , 150 mM NaCl , 0 . 5 mM EDTA , 0 . 5% NP-40 ) for 30 minutes on ice . Lysates were clarified by centrifugation at 17000 x g and 10% collected for input analysis . The remaining lysate was added to 20 μl of GFP beads ( ChromoTek ) and incubated with mixing at 4°C for four hours . Beads were washed extensively with wash buffer ( 10mM Tris ( pH 7 . 5 ) , 150 mM NaCl , 0 . 5 mM EDTA , 0 . 01% NP-40 ) before being resuspended in LDS sample buffer and boiled for 10 minutes . Statistical analyses were performed with Prism ( GraphPad Software , Inc . ) by use of the unpaired Student’s t test . P values less than 0 . 05 were considered to be significant .
|
Human Q fever is caused by the intracellular bacterium Coxiella burnetii . Successful infection of human cells relies on a Dot/Icm secretion system and the translocation of effector proteins into the host cell cytosol . The functions of many Coxiella effector proteins , and their contribution to bacterial growth and host manipulation , remain unknown . We show that a unique effector , Cig57 , has an important role in manipulation of host cellular clathrin-mediated trafficking . In particular , Cig57 binds FCHO2 , a protein involved in formation of clathrin-coated vesicles , in a manner that is dependent on a tyrosine-based endocytic sorting motif . Through engaging proteins in the clathrin pathway , Cig57 facilitates expansion of the Coxiella replicative vacuole and enables the pathogen to replicate to large numbers . Thus , we identify a relationship between a host process and a key virulence protein that are required for pathogen success .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"vesicles",
"vacuoles",
"pathology",
"and",
"laboratory",
"medicine",
"intracellular",
"pathogens",
"hela",
"cells",
"gene",
"regulation",
"pathogens",
"biological",
"cultures",
"cell",
"processes",
"membrane",
"proteins",
"cell",
"cultures",
"cellular",
"structures",
"and",
"organelles",
"research",
"and",
"analysis",
"methods",
"small",
"interfering",
"rnas",
"gene",
"expression",
"cell",
"lines",
"cell",
"membranes",
"endocytosis",
"biochemistry",
"rna",
"cell",
"biology",
"nucleic",
"acids",
"secretory",
"pathway",
"genetics",
"biology",
"and",
"life",
"sciences",
"cultured",
"tumor",
"cells",
"non-coding",
"rna"
] |
2016
|
The Effector Cig57 Hijacks FCHO-Mediated Vesicular Trafficking to Facilitate Intracellular Replication of Coxiella burnetii
|
Specialized proteins serve as scaffolds sculpting strongly curved membranes of intracellular organelles . Effective membrane shaping requires segregation of these proteins into domains and is , therefore , critically dependent on the protein-protein interaction . Interactions mediated by membrane elastic deformations have been extensively analyzed within approximations of large inter-protein distances , small extents of the protein-mediated membrane bending and small deviations of the protein shapes from isotropic spherical segments . At the same time , important classes of the realistic membrane-shaping proteins have strongly elongated shapes with large and highly anisotropic curvature . Here we investigated , computationally , the membrane mediated interaction between proteins or protein oligomers representing membrane scaffolds with strongly anisotropic curvature , and addressed , quantitatively , a specific case of the scaffold geometrical parameters characterizing BAR domains , which are crucial for membrane shaping in endocytosis . In addition to the previously analyzed contributions to the interaction , we considered a repulsive force stemming from the entropy of the scaffold orientation . We computed this interaction to be of the same order of magnitude as the well-known attractive force related to the entropy of membrane undulations . We demonstrated the scaffold shape anisotropy to cause a mutual aligning of the scaffolds and to generate a strong attractive interaction bringing the scaffolds close to each other to equilibrium distances much smaller than the scaffold size . We computed the energy of interaction between scaffolds of a realistic geometry to constitute tens of kBT , which guarantees a robust segregation of the scaffolds into domains .
Interactions between membrane shaping proteins play a critical role in sculpting intracellular organelles [1] . Membranes of such cellular compartments in as endoplasmic reticulum ( ER ) , Golgi Complex , mitochondria and transport intermediates possess peculiar shapes containing strongly curved regions with radii of few tens of nanometers [2] . Also upon pathologic conditions of , e . g . , viral infection intracellular membrane can develop strongly curved abnormal structures similar to lyotropic cubic phases [3 , 4] . While membrane curvature can be a consequence of a difference in lipid composition of the two membrane monolayers [5 , 6] or membrane pulling by molecular motors [7 , 8] , it is commonly agreed that in most cases large curvatures characterizing the intracellular membranes are generated and stabilized by specialized peripheral membrane proteins ( see for recent reviews [9–11] ) . It has been further suggested that , in addition to the curvature per se , proteins can generate also anisotropy of membrane shapes and elastic properties [12 , 13] . The two mechanisms suggested for generation of large membrane curvatures by individual membrane proteins or protein oligomers are the hydrophobic insertion ( wedging ) and scaffolding mechanisms ( see for recent review [11] ) . The essence of the hydrophobic insertion mechanism is a shallow embedding into the external membrane monolayer of small hydrophobic or amphipathic domains of such proteins as epsins [14] , N-BAR domain proteins [15 , 16] and ALPS motifs [17] . One such insertion serves as an effective wedge splaying the monolayer polar heads , which generates a local perturbation of the membrane structure [11] . Perturbations by numerous insertions concentrated within one spot in the membrane plane sum up into an overall membrane curvature [11 , 16] . The scaffolding mechanism is based on attachment to the outer membrane surface of a hydrophilic protein domain or a domain oligomer , referred to as a protein scaffold , which molds the membrane , locally , into a curved shape [7 , 8 , 18] . This mechanism requires the membrane binding face of the scaffold to be intrinsically curved , the protein-membrane interaction of , usually , electrostatic origin , to be sufficiently strong , and the scaffold to be more rigid that the lipid membrane matrix . Similarly to a hydrophobic insertion , a protein scaffold provides a local membrane deformation so that generation of membrane curvature along an extended membrane area requires an elevated surface density of such scaffolds . The protein segregation on the membrane surface into regions of high protein densities must be driven by attractive forces between the individual protein or their oligomers . Whereas direct electrostatic , Van-der Waals , and liquid crystal-like interactions may contribute to such forces [1] , an essential protein-protein interaction can be mediated by the membrane itself ( see for recent review [19] ) . Theoretical and numerical analyses of such membrane-mediated interactions are necessary for determination of conditions where these interactions become relevant for the in-plane protein assembly , i . e . , are attractive and strong enough to overcome the entropic spreading of the proteins over the membrane surface . The models suggested in the literature for analysis of the membrane-mediated interactions between proteins are of a phenomenological or microscopic character [20–23] . The phenomenological model considers a protein either as a point-like perturbation of the membrane elastic properties [23] or as a membrane patch whose bending modulus , κp and the modulus of Gaussian curvature , κ¯p , [24] , only slightly deviate from those of the surrounding lipid bilayer , κ and κ¯ , so that |κp−κ|κ≪1 , |κ¯p−κ¯||κ¯|≪1 . The energies of the membrane conformations , which determine the overall membrane free energy and the related protein-protein interaction , are computed in this case by integration of Helfrich Hamiltonian of membrane bending [24] over the total membrane area including the protein patches , and the results are presented as series in |κp−κ|κ and |κ¯p−κ¯||κ¯| [21 , 22] . The microscopic models assume the proteins to be infinitely rigid , as compared to the membrane either in terms of their bending rigidity , κpκ≫1 , or including also the rigidity with respect to Gaussian curvature |κ¯p||κ¯|≫1 [25] . In this case , the protein patches do not accumulate any bending energy but rather impose along their perimeters certain boundary conditions on the membrane shape [21 , 22] . Two types of such boundary conditions have been introduced . The first type sets a particular contact angle , ϕ , between the membrane and the protein patch [21 , 22 , 26–29] , which is supposed to be determined by an effective shape of the protein-membrane interface . The contact angle has been assumed to have either a fixed [21 , 26–29] or an energetically preferred [22] value at every point of the protein-membrane boundary . The boundary condition of the second type assumes that the protein generates , locally , a certain mean curvature of the membrane surface [19 , 30] ( see for review [31] ) . Fulfillment of the latter condition would require the protein to produce a constant bending moment acting on the membrane along the protein circumference . Whereas existence of a molecular mechanism for such bending moment generation seems uncertain , the qualitative features of the membrane mediated protein-protein interactions appear to be insensitive to the particular type of the boundary conditions [30 , 31] . The model analysis revealed two different physical origins for the membrane-mediated interactions between proteins . The first is of entropic nature and is related to modification by the proteins of the thermal membrane undulations [21 , 22 , 31–35] . The second , referred to as the elastic interaction , is a consequence of the membrane bending deformation generated by the proteins [19–22 , 26–31 , 34 , 36–40] . In both cases the interaction character depends on the shape of the effective membrane inclusions representing the protein . For axially symmetric inclusions having shapes of spherical segments or truncated symmetric cones , the entropic interaction is purely attractive , while the elastic interaction , in the case of vanishing lateral tension , is pure repulsive , both decaying as 1 / d4 for the inter-inclusion distances , d , greatly exceeding the inclusion size , a ( see for review [38] ) . Any kind of anisotropy in the inclusion shape changes qualitatively the character of the inter-inclusion interaction making it dependent on the inclusion orientation in the membrane plane . The considered types of such anisotropy are an elliptical rather than circular base shape of an inverted cone [22 , 28] with possible variations of the contact angle and of the level of the contact line with respect to the cone base [27] , and a saddle-like rather spherical local curvature imprint generated by the inclusion [19 , 30] . Specifically , for certain inclusion orientations , the elastic interaction has been predicted to acquire an attractive component , which decay with the distance as 1 / d2 for d ⪢ a [19 , 22 , 27 , 28 , 30 , 33] . While the previous extensive analysis revealed the major qualitative features of the membrane-mediated interactions between membrane inclusions , it was mainly performed ( but [37] ) for large inter-inclusion distances , d ⪢ a , low degrees of the inclusion anisotropy and in the approximation of small local perturbations by the inclusions of the initially flat membrane shape . In reality , these conditions are often violated . The crescent-like shapes of such typical curvature-generating protein scaffolds as BAR-domain dimers are strongly elongated having aspect ratio around five [15] and so strongly curved [7 , 41–44] that the contact angles at the effective membrane-protein boundary are large , hence , generating strong local perturbations of the membrane shape . Strongly elongated as well as strongly and anisotropically curved shapes characterize also oligomers of dynamin ( see for review [45–48] ) and EHD2 [49] . The inter-protein distances are hardly accessible for direct measurement within cells . However in in vitro systems [42] and , according to estimations , within strongly curved membrane domains such as ER tubules [9 , 50 , 51] , these distances are comparable to the protein size , d~a . Thus , understanding of the biologically relevant protein-protein interactions requires expansion by numerical methods of the previously obtained results to the experimentally relevant parameter ranges . The goal of the present study is to analyze quantitatively the membrane-mediated interaction between two strongly anisotropic and intrinsically curved membrane scaffolds , which mimic realistic proteins and protein oligomers shaping membrane by the scaffolding mechanism . Following the information on the protein structures provided by crystallographic studies [52] , we model a protein scaffold as a surface patch with infinite rigidity and an anisotropic , strongly and inhomogeneously curved shape . We first compute the elastic interaction between scaffolds whose footprint on the membrane plane is circular but the two principal curvatures are different . We calculate the orientational component of this interaction driving the scaffold mutual ordering in the membrane plane , and the repulsive-attractive component determining the preferred inter-scaffold distance . We demonstrate that the attractive interaction dominates at large distance between the scaffolds , while a repulsive interaction takes over at small distances . As a result , there is always an energy well corresponding to an equilibrium inter-scaffold distance , which is determined by the specific type and extent of the scaffold shape anisotropy . In a particular case of scaffolds with one vanishing principal curvature , the elastic interaction remains attractive up to vanishing distances . In addition to the purely elastic forces , we estimate , computationally , a previously unaddressed contribution of the entropy of fluctuations of the scaffold mutual orientation . We obtain that this contribution is always repulsive and is of the same order of magnitude as the entropic interaction related to the membrane undulations . Finally , we perform computations for strongly elongated and strongly and anisotropically curved protein scaffolds whose geometry is characterized by the specific parameters assessed experimentally for BAR proteins [15 , 41] . We find that these proteins prefer a parallel mutual orientation , and that the energy “well” corresponding to an inter-scaffold equilibrium distance much smaller than the protein width of several nanometers reaches few tens of kBT ( where kBT = 410–21 is the product of Boltzmann constant and the absolute temperature ) . This means that the elastic interaction is sufficiently strong to drive the protein segregation and mutual ordering .
We consider two infinitely rigid protein scaffolds having an anisotropic shape and embedded into a flexible lipid membrane . Our goal is to compute the energy and the related force of interaction between the scaffolds determined by the membrane elasticity and the free energy contribution from the entropy of fluctuations of the scaffold mutual orientation . We model a scaffold as a segment of ellipsoid ( or hyperboloid ) ( Fig . 1 A ) with a given area A , different principal curvatures , ca and cb , in the central point and an elliptical shape of projection on the initial membrane plane characterized by semi-axes ra and rb ( Fig . 1B ) . Specifically , the scaffold shape is obtained by cutting a surface determined in Cartesian coordinates by ( xρa ) 2+sign ( ρb ) ( yρb ) 2+ ( zρa ) 2=1 , ( 1 ) by a cylindrical surface whose axis of symmetry coincides with the axis z and the cross-section in the x-y plane is an ellipse determined by ( xra ) 2+ ( yrb ) 2=1 . ( 2 ) The parameters ρa , ρb , ra and rb determine the scaffold area A and the principle curvatures ca = 1/ ρa , cb = 1/ ρb in the central point of the scaffold . Positive values of ρa > and ρb > 0 correspond to ellipsoidal shapes of the scaffolds with two positive principal curvatures , whereas a positive ρa > 0 but negative ρb < 0 describe a hyperboloidal saddle-like shapes , whose principal curvatures have opposite signs ( Fig . 1A ) . To simulate a cell outer membrane , we model the initial membrane shape preceding its perturbation by the scaffolds as a closed sphere with radius R . This membrane radius is taken to exceed by four orders of magnitude the scaffold dimension , a=A , and the inter-scaffold distances , d , meaning that , locally , in the vicinity of the scaffolds , the initial membrane shape is , practically , flat . In the course of deformations , the membrane is assumed to remain closed and keep its area , 4πR2 , but to match the enclosed volume to the energy minimization requirements . As a boundary condition for the membrane shape imposed by a scaffold we require continuity between the membrane and scaffold surfaces , which means that at every point along the scaffold perimeter the normal vector of the membrane surface , n→e , coincides with that of the scaffold surface ( Fig . 1B ) . This boundary condition can be also expressed through the angle , ϕ , between the normal vector , n→e , and that at the scaffold center , n→c , which will be referred to as the contact angle ( Fig . 1B ) . The contact angle changes along the scaffold perimeter in the range between ϕa = arcsin ( ra / ρa ) and ϕb = arcsin ( rb / ρb ) . Note that the curvature continuity is not required since the border of the scaffold can apply bending moments to the membrane . Because of the infinite rigidity , the scaffolds do not accumulate any elastic energy . The elastic energy of the membrane per unit area of the membrane surface is described by Helfrich model [24] f=12κ J2 , ( 3 ) where κ is the membrane bending modulus [24] , whose value will be taken as k = 20 kB T , and J is the total curvature of the membrane surface ( which equals twice the mean curvature ) [53] . The ( Eq . 3 ) implies that the membrane spontaneous curvature vanishes; the energy of Gaussian curvature is constant according to Gauss-Bonnet theorem since the direction of the surface normal is fixed at the scaffold boundaries and the membrane surface remains closed; and there is no lateral tension in the membrane . Below we show that the latter assumption is not crucial and the following computation results account also for the cases of non-vanishing lateral tension , γ , provided that the related characteristic length of propagation of membrane deformations , κ/γ , greatly exceeds the scaffold size and the inter-scaffold distance . The total elastic energy is obtained by integration of Eq . 3 over the area A of the closed membrane exept for the two segments occupied by the scaffolds , We determine the relative positions and orientations of the two scaffolds on the membrane by the distance d between the scaffold centers , the angles φ1 and φ2 between the scaffold long axes and the line connecting the scaffold centers , and the angles θ1 and θ2 of tilting of the normal vectors at the scaffold centers , n→c1 and n→c2 , with respect to the normal vector of the initial membrane plane ( Fig . 1B ) . For simplicity , we consider only symmetric orientations of the scaffolds φ1 = φ2 ≡ φ and θ1 = θ2 ≡ θ ( Fig . 1 ) . Below we support this assumption by computing the membrane energy for several characteristic asymmetric orientations . Because of the boundary conditions at the scaffold perimeters , at each specific distance , d , and orientation angles , φ and θ , the membrane undergoes bending deformation , and acquires an elastic energy , Fm ( φ , θ , d ) . We define as the elastic energy of the membrane-mediated scaffold interaction , Fel ( d ) , the membrane elastic energy , Fm ( φ , θ , d ) , which is minimized for every inter-scaffold distance , d , with respect to the angles φ and θ . The angle values φ* ( d ) and θ * ( d ) corresponding to the minimal energy characterize the scaffold mutual alignment . Further , taking the angles φ and θ as variables determining the thermodynamic states of the system for any inter-scaffold distance , d , we define the free energy of the membrane-mediated interaction between the scaffolds as F ( d ) =−kBTlog[∫exp ( −Fm ( φ , θ , d ) kBT ) dθdφ] ( 5 ) where the integration has to be performed over the whole range of the angle variations 0 < φ < 90° , 0 < θ < 180° . Finally , taking the elastic energy , Fel ( d ) , as playing the role of the internal energy of the system , we define the entropic energy of the membrane-mediated scaffold interaction , Fent ( d ) , as Fent ( d ) =F ( d ) −Fel ( d ) . ( 6 ) Note that the entropic energy we consider ( Eq . 6 ) does not include the contribution of the membrane undulations analyzed in the previous works [20 , 21 , 31 , 33 , 38] and is related solely to the fluctuations of the scaffold orientation . Furthermore , as discussed below , computations within the limitation of the symmetric mutual orientations of the scaffolds give a low limit of the related entropic interaction . The interaction forces acting between the scaffolds and corresponding to the free , elastic and entropic energies are the derivatives of F ( d ) , Fel ( d ) and Fent ( d ) with respect to the inter-scaffold distance , d . Way of computation . For computations we use K . Brakke’s Surface Evolver [54] . For every given set of the inter-scaffold distance , d , and the orientation angles , φ and θ , we obtain the membrane bending energy , Fm ( φ , θ , d ) , by determining , computationally , the membrane shape satisfying the boundary conditions at the scaffold perimeters and minimizing the membrane bending energy . In the Supplemental Information we discuss in detail the steps of calculations illustrated by video V1 and the computational error estimation . We determine the elastic energy of the scaffold interaction , Fel ( d ) , by finding for every given distance d the angle values , φ* ( d ) and θ * ( d ) , which minimize the energy Fm ( φ , θ , d ) , and calculate the corresponding minimal energy value Fmin ( d ) . Since we are interested in the energy of the scaffold interaction , the energy Fel ( d ) is computed as Fel ( d ) = Fmin ( d ) —Fmin ( ∞ ) , where Fmin ( ∞ ) corresponds to large scaffold separations . We next compute the free energy of the scaffold interaction , F ( d ) . According to ( Eq . 5 ) , this requires computation of the bending energy , Fm ( φ , θ , d ) , for all possible values of the angles φ and θ . We vary the orientation angle φ in the whole range 0 < φ < 90° . For technical reasons , we change the tilt angle θ within a range limited by deviation of the energy Fm from its minimal value , Fel ( d ) , by not more than 4kBT , which must give a sufficient accuracy of integration in ( Eq . 5 ) . The results are then interpolated to give the energy Fm ( φ , θ , d ) over the entire range of θ . The resulting function is used for numerical integration according to ( Eq . 5 ) . Finally , we determine the entropic contribution to the scaffold interaction , Fent ( d ) , substituting the computed energies into ( Eq . 6 ) .
We start by analyzing scaffolds of circular shape , ra = rb = a , but different principal curvatures , ca = 1/ ρa , and cb = 1/ ρb ( Fig . 1B ) . We take the scaffold radius to be small compared to the principal curvature radii , a/ ρa ≪ 1 , a/ | ρb |≪ 1 so that the contact angle ϕ is small , sin ϕ ≪ 1 , which means that the scaffolds are shallow . For computations we take a specific ratio a/ ρa = 0 . 2 , fix ca and vary cb within a range , ca ≥ cb ≥ −ca which preserves the scaffold shallowness but represents different degrees of the scaffold curvature anisotropy . The limiting cases of cb = ca and cb = −ca correspond to spherically symmetric and saddle-like scaffolds , respectively , which have been analyzed analytically [19–21 , 26 , 30] . Our computations show that for any given inter-scaffold distance , d , the inter-scaffold interaction energy depends on the mutual scaffold orientation . For convex scaffolds with ca > 0 and cb > 0 ( or ca > 0 and cb > 0 ) , the minimal energy corresponds to such scaffold orientation where they front each other by their faces of the lowest contact angle ϕ , ( Fig . 2 ) . Saddle-like scaffolds characterized by one negative curvature , ca > 0 , cb < 0 , follow the same orientation rule in the major distance range , but exhibit a more complex mutual orientation at very small inter-scaffold distances . The elastic energy of the circular scaffold interaction , Fel ( d ) , corresponding to the optimal scaffold orientation , is presented in ( Fig . 3 ) for various values of the second principal curvature cb . For the spherically symmetric scaffolds , cb = ca we obtain a monotonic repulsion over the whole distance range , which recovers the predictions of the analytical calculation for large distances ( see Supplemental Information ) [20 , 21 , 26] . Any anisotropy of the scaffold curvature results in attractive interaction at large distances , whereas at small distances the interaction remains repulsive . As a result , for anisotropic scaffolds , the energy profile as a function of d is non-monotonic with a minimum corresponding to an equilibrium value , d* . This equilibrium distance d* rapidly decreases with the growing scaffold curvature anisotropy reaching values close to the particle size , d* = 2 . 36a , already for a relatively modest anisotropy of cb = 0 . 75 ca . For smaller absolute values of cb , the energy minimum distance becomes smaller than the limiting accuracy of our computations . However , according to our estimations , the energy minimum disappears and the scaffold interaction is purely attractive up to zero distances for vanishing second curvature , cb = 0 , i . e . for scaffolds shaped as cylindrical elements , which are curved only along one principal axis . For negative values of cb < 0 , i . e . for scaffolds with saddle-like shapes , the repulsion at small distances and the related energy minimum at finites inter-scaffold distances reappear , as illustrated by the curves of ( Fig . 2 ) corresponding to cb = −0 . 75 ca and cb = − ca . The free energy of the scaffold interaction , F ( d ) , for different degrees of the scaffold curvature anisotropy is presented in Fig . 4A . For all values of cb , the entropic effects included in F ( d ) turn the interaction at very short and at large distances to be repulsive ( Fig . 4A ) . As a result , the overall inter-scaffold interaction is purely repulsive for low degrees of the scaffold anisotropy ( Fig . 4A , cb ≥ 0 . 75 ca ) and characterized by a minimum corresponding to an equilibrium distance for stronger anisotropic scaffolds ( Fig . 4A ) . The depth of the energy well increases with the scaffold anisotropy and reaches a values of ~1kB T for saddle-like scaffolds ( ca = −cb ) ( Fig . 4A , purple curve ) . The entropic contribution to the interaction energy , Fent ( d ) , is presented in Fig . 4B . This interaction is purely repulsive and the energy has values of up to 2kBT . It is instructive to compare Fent ( d ) with the energy of the attractive interaction mediated by the membrane thermal undulations [20 , 21 , 31 , 32] . According to this comparison , the entropic effects related to the fluctuations of the scaffold orientations considered here can overcome those originating from the membrane undulations . Importantly , the presented computational results can only serve as a lower limit to the total entropic repulsion since for the purpose of simplicity we assumed the scaffold orientations to be mirror symmetrical . The full description of the free energy has to include also mirror asymmetric orientations , which , although energetically unfavorable , contribute to the entropic repulsion . Next , we analyze the scaffold interaction for the cases where the scaffold shape anisotropy is related to an elongated elliptical shape of the scaffold projection on the initial membrane plane , ra ≠ rb , rather than the difference in the scaffold principal curvatures . To this end , we compute the elastic energy of interaction , Fel ( d ) , between two scaffolds characterized by a fixed value of r=rarb , symmetric principle curvatures , ca = cb = 0 . 2∕ r ca , and different values of the aspect ratio ra ∕ rb . Concerning the scaffold mutual orientation , we obtain , similarly to the above case of anisotropic curvatures , that , for every distance d , the minimal energy corresponds to the scaffolds fronting each other by their faces of the lowest contact angle ϕ . This orientation corresponds to alignment of the scaffolds shortest axes . Thus , the mutual alignment of two convex scaffolds with the faces of the lowest contact angle fronting each other is a general prediction independent of whether the scaffold anisotropy is related to the deviation from the circular projection shape or inequality of the scaffold principle curvatures . The elastic interaction energy Fel ( d ) , corresponding to the optimal scaffold alignment and different degrees of the shape elongation is presented in Fig . 5 . For symmetric scaffolds , rb = ra , the interaction has been confirmed to be purely repulsive for all distances d ( Fig . 5 , Purple ) . The interaction between anisotropic scaffolds is attractive at large and repulsive and small inter-scaffold distances ( Fig . 5 ) . The equilibrium inter-scaffold distance corresponding to the energy minimum decreases and the depth of the energy well increases with growing anisotropy of the scaffold shape ( Fig . 5 ) . The above computations for two shallow scaffolds revealed that the scaffold shape anisotropy of any kind results in an attractive component of the inter-scaffold interaction . This determines the existence of short equilibrium distances between and the corresponding optimal mutual orientation of the scaffolds . However , the depth of the energy well corresponding to the equilibrium distance has been predicted to be of the order of 1kBT , which is generally insufficient for overcoming the effects of the entropy of distribution all over the membrane and , hence , for holding the scaffolds close to each other . To test the efficiency of the membrane mediated interactions between realistic scaffolds we performed computations for the scaffold parameters characterizing endophilin N-BAR domains . We took the dimensions of the scaffold projection on the membrane plane to be ra = 6 . 5 nm , rb = 1 . 5 nm and the curvature radius of the long axis to be ρa = 8 . 5 nm [7 , 15 , 41] , and computed the elastic energy of the two scaffold interaction for various ρb . The computations show that , for the explored ρb range , such scaffolds mutually orient by aligning their short axes , which corresponds to fronting each other by their faces of lowest contact angle . Fig . 6 presents the elastic interaction energy at the optimal scaffold orientation as a function of the inter-scaffold distance , Fel ( d ) , for various values of cb = 1 ∕ ρb . For cb < 0 . 2 nm-1 , the elastic attraction between such highly elongated scaffolds is very strong compared to the above case of shallow scaffolds . As a result , the depth of the energy well corresponding to the equilibrium inter-scaffold distances , which are much smaller than the scaffold length , can reach few tens of kBT . The entropy contribution to the free energy is about 1kBT and can , therefore , be neglected in this case . Hence , the membrane-mediated interaction of realistically elongated and curved scaffolds is predicted to be sufficiently strong for guarantying an effective mutual ordering of the scaffolds and stably keeping them in a close proximity to each other . The computed membrane shapes in the vicinity of two such scaffolds characterized by specific values of cb are presented in Fig . 7 .
In our analysis , we addressed a contribution to the two-scaffold interaction related to the entropy of fluctuations of the scaffold orientation with respect to each other and of the scaffold tilting with respect to the initial membrane plane . This interaction , which remained unaddressed by the previous works , has a repulsive character . The physical origin of this interaction is in a mutual limitation by the approaching scaffolds of the number of available orientations and the related reduction of the system entropy . Here , we compute the entropy effects of only the symmetric mutual orientations of the scaffolds , meaning that the results underestimate the strength of the related interaction . This entropic repulsion counteracts the well-explored attractive interaction , which originates from the entropy of membrane undulations [20 , 21 , 31 , 33] . According to our results , the orientational entropic repulsion considered here can overcome the entropic attraction related to the membrane undulations . We performed the computations for two scaffolds embedded into a closed membrane whose size , R , exceeds by four orders of magnitude the scaffold linear dimensions , ra and rb , and the inter-scaffold distance , d . In spite of the scale difference , a question remains whether the obtained results are equivalent to the scaffold interaction within an initially absolutely flat membrane contacting a lipid reservoir with vanishing lateral tension . Indeed , in the absence of the lateral tension , the membrane bending deformations are long range and decay with the distance according to a power low [26] . Hence , basically , the shape of the whole membrane must undergo some extent of deformation [61] upon the scaffold embedding , which may result in a dependence of the scaffold interaction on the boundary conditions imposed on the membrane shape at large distances . The effects of membrane closure on the inclusion interaction for relatively small membrane dimensions were analyzed in [62] . To check this issue in our system , we computed the elastic energy of the inter-scaffold interaction in the presence of low lateral tension , γ , which results in an exponential decay of the membrane deformations [26] and , hence , must eliminate the dependence of the results on the membrane boundary conditions at large distances . The scaffold parameters were taken to be ra = 0 . 23 ρa; rb = 0 . 175ρa; ρb = ρa and the tension value was chosen such that the characteristic length of the exponential decay of deformation , ξ=κ/γ [26] , was much smaller than the membrane dimension ξ ≈ 10–2 R , but exceeds by two orders of magnitude the scaffold dimensions ra and rb , and the inter-scaffold distance d . We found that while the tension-mediated interaction between the scaffolds is always repulsive and depends on the strength of the membrane tension γ , the curvature contribution to the interaction remains equal to that we obtained at zero tension . Hence , closure of large membrane does not influence the curvature-mediated interaction between two scaffolds . According to another assumption , we considered only mirror symmetric mutual orientations of the scaffolds . This was verified by computation of the interaction energy for circular saddle-like scaffolds with ρb = −ρa adopting asymmetric orientations φ1 ≠ φ2 , θ1 ≠ θ 2 . We found the energies larger than for the symmetric orientations .
|
A striking feature of most of the crucial intra-cellular organelles , such as Endoplasmic Reticulum ( ER ) , Golgi Complex and mitochondria is a peculiar and strongly curved shape of their membranes . Membranes resist elastically to bending and remodeling . Hence , special proteins must generate forces needed to create and maintain the intracellular membrane curvature and architecture . An effective mechanism by which proteins bend membranes is the scaffolding mechanism based on attachment to the outer membrane surface of hydrophilic protein domains referred to as a protein scaffolds . The scaffolds are intrinsically bent and mold , therefore , the membranes into curved shapes . This action of the scaffolds requires their concentration on the membrane surface due to inter-scaffold attractive forces originating from membrane deformations . Here we analyze quantitatively the membrane-mediated interaction between two strongly anisotropic and intrinsically curved membrane scaffolds , which mimic realistic proteins . We demonstrate that the scaffold shape anisotropy causes a mutual aligning of the scaffolds and generates a strong attractive interaction bringing the scaffolds close to each other to equilibrium distances much smaller than the scaffold size . We compute the energy of interaction between scaffolds of a realistic geometry to constitute tens of kB T , which guarantees a robust segregation of the scaffolds into domains .
|
[
"Abstract",
"Introduction",
"Model",
"Results",
"Discussion"
] |
[] |
2015
|
Membrane-Mediated Interaction between Strongly Anisotropic Protein Scaffolds
|
Most eukaryotic pathogens have complex life cycles in which gene expression networks orchestrate the formation of cells specialized for dissemination or host colonization . In the oomycete Phytophthora infestans , the potato late blight pathogen , major shifts in mRNA profiles during developmental transitions were identified using microarrays . We used those data with search algorithms to discover about 100 motifs that are over-represented in promoters of genes up-regulated in hyphae , sporangia , sporangia undergoing zoosporogenesis , swimming zoospores , or germinated cysts forming appressoria ( infection structures ) . Most of the putative stage-specific transcription factor binding sites ( TFBSs ) thus identified had features typical of TFBSs such as position or orientation bias , palindromy , and conservation in related species . Each of six motifs tested in P . infestans transformants using the GUS reporter gene conferred the expected stage-specific expression pattern , and several were shown to bind nuclear proteins in gel-shift assays . Motifs linked to the appressoria-forming stage , including a functionally validated TFBS , were over-represented in promoters of genes encoding effectors and other pathogenesis-related proteins . To understand how promoter and genome architecture influence expression , we also mapped transcription patterns to the P . infestans genome assembly . Adjacent genes were not typically induced in the same stage , including genes transcribed in opposite directions from small intergenic regions , but co-regulated gene pairs occurred more than expected by random chance . These data help illuminate the processes regulating development and pathogenesis , and will enable future attempts to purify the cognate transcription factors .
Eukaryotic pathogens typically employ specialized structures for dissemination and infection . Most filamentous fungi and oomycetes , for example , proliferate in their hosts as vegetative hyphae , which generate spores that are used to reach new infection sites [1] . The spores of many plant pathogens , especially those with a biotrophic disease stage , germinate to form structures known as appressoria that are used to breach the host epidermis . Transitions between these stages requires the precise control of transcription , which is accomplished through interactions between transcription factors and their binding sites ( TFBSs ) in DNA [2] . Some transcription factors and their cognate TFBSs have been identified in filamentous fungal and oomycete pathogens [3]–[6] , but relatively little is known about the structure or regulation of their promoters compared to those of model saprophytes and animals . Studies in Saccharomyces cerevisiae have shown that its promoters typically contain only a small number of regulatory sequences located a few hundred bases upstream of the transcription start site [7] . This contrasts with metazoans , where genes are also controlled by more distant motifs , which often bind many transcription factors and exert long-range effects across chromatin domains [8] . Identifying TFBSs in the promoters of pathogen genes is an important step towards characterizing the networks that regulate growth , differentiation , and pathogenesis . The classic strategy for identifying regulatory motifs by promoter mutagenesis is laborious and not suited to genome-wide application , especially in non-model systems which include most plant and animal pathogens . In recent years , bioinformatic analyses enabled by genome sequencing and expression profiling have helped accelerate the discovery of promoter motifs in model organisms . Typically , co-regulated promoters are searched for over-represented motifs using methods that include enumerative search , expectation maximization , or Gibbs Sampling algorithms [9]–[12] . Comparative genomics also offers methods for predicting motifs by searching cross-species promoter alignments for phylogenetic footprints , i . e . regions of conservation [13] . The over-representation and evolutionary approaches have both been used with some success , since many of the resulting motifs resemble those identified by traditional methods [10] , [13]–[17] . Relatively little is known about the organization and function of promoters in oomycetes , a group of eukaryotes that includes important pathogens of plants and animals . Studies of the potato late blight pathogen Phytophthora infestans and relatives revealed a novel genome structure comprised of gene-dense and gene-sparse regions [18] . P . infestans grows by extending tubular hyphae which then form sporangia , each of which can release multiple biflagellated zoospores [19] . In response to external cues , the motile zoospores transform into walled cysts which extend germ tubes that form infection structures called appressoria . In prior studies , we used the traditional strategy of promoter mutagenesis to identify three motifs directing transcription during sporulation and zoosporogenesis [20]–[22] . Resources enabling high-throughput promoter analysis have recently been developed for P . infestans , including a genome sequence and microarray data [18] , [23] . In this report , we combine bioinformatic and functional approaches to identify TFBSs involved in stage-specific expression . More than 100 motifs associated with five life-stages were identified based on over-representation analysis . Most are high-confidence candidates since they also showed conservation in related species or positional bias within promoters . Functional testing of six motifs using reporter genes in P . infestans transformants confirmed their predicted activities .
To obtain data for planning the strategy for motif discovery , the genome-wide distribution of intergenic distances , GC-content in intergenic regions , gene orientations , and stage-specific expression patterns were analyzed . Previous researchers reported that the P . infestans genome is partitioned into gene-dense and gene-sparse regions [18] . We repeated that analysis , incorporating data on gene orientation ( Figure 1A ) . We focused on the 67% of genes that had 5′ intergenic distances of <2 kb , since their transcriptional regulatory sequences would be more likely to interact with those of adjacent genes . Of gene pairs separated by <2 kb , 41% are transcribed from a common intergenic region , with transcripts in the 5′ to 5′ orientation; in such cases the median intergenic distance was 430 nt . Since 5′ UTRs in P . infestans average about 41 nt [24] , this implies that two functional promoters can reside within as little as 300 nt . By comparison , median intergenic regions in S . cerevisiae , Arabidopsis thaliana , and Homo sapiens are 0 . 45 , 1 . 5 and 35 kb , respectively [25]–[27] . The remaining 59% of P . infestans genes are transcribed in the same orientation , with the 3′ end of one gene being adjacent to the 5′ end of its neighbor; their median intergenic distance was 441 nt . We also studied if intergenic regions varied in size depending on how genes were expressed , as this would help indicate the best search space for motifs . This took advantage of a prior microarray study of five developmental stages [23] . Five sets of promoters from 100 genes induced strongly ( >7 . 5-fold ) in each of the stages were assembled . These were from genes up-regulated in sporangia compared to hyphae ( “sporangia promoter set” ) , sporangia chilled for 1 hr to stimulate zoosporogenesis versus untreated sporangia ( “cleavage set” ) , zoospores versus chilled sporangia ( “zoospore set” ) , and germinating cysts forming appressoria versus zoospores ( “germinating cyst/appressoria set” ) . A hyphal set was also developed from genes with higher mRNA levels in hyphae than the other stages . In addition , 150 constitutive genes were identified for which mRNAs varied by less than 25% between stages . Each gene model was curated manually , guided by EST data and sequences from Phytophthora ramorum and Phytophthora sojae . Corrections were applied to the 5′ ends of 13% of the P . infestans gene models . The resulting data suggested that stage-induced promoters are larger than those from constitutive genes . This involved sorting the genes into groups with the different expression patterns , and then calculating median 5′ intergenic distances for the subset that were closely spaced , i . e . 2 kb or less from another gene ( Figure 1B ) . Although each dataset spanned a broad range , the median 5′ intergenic region of constitutive genes was the smallest at 317 nt . Values from the inducible genes ranged from 373 for the sporangia set to 616 for the hyphal set . This resembles trends in other species , presumably since variably-expressed genes bind more transcription factors [25] . To develop background models for evaluating the statistical significance of motif frequencies , we also measured AT content 1-kb upstream of ATG codons . This averaged 49 . 6% , but rose to 54% near the start of genes ( Figure 1C ) . The profile of the curve in the figure may reflect the small size of the typical P . infestans promoter , if its functional regions have a uniform AT content . Alternatively , the core promoter ( the site that nucleates the assembly of a functional preinitiation complex; [28] ) may be more AT-rich than other upstream regions , where most stage-specific TFBSs are expected . Genome-wide , intergenic regions are 49 . 3% AT compared to 46 . 1% for coding sequences [18] . In light of the close proximity of most P . infestans genes , we examined whether genes influenced the transcription of their neighbors . This would be relevant to motif discovery since a TFBS between two genes might influence both . For the analysis , we mapped expression patterns along P . infestans supercontigs and also calculated correlations between adjacent gene pairs . For mapping expression patterns , we linked features in the microarrays to gene models in the P . infestans assembly , upon which the transcription patterns were plotted . This is illustrated in Figure 2A for a representative portion of Supercontig 1 ( not drawn to scale ) , in Figure 2B for four selected regions ( drawn to scale ) , and in Figure S1 for all genes . In these figures , genes showing >2-fold higher mRNA levels than average in one of the five stages are color-coded based on the stage with the maximum level; for example , green means highest in sporangia . Genes with the same stage-induced pattern were not typically adjacent , for example there were no physical clusters of genes having peak expression in sporangia . We also calculated the probability of adjacent genes having the same stage-induced pattern , focusing on 3744 pairs of neighboring genes as well as a subset of 2937 genes residing within 2 kb of each other; while P . infestans encodes about 17 , 797 genes , not all were represented or yielded signals on the arrays . With a few exceptions , adjacent genes showed unrelated patterns of stage-specific induction . Most exceptions involved tandemly repeated gene families , which would be expected to be co-expressed since both promoter and coding regions were likely to have undergone duplication . This occurred most for genes induced in the germinating cysts with appressoria stage; only for this stage were co-induced genes clustered more than expected by random chance at a 95% confidence interval . This was attributable to tandemly duplicated sets of β-glucanases , protein kinases , glucose transporters , and bZIP transcription factors , among others . One example is presented in the lower right portion of Figure 2B , which illustrates three co-expressed β-glucanases ( PITG_03511 , PITG_03512 , and PITG_03513 ) . A second example is an array of genes annotated as glucose transporters ( PITG_13001 to PITG_13007 ) . Such observations are consistent with prior studies that showed that genes induced in this pre-infection stage are rapidly evolving and prone to duplication [29] . In addition to the above which focused on the distribution of stage-induced patterns , we also measured global expression between gene pairs since this might detect subtle interactions . There was a weak tendency for pairs to be co-expressed , with an average Pearson correlation coefficient ( r ) of 0 . 11 . Moreover , the distribution of r values between gene pairs and pairs from a scrambled dataset were significantly different based on a Kolmogorov-Smirnov test ( p<0 . 001 ) . The expression of 375 pairs were highly correlated ( r>0 . 8 ) and 87 were anticorrelated ( r<−0 . 8 ) . Of the 375 co-regulated pairs , 53% were transcribed in the same direction , and 55% of these represented duplicated genes . In contrast , only about 10% of the co-regulated 5′-to-5′ genes were duplicated . There was little correlation between 5′ intergenic distance and co-regulation ( r = −0 . 09 ) . The scheme illustrated in Figure 3 was used to identify candidate stage-specific TFBSs . In brief , the five sets of stage-induced promoters were searched for motifs that were over-represented compared to total P . infestans promoters . A search space of 1-kb of 5′ sequences was selected since this should include most TFBSs , based on the data in Figure 1B . The motifs were then tested for positional bias , orientation bias , and evolutionary conservation . Each of the five stage-induced datasets were searched separately for motifs using BioProspector , MEME , and YMF . These programs were selected since they employ independent methods and scored well in prior comparisons [11] , [30] . We focused on promoters from genes induced >10-fold between developmental transitions ( 443 genes in the five promoter sets ) ; this fold cut-off was raised compared to our earlier analyses to reduce noise in motif discovery . We also focused on motifs detected by at least two of the programs , allowing degeneracy at two sites . About 145 motifs fit this requirement , which were consolidated to 107 by joining those that were similar in sequence and had similar patterns of over-representation . Based on a p-value threshold of 10−2 , 103 showed significant over-representation in at least one stage , which is shown in heat-map format in Figure 4; the motif sequences and number of hits in each dataset are in Table S1 . The overall AT content of the 103 motifs was 49 . 9% , five were palindromes , and lengths ranged from 6 to 9 nt . Approximately 80% of the 103 motifs were linked strongly to one developmental stage or two consecutive stages , and are therefore good candidates for binding sites for transcription factors that determine stage-specific expression . Based on the number of stages for each motif that passed the p-value threshold of 10−2 , about 52 motifs were specific for a single stage . Examples include motif 82 ( M82 ) , which was significantly over-represented in sporangia-induced promoters ( p = 10−8 ) but not the other sets , M95 which associated only with germinating cysts with appressoria ( p = 10−12 ) , and M99 which associated only with hyphae ( p = 10−12 ) . About 21 motifs were significantly over-represented in promoters from two sequential stages , and 10 from three sequential stages . About half of the 21 were over-represented in the germinated cyst/appressoria and hyphal promoters , such as M87 ( p = 10−14 and 10−13 , respectively ) . This was not unexpected , since cyst germ tubes are very similar to hyphae and transition into hyphae . Several motifs were over-represented in sporangia and cleaving sporangia-induced promoters , such as M43 ( p = 10−11 and 10−7 , respectively ) . This was also not surprising since these stages are separated only by a 1-hr cold treatment , and many mRNAs induced in sporangia continue to rise during zoosporogenesis and/or during the zoospore stage . Accordingly , some motifs such as M64 were also over-represented in the sporangia , cleaving , and swimming zoospore promoters ( p = 10−4 and 10−3 , and 10−3 respectively ) . Likewise , several motifs were over-represented in hyphal and sporangia-induced promoters , such as M86 ( p = 10−6 and 10−8 , respectively ) . This may be explained by the fact that oomycete sporangia develop directly from hyphae , or that some tissue samples used for microarray analysis were not very synchronous . Regardless of the explanation , the approximately 80 motifs that associated with promoters from one or two sequential life stages are all good candidates for sites that bind transcription factors with stage-specific activities . Two motifs matched the three promoter sites that were shown previously by mutagenesis to be needed for stage-specific transcription . M97 , which was over-represented in the cleavage promoter set , is a close match ( in the reverse orientation ) to a site required for inducing the NifC gene during that stage [20] . Sporangia-associated motif M43 is an exact match to the site required for inducing the Pks1 gene during sporulation [22] , and a close match ( in the reverse orientation ) to the region needed to induce Cdc14 during sporulation [21] . Not all motifs were associated only with consecutive stages . About 15 were over-represented in promoters from nonconsecutive stages , or both developmental or constitutive promoters ( Figure 4 , Table S1 ) . One example is M60 , which was over-represented in sporangia and swimming zoospore-induced promoters ( p = 10−16 and 10−13 , respectively ) but not the intervening stage of cleaving sporangia ( p = 0 . 8 ) . A total of six motifs ( M1 , M8 , M13 , M60 , M64 , M67 ) occurred more in total promoters than expected by random chance; these may act as general enhancers . Many transcription factors need to act at a certain distance from the transcription start site or other regulatory locations , and therefore their TFBSs concentrate at a certain site within promoter space [31] , [32] . Whether any of the motifs exhibited this bias was determined by mapping them within 200-nt bins from the relevant promoter set; 65 motifs were found to have positionally biased distributions ( column “Pos . Bias” in Figure 4 , Table S1 ) . This may be an underestimate , since convincing evidence of bias could not be drawn for low-frequency motifs . Data for 18 representative positionally biased motifs are shown in Figure 5 . About one-third , such as M8 and M38 , had distributions matching overall promoter size as shown in Figure 1A indicating that these TFBSs lack a strong positional bias . In contrast , motifs such as M68 and M83 tended to reside 200–600 nt upstream of the start codon . Others such as M30 , M39 , M53 ( not shown in Figure 5 ) , M57 , and M82 were found closer to the transcription start site . M30 and M39 do not match known oomycete core promoter motifs , but M53 resembled the Inr or Initiator [33] . Interestingly , M53 was over-represented in promoters induced in the germinating cyst with appressoria stage . As a control , we observed that similar biases were not observed in total promoters , where most matches may be false hits . This is illustrated at the base of Figure 5 for three representative motifs , M87 , M95 , and M97 . These had biased distributions in induced promoters ( Figure 5 , second row from bottom ) , but very different patterns in total promoters ( Figure 5 , bottom row ) . Due to variation in AT-content across promoters ( Figure 1C ) , the controls are not expected to have similar values in each bin . As AT-rich motifs , hits to M87 and M95 due to random chance are more common in the 3′ portion of total promoters , which are AT-rich . The opposite was observed for GC-rich motifs such as M97 . Some transcription factors must orient in a certain direction to fulfill their regulatory function . In S . cerevisiae , for example , 47% of TFBSs were found to have an orientation bias [32] . As shown in the column labeled “F/R bias” in Figure 4 and Table S1 , this was the case for 50 of the 98 non-palindromic motifs ( 52% ) from P . infestans , using a p-value threshold of 10−2 . Cleaving sporangia-associated motif M33 , for example , was detected 87 times in the forward orientation in the 95 cleavage-induced promoters but only 50 times in the reverse orientation . After correcting for the false discovery rate , the bias is even greater at 71 versus 27 . About 78% of P . infestans motif candidates were judged to be conserved in orthologous promoters from P . ramorum or P . sojae . A conclusion about whether a motif was conserved was developed by aligning promoters from about five genes; a match in at least some was considered to be indicative of conservation . Assessments for each motif are shown individually in the “Evol . Con . ” column in Figure 4 and in Table S1 , and results for all motifs are summarized in Figure 6A . Evidence for conservation between P . infestans and both of the other two species was obtained for 52% of motifs . About 10% of motifs were conserved between P . infestans and P . ramorum only , and 16% between P . infestans and P . sojae only . In 11% of cases , motifs were absent from both P . ramorum and P . sojae . In 11% of cases , the motifs were detected at new locations in one or both species; this was taken as an ambiguous result , since while promoter rearrangements are common [34] they are hard to distinguish from false hits . Figure 6 shows representative alignments where conservation was detected . For cleavage-induced P . infestans gene PITG_16321 and its P . ramorum and P . sojae orthologs , for example , perfect matches to M51 were detected in the same location in all three species . The PITG_16321 alignment also reflects the common relationship seen between orthologous promoters: two to four sequence blocks are typically conserved . One usually spans the transcription start site , which in this case contains an Initiator-like sequence at −71 in P . infestans . Other conserved regions are typically found 40–200 nt upstream . For PITG_16321 these are the M51-containing block at −177 , and another at −114 . As will be shown later , the −114 block and conserved nucleotides a few bases to the left and right of M51 do not determine stage-specific expression . Results for three ortholog sets containing sporulation-associated motif M58 are also shown in Figure 6 . For PITG_03886 , M58 is conserved perfectly in the three species . In PITG_09960 , a three-way match also exists allowing for one base change in P . sojae; this was scored as a positive hit , since TFBSs often vary between species [35] . The PITG_14222 alignments show M58 at the same location in P . infestans and P . sojae , but 80-nt upstream in P . ramorum . Since the latter could be a false hit , our scoring scheme classifies M58 in PITG_14222 as conserved only between P . infestans and P . sojae . Since M58 was at the expected location in P . ramorum and P . sojae orthologs of PITG_03886 and PITG_09960 , however , its overall classification is “conserved” . Nearly all of the motifs demonstrated one or more characteristics typical of authentic TFBSs besides over-representation , such as interspecific conservation , positional bias , orientation bias , or palindromy ( Figure 7 ) . Of the 103 motifs , 101 had at least one of these features in addition to over-representation , 78 had at least two , and 25 had three . These classifications help indicate which motifs have the highest likelihood of having a function , in addition to suggesting how they interact with the transcriptional apparatus . The two motifs lacking these additional characteristics were M1 and M66 . These may still be real TFBSs , since not all experimentally confirmed sites exhibit positional bias or directionality , or reside in the same location in orthologs . The two motifs were over-represented in at least one developmental stage with p-values ranging from 10−4 to 10−5 and thus are unlikely to be false hits . A few of the “high-confidence” motifs were close in sequence . These were M93 ( TACATGTA ) and M94 ( TACCGGTA ) , which are palindromes differing only at the two central bases , M32 ( AGC[AG]CAAG ) and M34 ( AGCTGAAG ) which also differ at the two central bases , and M16 ( AAATAAA ) and M91 ( TAAATAA ) which overlap . As mentioned earlier , most motifs from BioProspector , MEME , and YMF had been merged if they differed at two or fewer sites and were over-represented in the same stages . The six motifs remained unmerged since their biases and/or probability distributions and varied . For example , M16 but not M91 was over-represented in hyphal-induced promoters ( p = 10−6 and p = 10−1 , respectively ) , and M93 was more over-represented than M94 in hyphal promoters ( p = 10−26 , p = 10−2 ) . Six of the motifs were subjected to experimental analysis to see if they could drive β-glucuronidase ( GUS ) expression with the expected stage-induced pattern in transformants of P . infestans . As described below , each yielded the expected pattern . First analyzed was M51 , which was predicted to confer expression during zoosporogenesis ( i . e . sporangial cleavage ) . Interestingly , M51 is flanked by two sequence blocks that are conserved in P . ramorum and P . sojae , which are labeled LB and RB in Figure 6 . These flanking sequences were not over-represented in cleaving sporangia promoters , but we considered the possibility that our definition of M51 was smaller than the authentic TFBS . Initial experiments showed that at least part of the LB-M51-RB region was required for zoosporogenesis-specific expression . Plasmid pDEL312 , which contains a 312 nt promoter fused to GUS , yielded expression in sporangia treated at 10°C for 1-hr to induce the cleavage of sporangia into zoospores , but not sporangia maintained at 22°C; for this plasmid and others described below , similar results were observed in multiple transformants . The zoosporogenesis-specific activity of the promoter fragment was shown first by histochemical staining ( as in Figure 8 ) , and later by RNA blot analysis in which bands of the expected size were detected only in the chilled samples ( Figure 9 ) . No activity was seen in hyphae . Indistinguishable results were obtained using a 500 nt promoter ( not shown ) . pDEL187 , which lacked bases upstream of the LB-M51-RB region , showed the same staining pattern and gene induction was confirmed by RNA blot analysis . pDEL104 , which lacks the LB-M51-RB block , showed no expression . Subsequent experiments specifically tested the functions of LB , M51 , and RB by mutating those regions within pDEL187 , and led to the conclusion that only M51 conferred stage-specific expression . Similar results were obtained from histochemical staining ( not shown ) and RNA blot analysis ( Figure 9 ) . Specifically mutating LB had no effect on stage-specific expression ( pMUT1 ) , while altering M51 prevented expression ( pMUT2 ) . As a control , we showed that the native gene in the transformants was induced in sporangia by the cold-treatment . Mutating RB did not block cold-induction of the transgene , although its basal expression seemed to be slightly elevated ( pMUT3 ) . Next , oligonucleotides containing the LB-M51-RB block or M51 alone were fused to the NifS minimal promoter , which contains a transcriptional start site but is not expressed on its own [21] . As shown in Figure 9 , a fusion of LB-M51-RB to the minimal promoter , separated by a 37-nt spacer of random DNA , drove the normal chilling-specific expression of GUS ( pOLIGO1 ) . As a final and definitive test , an oligonucleotide containing M51 alone was shown to also confer this wild-type pattern to transformants ( pOLIGO2 ) . Since the above experiments indicated that at least some motifs could act autonomously , we next tested five other predicted stage-specific motifs by fusing them one at a time to the NifS minimal promoter . Motifs M39 , M58 , M64 , and M75 were most over-represented in the sporangia stage , and each resulted in the sporulation-specific accumulation of GUS ( Figure 8 ) . No staining was seen in nonsporulating hyphae . The effects of the motifs were subtly different , however . Transformants containing M58 showed GUS staining at the earliest stage; these showed expression within hyphae soon after cultures were stimulated to sporulate , and then later in sporangiophore and mature sporangia . This illustrated in Figure 8 where the three panels show ( left to right ) staining within a small segment of a hypha in a sporulating culture , immature sporangia ( lacking basal septa and papilla ) , and mature sporangia . Transformants containing M39 and M64 first exhibited GUS staining in hyphal-like structures that are presumed to be sporangiophores , and then in mature sporangia . In contrast , expression driven by M75 seemed to be activated at a later stage , since staining was first observed in sporangia near maturity . It should be noted that while M58 was most over-represented in the sporangia stage ( p = 10−8 ) , it was also over-represented in hyphae ( p = 10−6 ) and constitutive promoters ( p = 10−3 ) ; perhaps it binds a transcription factor which does not become activated until sporulation is induced . Also tested as a fusion with the minimal promoter was M95 , which was associated with transcription in germinated cysts and appressoria . This resulted in the accumulation of GUS in germinated cysts , including their germ tubes and appressoria ( Figure 8 ) . Staining was first observed 2 hours after encystment . No expression was observed in hyphae , sporangia , chilled sporangia , or zoospores . As described later , this motif is associated with the expression of many pathogenesis-related proteins . Further support for the motifs was provided by electrophoretic mobility shift assays ( EMSA ) involving M51 , M58 , and M75 . As shown in Figure 10 , each motif bound a protein from nuclear extracts of cleaving sporangia ( M51 ) or sporangia ( M58 , M75 ) . Binding appeared to be specific based on comparing different unlabeled competitors . These included a specific competitor ( same sequence as the labeled probe ) , a nonspecific competitor ( a random sequence ) , and mutated competitor ( same as the labeled probe , but with the motif mutated ) . In each case the nonspecific and mutated competitors had little effect in reducing the binding of the labeled probe , compared to the specific competitor . For M51 , several bands were detected , which was suggestive of a multi-protein complex or the binding of proteins of different sizes . Several classes of proteins have been identified that play roles in pathogenesis , of which many are secreted and sometimes induced during infection [29] , [36] , [37] . To assess the usefulness of our data for understanding how such genes are regulated , we checked their promoters for the motifs , focusing on motifs associated with the germinating cyst/appressoria stage . This involved analyzing the main classes of genes annotated by Raffaele et al . [29] as potentially encoding secreted pathogenicity factors , of which many are induced during plant infection . As shown in Table 1 , four motifs associated with the germinating cyst/appressoria stage and ten linked to both the hyphal and germinating cyst/appressoria stages were over-represented ( p<0 . 05 ) in such genes . These included those encoding plant cell-wall degrading enzymes , glucanase inhibitors , Nep1-like ( NLP ) toxins , PcF toxins , elicitins and elicitin-like proteins ( potential sterol carriers ) , proteases , protease inhibitors , and RXLR effectors . As expected , motifs linked to stages such as sporangia and zoospores were typically under-represented ( Table S2 ) . Not all genes in each group contained a germinated cyst/appressoria motif in their promoters , however . For example , such motifs were in only 225 of the 493 RXLR promoters , with 66 containing M95 , 163 having M101 , and 49 having M103 ( Table S2 ) . As only some RXLR genes are induced during infection [18] , [29] , [38] , we checked for a correlation between motif and expression pattern . RXLR genes with a germinated cyst/appressoria motif were more likely to be infection-induced than those without; many had more than one motif , with a correlation between the degree of induction and motif number ( r = 0 . 27 , p = 0 . 04 ) . Crinkler genes , which are not typically infection-induced but are considered to encode pathogenicity factors due to the ability of some to produce necrosis in plants [18] , had M93 as the sole over-represented motif . M93 , a palindrome , is over-represented in both germinated cyst/appressoria and hyphal-induced genes and occurs 4150 times within P . infestans promoters . Its abundance suggests that it is associated with general growth and not specifically with pathogenesis . We assessed the extent to which the presence of a motif predicts a gene's expression pattern . This involved searching promoters of all 7 , 862 genes on the microarrays for motifs associated with sporangia-induced genes and germinated cyst/appressoria genes , using 500-nt of DNA upstream of the start codon as the search space . We then compared motif frequencies in promoters induced by >5-fold at each stage versus non-induced promoters . The 99 sporangia-induced and 103 germinated cyst/appressoria-induced promoters used originally for motif discovery were excluded from these analyses , to test if our earlier results extended to all P . infestans genes . Each of the 11 sporangia-associated motifs occurred more often in the induced promoters than non-induced controls ( Table 2 ) . On average , each motif was 66% more likely to occur in an induced promoter , with individual motifs showing a 21 to 100% enrichment . For example , M8 was found in 14 . 3% of induced promoters compared to 11 . 6% of non-induced promoters , representing a 23% enrichment . It is important to note that hits due to random chance are always expected to greatly exceed the number of functional TFBSs for reasons elaborated upon in Discussion [39] . Most of the 15 motifs linked to the germinated cyst/appressoria stage were also over-represented in that stage when the total microarray data were analyzed ( Table 2 ) . Each was on average 19% more likely to be in an induced promoter compared to controls . It is notable that four of the motifs were not enriched in the genome-wide set of induced promoters , including M95 . Since our functional tests showed that M95 conferred expression in the germinated cyst/appressoria stage , it is possible that M95 binds a bifunctional transcription factor or has its activity mediated by other transcription factors . We also checked for the association of a sporulation-associated motif with expression pattern in ten genes from P . infestans that were not on the microarrays . Motif M8 was chosen for this exercise simply since it was first on the list in Table 2 . We identified promoters containing M8 , used RT-qPCR to measure mRNA in sporangia and nonsporulating hyphae , and assessed if M8 was within the orthologous promoter from P . sojae ( Table 3 ) . Of six P . infestans genes in which the P . sojae ortholog also contained the motif , five were induced by >2-fold in sporangia . This was significant ( p = 0 . 004 ) , compared to the likelihood of this fraction of genes being induced by random chance . In contrast , none of the four P . infestans genes that lacked M8 in their P . sojae ortholog was induced based on the 2-fold cutoff .
Genome-wide searches for promoter motifs shared by co-expressed genes have been performed in model animals , plants , and fungi [14]–[17] , but only on a limited scale in pathogens [40] , [41] . The strategy seemed attractive for Phytophthora since its modest transformation efficiencies make motif discovery through traditional means challenging [42] . The success of our approach was shown not only by the identification of 100 putative TFBSs , but the fact that all six motifs tested performed as predicted in functional assays . Nearly all motifs also exhibit at least one feature typical of TFBSs besides over-representation such as positional bias , orientation bias , or evolutionary conservation . Discovering the motifs , which include several associated with pathogenicity factors , is a key step towards understanding the networks that control development and host infection in Phytophthora and similar approaches should be useful in other pathogens . Several features contributed to our approach by increasing the sensitivity of our searches and reducing false positives . First , our requirement that motifs be identified by two of three algorithms served as a stringent filter . Second , we focused on promoters that show large changes , which was possible since major shifts in mRNA levels occur during the P . infestans life cycle as about 12% of genes change by >100-fold in the stages addressed by this study [23] . Third , gene models were manually curated to accurately define the search space . Finally , since intergenic distances are typically small in P . infestans , most regulatory regions were probably within the 1-kb search space . Our analysis of the overall transcriptional landscape of P . infestans has also helped illuminate the structure and function of its promoters; few promoter studies have previously been performed in the entire Kingdom Stramenopila , which includes diatoms and brown algae in addition to oomycetes [43] . Remarkably , the median intergenic distance within gene-dense regions of P . infestans is even less than that of most yeasts [25] , [44] . The ratio of adjacent P . infestans genes that are transcribed in the same direction versus from a shared or adjacent promoter region is 1 . 43 , which is higher than that of S . cerevisiae and A . thaliana [45] . This presumably reflects functional constraints associated with having small adjacent promoters , which is reflected in the co-expression or anti-correlated profiles of about 10% of adjacent P . infestans genes . Excluding cases where co-expressed pairs are duplicated genes , most adjacent genes in P . infestans are nevertheless transcribed independently . Our prediction of directionality for more than half of the motifs helps to explain how this independence is mediated . The predominant mechanism for regulating transcription in Phytophthora may also not involve chromatin-level effects , which in yeasts and metazoans are inferred to extend up to 4 kb and tens of kilobases , respectively [46] . Nevertheless , of the approximately 300 transcription factors annotated within each Phytophthora genome , several belong to families associated with chromatin remodeling [47] . Relative simplicity in transcriptional regulation in P . infestans is also implied by our finding that each of six stage-induced motifs tested conferred tissue-specific expression with a minimal promoter . Combinatorial control , not counting transcription factor heterodimerization , thus may not be a principal feature of stage-specific regulation in oomycetes , unlike other eukaryotes with complex genomes [48] , [49] . Since position effects in P . infestans make it challenging to compare transgene expression between transformants [50] , our data are silent on roles of other TFBSs in quantitative expression . The potential involvement of only a few TFBSs per gene is consistent with our observation of limited blocks of similarity between P . infestans , P . ramorum and P . sojae promoters , as shown in Figure 6 . As the three organisms are relatively distant in molecular phylogenies [51] and have significant morphological differences , it would be useful to know if the orthologs had similar patterns of expression . Our analyses of motifs associated with sporangia and germinated cyst/appressoria stages ( Table 2 ) suggests that the occurrence of a motif has utility in predicting expression pattern . However , it is important to recognize the limitations of this approach . Since TFBSs are short and often degenerate , they occur by random chance in great abundance . Moreover , TFBS function depends on chromatin structure and often the co-occurrence of other TFBSs . Some transcription factors are also bifunctional , leading to different outcomes depending on post-translational modification or co-regulators [52] . Because of such complications , Wasserman and Sandelin [39] posited the “futility theorem” which states that the great majority of predicted TFBSs lack function: it is thus futile to predict expression based on the occurrence of a promoter motif . Our experience in P . infestans was more encouraging , however . For example , while the presence of a sporulation-associated motif was a weak forecaster of expression pattern , the predictive value was fairly strong if the motif was conserved in another Phytophthora . The limits of predictions based solely on motif presence can be illustrated for the 11 sporulation-associated motifs in Table 2 . Based on their average size and base composition , and using 500-nt of promoter sequences as a search space , about 8250 promoters should contain one or more of the 11 motifs by random chance . Extrapolating from microarray data , however , only about 2200 genes are sporulation-induced , so random hits exceed functional TFBSs by a 4 to 1 ratio . Nevertheless , a future can be envisioned where better predictions of expression based on motif occurrence alone may be possible . In S . cerevisiae , a network model that integrated expression patterns of 2 , 587 genes under 255 conditions of growth and development with 666 TFBS definitions using AND , OR , and NOT logic resulted in fairly good predictions of expression of about 3/4 of the genes [53] , [54] . Inferences about the complexity of the networks that control development and pathogenesis in P . infestans may be drawn from our observation that roughly 10 to 20 motifs were linked to each stage of the life cycle . This is consistent with observations that show that sporangia and zoospore formation involves several steps and signaling pathways [20] , [55] , [56] . Characterizing transcription factors that bind the motifs will help reveal details of these pathways , and enable chromatin immunoprecipitation studies to confirm the target genes [57] . Studying the transcription factors may also lead to strategies for blocking diseases , by interfering with the expression of proteins used for overcoming host barriers and defenses .
Expression data were from a prior study that used Affymetrix microarrays to measure mRNA during the stages addressed by this paper [23] . Reliable expression calls were detected for 12 , 463 of the 15 , 650 sequences targeted by the arrays . Since the microarrays predated the current draft genome which is based on strain T30-4 ( available from the Broad Institute of Harvard and MIT ) , we linked the microarray sequences to annotated T30-4 genes using BLASTN , but excluded genes on small contigs to reduce errors in analyses of intergenic distances and co-expression . By selecting the best hit with >97% identity , 7 , 862 genes with reliable expression data in the five life-stages were matched , including 3944 adjacent gene pairs of which 2937 were within 2 kb of each other . These were mapped to the assembly , omitting unexpressed or missing genes . Datasets of P . infestans promoters included 1-kb of sequences 5′ of predicted open reading frames . Total promoters were downloaded from the Broad Institute database , and then subsets were extracted using custom scripts . Sets included promoters from the differentially-expressed gene sets described above , in which mRNA levels were induced by at least 7 . 5-fold compared to the prior development stage ( p<0 . 05 based on replicates ) . Sets of at least 100 promoters were used for calculating 5′ intergenic distances . For identifying over-represented motifs , analyses were limited to genes induced >10-fold , which corresponded to 99 , 95 , 46 , 103 , and 100 in the sporangia , cleavage , zoospore , germinated cyst , and hyphal sets , respectively . Prior to extracting promoters , gene models were examined and corrected as needed ( changing 8 , 14 , 17 , 3 , and 9 promoters , respectively ) . This mostly involved eliminating introns that contradicted EST evidence , or spanned regions that when converted to exons maintained the reading frame and had high similarity to P . ramorum and P . sojae orthologs . In addition , a constitutive promoter dataset was established from 150 genes that showed <25% variation between the stages . Promoters from P . ramorum and P . sojae were extracted from genome assemblies downloaded from the Virginia Bioinformatics Institute . Stand-alone versions of MEME ( version 4 . 3 . 0; [12] ) , YMF ( version 3 . 0; [9] ) , and BioProspector ( release 2; [10] ) were employed . MEME ran with minimum and maximum widths of 5 and 8 , respectively , using 5 iterations . Gap opening and extension costs were 11 and 1 , respectively , any number of repetitions were allowed , and the E-value cut-off was 10−5 . YMF used a value of 8 for lenOligo ( the number of non-spacer characters ) and output was sorted by z-score . BioProspector used a value of 8 for motif width , with the 100 top motifs reported per run . The program was run 10 times on each set of promoters and a PERL script was used to eliminate redundant motifs . Initial outputs ( 382 motifs from BioProspector from the five stage-induced promoter datasets , 450 from MEME , and 1261 from YMF ) were submitted to a PERL script to detect motifs detected by at least two programs . These were then merged to eliminate redundancy , allowing degeneracy at two sites . P values for over-representation of the final motifs were calculated based on a hypergeometric distribution , using Fisher's Exact test . The locations , numbers , and orientations of each motif were extracted from the datasets using custom Perl scripts , with tests for orientation bias employing Chi square . Motifs that were positionally biased were identified by counting the number of hits per 200-nt bin , extending 1-kb upstream of the start codon , and checking for deviations from a random allocation model using a 2 by 5 Fisher's Exact test . Candidate orthologs were identified in P . capsici , P . ramorum , and P . sojae genome databases ( from the Joint Genome Institute of the U . S . Department of Energy ) using BLASTP . Their promoters were then extracted , and aligned with CLUSTALW using gap opening and extension penalties of 10 and 0 . 1 , respectively , and DIALIGN using default parameters [58] , [59] . After preliminary tests , P . capsici was omitted since the version of its genome assembly available at the time contained too many gaps and erroneous gene models . Putative three-way orthologs ( P . infestans , P . ramorum , P . sojae ) were identified for 66% of genes . Up to five genes ( mean = 4 . 6 ) were typically examined for each motif . If a motif appeared in the alignment at the same position in at least one comparison it was scored as being evolutionarily conserved . A score of “ambiguous” was given for motifs found in a different location ( including by searching in both orientations ) ; on average , 8% of promoters would have a false positive . Conservation at the same site in ortholog sets for all genes was never expected: gene models in the different species often started at different locations , errors may have occurred in selecting orthologs , not all orthologs might have similar expression patterns , and promoter rearrangements are common during evolution . Stable transformants were generated from isolate 1306 ( from tomato in California , USA ) using a liposome-assisted protoplast method as described [42] , except that Extralyse ( Laffort , Bordeaux , France ) was used as the β-glucanase . Non-sporulating mycelia were obtained by inoculating clarified rye-sucrose broth with a sporangial suspension ( 104/ml ) , followed by 48 hr incubation at 18°C . Sporangia were obtained from rye-sucrose agar cultures by adding water , rubbing with a glass rod , and passing the fluid through a 50 µm mesh to remove hyphal fragments . To induce cleavage , sporangia were placed in 100 mm glass culture dishes resting on ice ( internal temperature 8–10°C ) for 60 min . Germinated cysts were obtained by allowing the chilled sporangia to release zoospores , to which CaCl2 was added to 0 . 5 mM followed by vortexing for 1 minute and incubation at 18°C for up to 9 hr . Gene expression analyses involved RNA blotting and β-glucuronidase ( GUS ) staining as described [21] . Constructs for testing promoters were based on pNPGUS , which is an improved version of pOGUS [60] , and pNIFS-NPGUS . Each contains a promoterless GUS gene and an nptII selectable marker driven by the ham34 promoter . The improvements in pNPGUS included the addition of additional cloning sites upstream of GUS ( the polylinker from pBS-KS2+ ) and translational stop codons upstream of the polylinker to reduce the number of cryptic transcripts with GUS activity . pNIFS-NPGUS contains a 74-nt minimal promoter from the NifS gene of P . infestans [21] , [61] cloned into XmaI and EcoRI sites of the polylinker . Promoter fragments were inserted into pNPGUS or pNIFS-NPGUS as fragments amplified by polymerase chain reaction , or by ligating double-stranded oligonucleotides into the XbaI and XmaI sites of the vectors . Oligonucleotides used for cloning are listed in Table S3 . Nuclear protein isolation and EMSA were as described [22] , except that heparin agarose was not used for the extractions . EMSA involved mixing 5 µg of nuclear protein with 1 µg poly dI-dC and 1 . 6 ng of 32P-labeled probe in buffer containing 1 mM dithiothreitol for 15 min at room temperature followed by 30 min on ice , followed by electrophoresis at room temperature on a 4 . 5% acrylamide gel . For competition assays , protein was incubated with unlabeled DNA for 15 min and then the labeled probe for 30 min on ice . Double-stranded oligonucleotides generated using the sequences in Table S3 were used as probe and cold ( unlabeled ) competitors . Mutated competitors were altered for the predicted motifs ( A↔C , G↔T ) , and the nonspecific probe was a random sequence . qRT-PCR employed DNAse-treated RNA , pooled from two biological replicates , which was reverse-transcribed using oligo-dT with a first-strand synthesis kit from Invitrogen ( Carlsbad , CA , USA ) . Amplifications employed hot-start Taq polymerase with primers targeted to the 3′ regions of genes , typically yielding amplicons of 100 to 125 nt , using SYBR Green as a reporter . Reactions were performed in duplicate using the following conditions: one cycle of 95°C for 8 min , and 35 cycles of 95°C for 20 s , 55°C for 20 s , and 72°C for 30 s . Controls lacking reverse transcriptase and melting curves were used to test the data . Results were normalized based on primers for a constitutively expressed gene encoding ribosomal protein S3a , and expression was determined by the ΔΔCT method .
|
The genus Phytophthora includes over one hundred species of plant pathogens that have devastating effects worldwide in agriculture and natural environments . Its most notorious member is P . infestans , which causes the late blight diseases of potato and tomato . Their success as pathogens is dependent on the formation of specialized cells for plant-to-plant transmission and host infection , but little is known about how this is regulated . Recognizing that changes in gene expression drive the formation of these cell types , we used a computational approach to predict the sequences of about one hundred transcription factor binding sites associated with expression in either of five life stages , including several types of spores and infection structures . We then used a functional testing strategy to prove their biological activity by showing that the DNA motifs enabled the stage-specific expression of a transgene . Our work lays the groundwork for dissecting the molecular mechanisms that regulate life-stage transitions and pathogenesis in Phytophthora . A similar approach should be useful for other plant and animal pathogens .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"plant",
"biology",
"botany",
"microbiology",
"developmental",
"biology",
"fungi",
"plant",
"science",
"plant",
"pathology",
"mycology",
"gene",
"expression",
"biology",
"molecular",
"biology",
"genetics",
"genomics",
"molecular",
"cell",
"biology",
"computational",
"biology",
"genetics",
"and",
"genomics"
] |
2013
|
Genome-wide Prediction and Functional Validation of Promoter Motifs Regulating Gene Expression in Spore and Infection Stages of Phytophthora infestans
|
Estimates of dengue transmission intensity remain ambiguous . Since the majority of infections are asymptomatic , surveillance systems substantially underestimate true rates of infection . With advances in the development of novel control measures , obtaining robust estimates of average dengue transmission intensity is key for assessing both the burden of disease from dengue and the likely impact of interventions . The force of infection ( λ ) and corresponding basic reproduction numbers ( R0 ) for dengue were estimated from non-serotype ( IgG ) and serotype-specific ( PRNT ) age-stratified seroprevalence surveys identified from the literature . The majority of R0 estimates ranged from 1–4 . Assuming that two heterologous infections result in complete immunity produced up to two-fold higher estimates of R0 than when tertiary and quaternary infections were included . λ estimated from IgG data were comparable to the sum of serotype-specific forces of infection derived from PRNT data , particularly when inter-serotype interactions were allowed for . Our analysis highlights the highly heterogeneous nature of dengue transmission . How underlying assumptions about serotype interactions and immunity affect the relationship between the force of infection and R0 will have implications for control planning . While PRNT data provides the maximum information , our study shows that even the much cheaper ELISA-based assays would provide comparable baseline estimates of overall transmission intensity which will be an important consideration in resource-constrained settings .
Affecting more than one hundred countries with 2 . 5 billion people at risk and 50–100 million infections per year as estimated by the World Health Organisation ( WHO ) , dengue is a global public health burden [1] . Estimates of global dengue distribution and transmission intensity ( as quantified by either the force of infection—the per capita rate at which susceptible individuals acquire infection , or the basic reproduction number , ( R0 ) remain ambiguous [2] . Infection with any of the four serotypes of dengue virus ( DENV-1 , 2 , 3 , and 4 ) can cause dengue fever with increased risk of more severe dengue with subsequent heterologous infections . Individuals develop protective monotypic immunity upon infection with a single serotype . Cross-reactive immunity is short-lived and the waning of antibodies below a threshold can facilitate antibody-dependent enhancement ( ADE ) upon secondary heterologous infection increasing the risk of more severe outcomes of dengue ( such as dengue haemorrhagic fever ( DHF ) and shock syndrome ( DSS ) ) [3–5] . The impact of cross-immunity and tertiary and quaternary infections are controversial . The estimated duration of short-term cross-protection varies widely from four months to 9 years [6] , 5–12 months [7] , 2 years [8] , and 1–3 years [9] . However whether this protects against infection or clinically apparent disease is unknown . Therefore individuals may still contribute to onward transmission [8 , 10 , 11] . Clinically apparent tertiary and quaternary infections are rarely reported , and cannot be tested for retrospectively [10] . Wikramaratna et al . showed that tertiary and quaternary infections allows for the high seroprevalence at very young ages observed in Haiti [12] and Nicaragua [13] better than when assuming complete protection after two heterologous infections [10] . There are no antiviral therapies available as yet and disease control is restricted to vector control , community education and the development of an effective dengue vaccine . Recent estimates of the global distribution of dengue and the resulting disease burden have refined our understanding , but remain controversial [2] . Shepard et al . highlight some of the difficulties in accurate dengue burden estimation including differences in surveillance systems leading to underestimation of dengue incidence , the lack of standardized reporting procedures or diagnostic criteria , and the lack of integration between private and public sectors [14] . Previous studies have attempted to estimate the burden of dengue and associated economic costs in South East Asia and South America by calculating expansion factors from systematic literature reviews , collation of existing data , and population-based cohorts [15–18] . In particular , Bhatt et al . ’s estimate of 390 million dengue infections per year is three times higher than previous official WHO estimates , with India accounting for 34% of that total [2] . Motivated by previous work on malaria , the Bhatt et al . analysis relied on correlating their geographic niche-modelling based estimates of dengue presence with burden estimates derived from serological surveys . While an improvement on previous approaches , the fact that dengue infection induces serotype specific neutralising immunity weakens the parallels with malaria , in that the maximum number of dengue infections an individual can experience is strictly limited ( while a person can experience dozens of malaria infections in their lifetime ) . Here we argue that obtaining robust estimates of the geographic variation in average dengue transmission intensity—as quantified by the basic reproduction number , R0 ( the average number of secondary cases resulting from the introduction of a single infectious individual into a large susceptible population [19] ) , of each serotype—is key to improving the reliability of burden estimates . In addition , a quantitative understanding of variation in transmission intensity is essential to assessing the likely impact of interventions such as vaccine [20 , 21] or novel vector control measures [22–24] . However , with no standardised diagnostic method , challenging clinical diagnosis ( Box 1 ) and highly variable surveillance systems , there is no consistent way to estimate global dengue transmission [25–27] . Dengue transmission is geographically highly heterogeneous , even down to very fine spatial scales [28] . Most model-based estimates of dengue transmission intensity and reproduction number have utilised case-notification data , which heavily depend on the quality of the surveillance system and the health infrastructure of the country in question [29–36] . Additionally , since the majority of dengue infections generate only mild symptoms , are asymptomatic , or are clinically diagnosed as a viral infection , even sensitive healthcare-based surveillance systems substantially underestimate true rates of infection [37 , 38] . Serological data are therefore invaluable in quantifying dengue transmission , in being able to identify both symptomatic and asymptomatic past infections and thus quantify infection prevalence and incidence in the population as a whole . Here we utilise published age-stratified seroprevalence surveys and estimate the force of infection ( λ ) and corresponding basic reproduction number ( R0 ) for dengue in a variety of settings . Due to the much lower costs , future seroprevalence studies are still likely to depend on IgM or IgG enzyme-linked immunosorbant assays ( ELISAs ) rather than the more labour intensive plaque-reduction neutralisation tests ( PRNTs ) . The comparison of estimates derived from IgG , IE and PRNT data allows us to determine the usefulness of less expensive assays .
We searched MEDLINE , EMBASE , and Web of Knowledge for publications reporting age-stratified dengue serological surveys . Fig 1 describes the search process and search terms used . Studies published before 1980 were not included in the analysis as we were interested in contemporary dengue transmission . Studies reporting age-specific seroprevalence for at least 5 age groups were included and categorised according to the assay type used . Studies reporting less than 5 age groups were excluded as these studies tended to have wide age groups where the mean seroprevalence did not accurately reflect the variability in seroprevalence within that age group . Data were extracted from published datasets where age-specific seroprevalence was tested by IgG ELISAs , inhibition ELISAs ( IEs ) or PRNTs . IgG and IE data are both non-serotype specific and we refer to them interchangeably . In the context of dengue , seroprevalence measures obtained with IgG ELISAs only give an indication of whether an individual has ‘ever’ been infected and do not differentiate between infecting serotypes or identify the number of past infections . Since infection with one serotype only provides homologous immunity , a seropositive individual may still be susceptible to secondary heterotypic infection [39] . We fitted the single cross-sectional IgG datasets using a simple catalytic model ( model A ) . The model assumes a constant infection hazard λ , with infection causing individuals of age a to move from a seronegative x ( a ) to a seropositive z ( a ) state [19] . Since some datasets appeared to have declining seroprevalence with age , we extended model A by assuming that protection could decay with age at a rate α ( model B ) . Whenever yearly cross-sectional IgG data were available from the same location , these data were fitted using a time-varying catalytic model ( model C ) which allowed estimation of the periodicity ( T ) , seasonal amplitude ( δ ) and within-year timing ( θ ) of dengue outbreaks , and the critical age ( Acrit ) and scale ( S ) at which exposure levels change . See the Supporting Information for full details ( S1 Text ) . In order to fit serotype-specific PRNT data , we applied the multi-strain catalytic model developed by Ferguson et al . [40] . Different model variants were assessed , which explored different assumptions on serotype interactions . Model D1 assumed no serotype-interaction . Model D2 assumed that cross-protection or enhancement did not vary by serotype . Model D3 assumed that the magnitude of cross-protection or enhancement varied by the primary infection serotype . Last , model D4 assumed that the magnitude of cross-protection or enhancement depended on the serotype of the secondary infection . Moreover , for comparison purposes , we fitted model A to PRNT data , having defined individuals with PRNT titres below the detection limit for all four dengue serotypes as seronegative and individuals with at least one PRNT titre over the detection limit as seropositive . Since assays differed between surveys , here the detection limit also varied from study to study . We defined a beta-binomial likelihood for models A—C and a multinomial likelihood for models D1-D4 . Models were fitted to the data using the Metropolis-Hasting Markov Chain Monte Carlo ( M-H MCMC ) algorithm using the R Statistical Package ( version 3 . 1 . 0 , R Development Core Team , Vienna , Austria ) [41] . Full details are given in S1 Text . We assumed that dengue is at endemic equilibrium and that the force of infection λ is constant in time in all cases except model C . Unless otherwise stated , we assumed that all four serotypes of dengue were in circulation . Since IgG data contain no information on the infecting serotype , we assumed that the four dengue serotypes are equally transmissible and estimated a single reproduction number applicable to each serotype . For the PRNT data , since we were able to estimate serotype-specific forces of infection , we computed strain-specific reproduction numbers as described by Ferguson et al . [40] . We computed the reproduction numbers under two different assumptions on the number of infections required to obtain full protection against infection by any dengue serotype . This allows us to explore whether tertiary and quaternary infections contribute to transmission significantly . Under assumption 1 complete protection is obtained upon quaternary infection ( all four infections contribute equally ) ; whilst under assumption 2 complete protection is reached upon secondary infection ( only primary and secondary infections are infectious ) . Under assumption 2 we were also able to incorporate cross-immunity leading to inhibition or enhancement of susceptibility to secondary infection . For each model variant other than B , we computed the serotype-specific basic reproduction number under assumptions 1 and 2 . We only considered model B under assumption 1 , as decay of immunity by definition allows an arbitrary number of infections to occur . Full details are given in S1 Text .
We identified 53 studies reporting age-specific seroprevalence from a total of 15 , 525 potentially relevant papers ( Fig 1 ) . Of these , 38 used non-serotype specific assays including IgG and inhibition ELISAs ( IE ) . Only nine studies used PRNTs and five studies reported results from multiple assays . Excluding studies with less than 5 reported age groups from further analysis left a total of 30 surveys from 18 countries for IgG data , and 7 studies from 5 countries for PRNT data . 28 ( out of 30 ) surveys from 17 countries were cross-sectional IgG seroprevalence surveys from a single year . The remaining 2 ( out of 30 ) surveys were conducted in Nicaragua and combined provided 7 years’ worth of cross-sectional inhibition ELISA ( IE ) data . Most IgG surveys identified were conducted in 2000–2010 ( 23/30 ) , while most PRNT surveys were conducted in the 1990s ( 4/7 ) . Although recent serosurveys used commercial diagnostics , many studies used in-house assays . Tables 1 and 2 summarises the study and demographics of the datasets retained for analysis from the corresponding or closest year . All studies summarised in Table 1 were fitted using model A and B , and model C was also fitted to the two Nicaraguan datasets ( Table 1 ) . Models D1—D4 were fitted to studies summarised in Table 2 . Only an overall force of infection could be estimated from non-serotype specific IgG data . As expected , estimates of the force of infection varied widely between countries and , to a lesser extent , within countries ( Fig 2A ) . Southeast Asian countries known to be hyper-endemic for dengue , such as Vietnam and Thailand , had a higher force of infection compared with most sites in the Americas [75] . Corresponding estimates of R0i varied according to the assumptions made regarding host immunity ( Fig 2B ) . Assuming that two heterologous infections are sufficient for complete immunity ( Assumption 2 ) produced up to two-fold higher estimates of R0i compared to when we assumed that quaternary infections are required for complete immunity ( Assumption 1 ) . However , R0i estimates under these two assumptions converge as the estimated force of infection decreases . With age-structured serosurvey data from multiple sequential years ( as was available for Nicaragua , Table S3 ) , it is possible to estimate temporal and age-specific changes in exposure [13 , 68] ( Fig 3A ) . We fitted a model ( model C ) to those data which allowed for the force of infection to vary sinusoidally over time and to change at ( fitted ) age threshold . We estimated that exposure increased in individuals over 3 . 9 years old ( 95% CI: 2 . 7–5 . 4 years ) , with the estimated force of infection during the study period ( 2001–2007 ) being 0 . 323 ( 95% CI: 0 . 261–0 . 377 ) above 3 . 9 years and 0 . 174 ( 95% CI: 0 . 118–0 . 280 ) below 3 . 9 years . These estimates represent the average total force of infection for all four serotypes in circulation . The force of infection was estimated to vary with a period of 8 . 8 years ( 95% CI: 1 . 3–12 . 5 years ) . Resulting estimates of R0i ( Fig 3B ) showed the same dependence on immunity assumptions as the point estimates derived from single serosurveys ( Fig 2 ) , but interestingly showed less temporal variation than the force of infection estimates ( Fig 3A ) . PRNT data are serotype-specific , allowing us to estimate the force of infection ( λi ) and basic reproduction number ( R0i ) for each serotype individually ( Fig 4 ) . Estimates varied widely between different surveys , again highlighting the heterogeneity of dengue transmission . Within the same survey , serotype-specific differences in transmission intensity were apparent , demonstrating how a certain serotype may be more dominant at any one time point . For example , for model D2 , force of infection estimates for Haiti were 0 . 046 ( 95% CI: 0 . 010–0 . 179 ) for DENV-1 but 0 . 219 ( 95% CI: 0 . 088–0 . 445 ) for DENV-4 . Comparison of cross-protection or enhancement parameters under different assumptions allowed us to estimate the probable serotype causing primary and secondary infections . However , due to the wide credible interval of the estimated parameter , it is difficult to definitively determine the sequence of infections ( Tables S5—S8 in S1 Text ) . For all datasets , the model fit improved when we assumed some level of inter-serotype interaction , demonstrating that inter-serotype interactions play an important role in dengue dynamics . Interestingly , the serotype-specific estimates of the reproduction number did not scale linearly with the estimated values of the force of infection , although the relative order is maintained i . e . if λ3 < λ4 then R03 < R04 . If one serotype dominates , as was the case in Haiti , changes in the force of infection of the other non-dominant serotypes marginally affect the estimates of the reproduction number of the non-dominating serotypes . In order to compare the estimates of dengue force of infection derived from IgG and PRNT assays , we also analysed the PRNT data ignoring strain-specificity ( i . e . treating PRNT data as if it were IgG data ) , by categorising individuals as ‘seronegative’ if their PRNT titers were negative for all serotypes , or seropositive if they tested positive for at least one serotype . We used the same thresholds for seronegativity used by each source study . The resulting force of infection estimates generated using model A were consistent with the sum of the individual serotype-specific λ estimates obtained from the full PRNT datasets . This consistency was highest when some level of inter-serotype interaction ( cross-protection or enhancement ) was allowed for ( Fig 5 ) .
From a literature review , we selected 39 studies reporting age-structured seroprevalence data obtained with IgG/IE ( 31 out of 39 ) or PRNT ( 8 out of 39 ) assays in 22 different locations from 1980 to 2010 . From each dataset , we estimated dengue transmission intensity , quantified by the force of infection ( λ ) and the basic reproduction numbers ( R0i ) . Overall , our estimates highlight the highly heterogeneous nature of dengue transmission in both space and time , and by serotype . Our analysis also highlights how the relationship between the force of infection and R0i is affected by underlying assumptions about serotype interactions and immunity . The majority of our estimates of R0i from 22 countries ranged from 1–4 ( 28 out of 28 and 24 out of 28 from model A fitted to IgG datasets under assumption 1 and 2 respectively , and 6 out of 7 from model D2 fitted to the PRNT surveys ) . Dengue epidemiology differs between the Americas and Southeast Asia . Severe dengue predominantly affects children in Southeast Asia in contrast to the Americas where disease more often manifests in adults as the milder dengue fever [75] . However the changing demographics in Thailand ( lower birth and death rates ) have increased the average age of DHF suggesting that the epidemiology will continue to evolve [36] . However with the cross-sectional data we use in this study it is difficult to determine whether the higher force of infection in South East Asia is a reflection of the length of time dengue has been in circulation . The recent Phase III dengue vaccine trial conducted in several countries in Latin America showed that the forces of infection are highly heterogeneous across Latin America , with some countries comparable to South East Asia ( Columbia and Honduras ) and others having much lower forces of infection ( Mexico and Puerto Rico ) [76] . However , multiple cross-sectional surveys or cohort studies would be needed to estimate how forces of infection by age have changed over time . The low R0i estimated in the Indian subcontinent is probably due to the lack of datasets from this region and the spatial heterogeneity of transmission within that large region . The one serosurvey from India used in our study was conducted in Andaman , an island with a low population density where we estimated a very low force of infection . It is likely that the epidemiology of dengue on Andaman is not representative of dengue epidemiology on the mainland . Seroprevalence surveys have the benefit of not being affected by surveillance system sensitivity or case reporting rates , but still have several limitations ( Box 1 ) [77 , 78] . A particular issue is the wide variation in the assays used between studies ( Table 1 ) . Optimally , one would assess the sensitivity of transmission intensity estimates to factors that varied between assays , such as the threshold used to define seronegativity . However , such an analysis requires access to the raw titer data which was not provided in any of the publications we reviewed . Additionally seroprevalence surveys sometimes use serum samples collected for a different purpose and therefore may not be representative of the population . Six out of the 37 studies used such samples: from blood banks [44] , ante-natal clinics [64] , hospitals [55 , 79 , 80] , or residual samples from a different study [66] . Use of convenience samples can increase the volume of serological data produced , but the potential biases such sampling introduces must be taken into account when analysing such data . Although we can only calculate a total force of infection across all serotypes from non-serotype specific data ( such as surveys using IgG ELISA assays ) , such data are still sufficient for assessing heterogeneity in overall dengue transmission intensity between different populations . However as demonstrated by the variable serotype specific λi estimated from the PRNT data , even within the same population , the dominant serotype in circulation changes over time [8 , 81 , 82] . Furthermore , we found that estimates of R0i varied between serotypes , suggesting serotypes ( or genotypes ) differ in their intrinsic transmissibility [40 , 74 , 82] . Therefore the assumption that all serotypes have identical λi required to estimate serotype-specific transmission intensity from IgG data must be regarded as a crude simplification . However , we found that non-serotype specific data does yield an estimate of the total force of infection from all serotypes consistent with the sum of serotype-specific forces of infection able to be derived from PRNT data , particularly when analysis of the latter allowed for inter-serotype interaction ( cross-protection or enhancement ) [8] . It is not possible to disentangle temporal from any age-dependent variation in exposure from single cross-sectional seroprevalence surveys , requiring broad assumptions to be made about such variation . Hence , for simplicity , we generally assumed constant transmission intensity over time when analysing single cross-sectional surveys . However , for Nicaragua [13 , 68] , data from multiple sequentially conducted serosurveys were available , so we were able to estimate time and age-dependent changes in the force of infection . We found evidence of long term variation in transmission intensity over a timescale of 1–12 years , and that exposure levels changed with age , with children aged 4 or older having twice the exposure of those under that age . We suspect that this may be associated with school attendence , with children spending more time away from home leading to an increase in exposure if the majority of transmission is occuring outside the domestic environment [72] . This school-cohort effect has also been observed in Sri Lanka , conversely with a decrease in exposure , where Tam et al . estimated an age-varying force of infection of 0 . 154 ( 95% CI: 0 . 132–0 . 177 ) for 0 . 5–6 year olds and 0 . 087 ( 95% CI: 0 . 020–0 . 154 ) for children aged 6 years and above also demonstrating the existence of different transmission environments [63] . Our analysis has a number of additional limitations . First , in translating force of infection estimates into estimates of R0i we rely on a model which assumes exposure is due to endemic transmission , meaning all resulting R0i estimates are by definition greater than one . Clearly this is less appropriate for settings with low seroprevalence such as Texas ( USA ) , where some or all of the seropositivity detected is due to imported cases rather than local transmission . Second , estimates of transmission intensity ( particularly R0i ) are sensitive to assumptions about cross-protective immunity between serotypes—and most notably the extent to which tertiary and quaternary infections contribute to transmission . While there is increasing evidence that tertiary and quaternary infections occur [10 , 82] , there is little quantitative data on the infectiousness of such infections relative to primary and secondary infections . Consistent with published theory [81] , our estimates of R0i were lower when we assumed tertiary and quaternary infections were as infectious as earlier infections ( Assumption 1 ) than when we assumed complete immunity was acquired after secondary infection ( Assumption 2 ) . When one serotype had a very large force of infection relative to the other three serotypes ( e . g . Haiti model 2: DENV-1 at 0 . 046 ( 95% CI: 0 . 010–0 . 179 ) compared to DENV-4 at 0 . 219 ( 95% CI: 0 . 088–0 . 445 ) , then regardless of the value of λi of the remaining serotypes , all R0i estimates were large and similar to each other . Thus it appears that the value of R0i is dominated by very large λi and changes in the other three λi play a minimal role . This uncertainty has relevance for planning interventions [8 , 11 , 83] , since R0 determines the coverage and effectiveness of vaccination or vector control measures required to achieve control of transmission [84] . The recent results from trials of the Sanofi live-attenuated chimeric vaccine [20 , 21] make this issue more pressing , since reliable estimates of transmission intensity—and of the health burden due to dengue—will be important in strategic planning and resource allocation for vaccination in different contexts . Third , while PRNT assays are currently the gold standard for routine dengue serotyping , cross-reactivity means care must be taken when interpreting the results of serosurveys in areas where there is co-circulation of different flaviviruses or routine use of yellow fever or Japanese Encephalitis vaccine [3] . Finally , our literature search highlighted that use of serological surveys as a tool to assess transmission remains rare for dengue , with publications of outbreak reports and notified case incidence data being much more common . Generally , published models estimating dengue transmission risk have therefore used notification data , the reliability of which therefore heavily depend on the quality of the surveillance system [85] . Gaining a better global picture of the variation in transmission will improve both estimates of the disease burden caused by dengue and assist in control planning . We would therefore advocate much more widespread and routine use of serological surveys as a surveillance tool which provides invaluable data for an immunising infection such as dengue . While PRNT data provides the maximum information , our study shows that even the much cheaper ELISA-based assays would provide reasonable baseline estimates of overall transmission intensity .
|
With an estimated 390 million infections each year , dengue imposes a significant global public health burden . Yet estimates of the intensity of dengue transmission in different settings are still sparse , making it difficult to plan efficient control programs . Since many dengue infections have no symptoms , cases reported through hospitals are only a small proportion of true cases . The authors used seroprevalence surveys which can detect all past infections to estimate dengue transmission intensity in 22 countries . Estimates derived from data collected using cheaper diagnostic tests were comparable to those making use of more expensive tests , an important conclusion for surveillance in resource constrained countries . We found dengue transmission intensity varied up to 4-fold in endemic settings , with estimates showing some sensitivity to how many dengue infections were assumed to confer complete immunity .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Estimating Dengue Transmission Intensity from Sero-Prevalence Surveys in Multiple Countries
|
Super-resolution microscopy recently revealed that , unlike the soma and dendrites , the axon membrane skeleton is structured as a series of actin rings connected by spectrin filaments that are held under tension . Currently , the structure-function relationship of the axonal structure is unclear . Here , we used atomic force microscopy ( AFM ) to show that the stiffness of the axon plasma membrane is significantly higher than the stiffnesses of dendrites and somata . To examine whether the structure of the axon plasma membrane determines its overall stiffness , we introduced a coarse-grain molecular dynamics model of the axon membrane skeleton that reproduces the structure identified by super-resolution microscopy . Our proposed computational model accurately simulates the median value of the Young’s modulus of the axon plasma membrane determined by atomic force microscopy . It also predicts that because the spectrin filaments are under entropic tension , the thermal random motion of the voltage-gated sodium channels ( Nav ) , which are bound to ankyrin particles , a critical axonal protein , is reduced compared to the thermal motion when spectrin filaments are held at equilibrium . Lastly , our model predicts that because spectrin filaments are under tension , any axonal injuries that lacerate spectrin filaments will likely lead to a permanent disruption of the membrane skeleton due to the inability of spectrin filaments to spontaneously form their initial under-tension configuration .
It is known for some time that microtubules and neurofilaments are the predominant structural filamentous proteins in the axon [1 , 2] . However , how these filaments are arranged to generate the structure of the axon plasma membrane skeleton was only very recently discovered [3] . Super-resolution fluorescence microscopy [4] revealed that the membrane skeleton of an unmyelinated axon consists of actin filaments , capped with adducin at one end , that form ring-like structures along the circumference of the axon . The actin rings are connected via spectrin tetramers oriented along the longitudinal direction of the axon ( Fig 1A ) . This cytoskeletal structure is extended across the entire axon . The distance between the periodic actin rings is approximately 180 to 190 nm [3 , 5 , 6] . Each spectrin tetramer is formed by the association of two identical heterodimers comprising an α-chain and a β-chain with 22 and 17-triple-helical segments , respectively [7] . In the distal axon , each heterodimer consists of two intertwined αII- and βII-spectrin chains running antiparallel to one another ( Figure A in S1 Fig ) . The axon initial segment ( AIS ) of mature neurons , however , appears to contain the subtype βIV-spectrin instead of βII-spectrin [5] . Spectrin tetramers are associated with the lipid bilayer [3] in a manner similar to that occurring at the red blood cell ( RBC ) membrane [7 , 8] ( Figure B in S1 Fig ) . In RBCs , ankyrin plays a major role in anchoring the lipid bilayer to the spectrin network by associating a spectrin filament with the anion exchanger integral membrane protein band-3 [9 , 10] ( Figure A in S1 Fig ) . Ankyrin binds to the middle of the spectrin tetramer , at the 15th repeat of β-spectrin near its carboxyl terminus [7 , 11] , and at the same time it binds to the cytoplasmic domain of band-3 [9] . In axons , the spatial distribution of ankyrin-G and ankyrin-B is highly periodic in the proximal and distal area of the axon , respectively [3 , 5] . Ankyrin , besides anchoring the spectrin network to the lipid bilayer , is critical for the organization of the axonal plasma membrane because it binds to several molecules . Voltage-gated Na ( Nav ) channels can bind to subdomains 3 and 4 of ankyrin [12] . Since ankyrin has a periodic pattern in the axon , the Nav channels also exhibit a periodic distribution pattern in the AIS alternating with the N terminus of βIV-spectrin [3] . The periodic structure of the membrane skeleton is thought to play an important role in the structural durability of the axon [3] . This conjecture comes from the similarity between the structural elements of the plasma membrane skeletons of the RBC and of the neuronal axon . However , we note that their geometrical arrangements are radically different resulting in hyperelastic flexibility for the RBC cytoskeleton and in reduced radial deformability and longitudinal extensibility for the axon . The two most important differences between the geometric configurations of the RBC and of the axon membrane skeletons are the following: first , the RBC cytoskeleton forms an approximately six-fold symmetric two-dimensional network ( Figure B in S1 Fig ) that behaves as an incompressible hyperelastic material [13 , 14] . In the axon , actin filaments form rings along the circumference , connected by spectrin filaments oriented along the axon . In this case , the membrane skeleton assumes the form of a two-dimensional cylindrically symmetric orthotropic network ( Fig 1 ) . The second important difference is that in the case of the RBC membrane , the end-to-end distance of the spectrin tetramers is ~ 75nm , which is close to the end-to-end distance of a free spectrin filament [15] . This suggests that the spectrin network in the RBC is near equilibrium . In the axon , however , the distance between the actin rings was reported to be approximately 180 to 190nm [3 , 6] . It is thought that microtubules stabilize the axon through interactions with neurofilaments , organelles [1 , 2 , 16] and possibly directly or indirectly with the actin rings , holding them apart at a specific distance . Since we do not know the exact configuration of actin rings , we assume that the upper limit of a junction of actin filaments and proteins promoting actin-spectrin binding is approximately 35 nm , which is close to the size of actin junctions in RBCs [17] . Thus , we consider that the end-to-end distance of the spectrin tetramers is approximately 150 nm while their contour length is approximately 200 nm [18 , 19] . This means that the spectrin filaments in the axon membrane skeleton are held under entropic tension suggesting that the flexibility of the network along the axon might be limited . However , because we do not know the exact thickness of the actin rings and consequently the end-to-end distance of the spectrin filaments , we also considered cases where the size of the junctions between actin and spectrin filaments were 25nm , 15nm , and 5nm . The diameter of an actin filament is approximately 8nm [20 , 21] . Based on the particular structure of the axon , we expect that its mechanical properties are different than the mechanical properties of soma and dendrites . Here , we used atomic force microscopy ( AFM ) to measure , via indentation , the stiffness of the plasma membrane of the subcompartments of cultured hippocampal pyramidal neurons . Importantly , we developed a coarse-grain molecular dynamics ( CGMD ) model for the membrane skeleton of the axon that comprises representation and connectivities of its main filaments . Based on the AFM measurements and on geometric and material parameters for the implemented filament models available in literature , we were able to examine the effect of the particular geometric configuration of the membrane skeleton and reproduce the stiffness of the axon plasma membrane . We note that while the model represents only the axon plasma membrane and considers all connectivities between the different components mostly as stable and not as dynamic processes , it provides a clear picture of how spectrin filaments in conjunction with actin rings contribute to the mechanical properties of the axonal membrane . We expect that the model will be used in studies of the mechanical stability of the axon , and generation and propagation of the action potential .
E18 hippocampal tissue obtained from BrainBits ( BrainBits , Springfield , IL ) was treated with trypsin and plated onto poly-D-lysine ( Sigma Aldrich , St . Louis , MO ) -coated glass bottom petri dishes ( Ted Pella , Redding , CA ) in neurobasal media ( Thermo Scientific , Waltham , MA ) supplemented with B27 ( Thermo Scientific , Waltham , MA ) , penicillin streptomycin ( Thermo Scientific , Waltham , MA ) and glutamax ( Thermo Scientific , Waltham , MA ) . The cells were maintained at 37°C in a humidified incubator with 5% CO2 until use . After 8–10 DIV , neurons were transfected with tau-gfp using Lipofectamine 2000 according to manufacturer directions ( Thermo Scientific , Waltham , MA ) . Tau-gfp was used to visualize axons in living neurons . Although tau-gfp tagged both axons and dendrites , axons were identified by their distinct morphology ( S3 Fig ) . Tau-gfp was a gift from Dr . Walikonis , Department of Physiology and Neurobiology , UCONN , Storrs . We obtained the Young’s moduli of the axon plasma membrane , dendrites , and soma via AFM indentation experiments with a maximum displacement of 200 nm . In these cases , the simple Hertz contact model of elastic half-space indentation cannot be used because cells do not behave elastically under large deformations and because of the geometric characteristics of dendrites and axon . Instead , we implemented a finite element model ( FEM ) to compute force-indentation ( F − d ) relationships and used them to obtain the Young’s moduli for soma , dendrites , and axon . The method and results are explained in detail in the S1 Text . Below , we briefly describe the FE approach . Large deformations of cell plasma membranes can be described reasonably well using the nearly incompressible neo-Hookean constitutive model [13 , 22 , 23] . We employ the isochoric deformation gradient F¯=J−1/3F , and similarly the isochoric right Cauchy-Green tensor C¯=J−2/3C , where C = FTF and J is the Jacobian , the determinant of the deformation gradient F . For the neo-Hookean model ψ=1c ( J−1 ) 2+μ2 ( I¯1−3 ) , where c = 6 ( 1−2ν ) /E = 2/κ , E is the initial Young’s modulus , ν is the Poisson’s ratio , and κ is the initial bulk modulus of the material . In the case of incompressibility , c degenerates to a nonphysical , positive penalty parameter used to enforce incompressibility . The parameter μ is the initial shear modulus , and I¯1=trC¯ is the first invariant of the isochoric right Cauchy-Green tensor . From the deformation gradient , we calculate Cauchy stress σ as σ=2JF∂ψ∂CFT . Applying the nearly incompressible neo-Hookean material model , we simulate indentation of both ( i ) a homogeneous isotropic rectangular cuboid and ( ii ) a homogeneous and isotropic thin-walled cylinder , by a conical indenter with a blunt tip using FE analyses in ANSYS workbench 14 . 0 ( Canonsburg , PA ) . We note that in the actual AFM experiments , the cantilever tip was of a pyramidal shape while in the FEM calculations we used a conical indenter with a blunt tip to avoid complications stemming from the pyramid edges . However , we show in the S1 Text that the F − d relationship valid for a pyramidal indenter , with a blunt tip with a semi-included angle of 20° and a tip radius of 20 nm , is very similar to the F − d relationship for a conical indenter with the same semi-included angle and tip radius ( S4 Fig ) . Because of this , we expect that the FEM results are suitable in the calculation of the Young’s moduli from the AFM indentation experiments as we explain in the section below . We carried out stiffness measurements on living rat hippocampal pyramidal neurons ( 16–18 DIV ) using AFM silicon nitride cantilevers with a nominal spring constant of 0 . 03 N/m ( MLCT , Bruker Probes , Camarillo , CA ) . Exact values for the cantilever spring constants were obtained via a thermal noise based method implemented by the manufacturer and were used in all calculations . Probes were of four-sided pyramidal shape with nominal tip radius of 20 nm and nominal semi-included angle of approximately 20° , as provided by the manufacturer . The tip was indented ~ 200 nm into the cell . Only ~ 100 nm of this indentation was used to determine the Young's modulus . The diameter of the axon at the measurement locations was approximately 1 μm . We note that in pyramidal neurons , microtubules , and neurofilaments are located at a distance greater than 200 nm from the axonal membrane [24 , 25] . Because the indentation depth in our experiments was approximately 100 nm , we do not expect that microtubules and neurofilaments will contribute to the measured axon plasma membrane stiffness . The same argument is true for dendrites since microtubules are located at more than 200 nm distance from dendritic plasma membrane [26] . For dendrite stiffness measurements , we tested areas where the dendrite diameter was larger than 2 μm . Measurements of soma stiffness were performed at different regions of the soma excluding the area over the nucleus . All measurements were performed in supplemented neurobasal media at 37°C . To measure the axon plasma membrane stiffness , we performed indentations at 16 x 16 points distributed uniformly in a 500 nm×500 nm area of the axon surface at a loading rate of 10 , 000 pN/s . For each measurement the cantilever displacement was calibrated at the rigid substrate next to the cell . Young's moduli E were calculated based on the force-indentation ( F − d ) relationship ( Eq S7 ) for a conical indenter with a blunt tip , with a semi-included angle θ = 20° and a tip radius of R = 20 nm , indenting ( up to 200 nm ) a neo-Hookean thin-walled cylinder of 1 μm diameter and of h = 10 nm wall-thickness . For the assessment of the plasma membrane stiffness of dendrites we followed the same approach as with the axon plasma membrane and used the same equation ( Eq S7 ) . In addition , the Young’s modulus of soma was calculated based on the ( F − d ) relationship ( Eq S5 ) . The soma was simulated as a nearly incompressible ( Poisson’s ratio ν ≃ 0 . 5 ) neo-Hookean rectangular 10 μm×10 μm×5 μm homogeneous and isotropic cuboid . In the S1 Text we show that the ( F − d ) for the thin-walled cylinder is F = ( 4 . 41×10−3 E ) d1 . 37 ( Eq S7 ) , and for a rectangular cuboid is F = ( 7 . 95×10−3 E ) d1 . 46 ( Eq S5 ) . We note that the above ( F − d ) relationships are valid when the indentation d is measured in nm , the initial Young’s modulus E in kPa , and the force F in pN . We used an open source program called force review automation environment ( FRAME ) [27] , developed by our lab , to determine the Young's moduli of the soma , dendrites and axon of hippocampal neurons . A value of the Young's modulus from each force-displacement curve was determined by fitting the theoretical curve for a pyramidal indenter with a blunt tip mentioned above . The resulting stiffness for the specific neuronal sub-compartment was determined as the median value of the probability distribution plot generated by the individual measurements of stiffness at each point . We tested n neurons from N samples . The median values were determined horizontally across all samples . We found that the soma has a Young's modulus of 0 . 7 ± 0 . 2 kPa ( N = 2 , n = 8 ) ( Fig 2A ) . The term frequency in Fig 2 corresponds to the percentage of measurements that gave a value within the corresponding bin range . The Young's modulus of dendrites was found to be 2 . 5 ± 0 . 7 kPa ( N = 2 , n = 8 ) ( Fig 2B ) . Importantly , the axon was the stiffest sub-compartment of the neuron exhibiting a median value of the Young’s modulus of 4 . 6 ± 1 . 5 kPa ( N = 2 , n = 8 ) . To evaluate if actin is critical for the observed high stiffness of the axon plasma membrane , we incubated neurons with Latrunculin B ( 20 μM for 1 hour ) , a compound that inhibits actin polymerization . This in turn , results in disruption of actin filaments . We found that in the presence of Latrunculin B the median value of the Young’s modulus was reduced to 2 . 2 ± 0 . 6 kPa ( N = 1 , n = 6 ) indicating that actin rings play a very significant role in determining the overall axon plasma membrane stiffness . We note that our results on the Young's modulus of soma and dendrites of rat hippocampal neurons are in agreement with published results [28 , 29] . However , to our knowledge , there are no published measurements of the stiffness of pyramidal neuron axon plasma membrane . It is clear that the stiffness of the axon is much higher than the stiffness of the soma and the stiffness of the dendrites . Below , we introduce a CGMD model for the axon and we then use it to understand how the axon plasma membrane skeleton structure leads to its elevated stiffness . The proposed model reflects the structure of the membrane skeleton of the proximal and distal unmyelinated axon as previously described [3 , 5] . The model is a representation of actin rings , oriented along the circumference of the axon , that are connected by spectrin tetramers tethered to the lipid bilayer at their middle section ( Fig 1 ) . A spectrin tetramer consists of two identical , intertwined , head-to-head associated heterodimers [18] . Each heterodimer is comprised of an α-spectrin and a β-spectrin chain consisting of 22 and 19 homologous triple helical repeats , respectively [7] . In our model , a spectrin tetramer is represented as a single chain of 41 spherical beads ( gray particles in Figure A in S2 Fig ) connected by 40 harmonic springs . The solid red line in Figure B in S2 Fig reflects the spring potential , USS ( r ) =1/2k0 ( r-reqSS ) 2 , where r is the distance between two consecutive spectrin particles , reqSS=Lc/40=5nm is the equilibrium distance between the spectrin particles ( close to the size of the spectrin repeats ) , Lc ≃ 200nm is the contour length of the spectrin tetramers , and k0 is the spring constant ( defined below ) . All spectrin particles interact via the repulsive Lennard-Jones ( L-J ) potential: UrepSS ( rij ) ={4ε1[ ( σrij ) 12− ( σrij ) 6]+ε1rij<Rcut , LJ=reqSS0rij>Rcut , LJ=reqSS , ( 1 ) where ε1 = ( 1/16 ) ε with ε being the energy unit , σ the length unit , and rij the distance between spectrin particles . The value ε1 = ( 1/16 ) ε gives a curvature at the equilibrium equal to the spring constant k0 . Setting the diameter of the spectrin particles equal to the equilibrium distance of the L-J potential reqSS=21/6σ=5nm , the length scale is σ = 4 . 45 nm . We discuss the energy scale in the section below related to modeling the plasma membrane network . We chose the cutoff distance of the potential Rcut , LJ to be the equilibrium distance reqSS between two spectrin particles . The potential is plotted as a dashed red line in Figure B in S2 Fig . We note that in order to reduce the number of free parameters of the model the spring constant was set to k0 = 3 . 56 ε/σ2 which is identical to the curvature at equilibrium of the L-J potential used in actin-spectrin interaction ( see the section on Modeling the axon plasma membrane network ) . We computed the end-to-end distance 〈ree2〉1/2 of the spectrin chain model for KBT/ε = 0 . 03 , where KB is the Boltzmann’s constant . We first equilibrated the filament for 105 time steps , and then measured the end-to-end distance for 3×106 time steps during its thermal fluctuations . The recorded distances follow a Gaussian distribution P ( ree ) =1/ ( λ2π ) exp[− ( ree−〈ree〉 ) 2/2λ2] , where λ=〈 ( ree−〈ree〉 ) 2〉 , and with a mean value of 〈ree2〉1/2=74 . 3nm ( S14 Fig ) . For flexible filaments with lp << Lc , the end-to-end distance is correlated with persistence length and contour length via the expression 〈ree2〉1/2≅2lpLc . Taking into consideration that the spectrin contour length is approximately 200 nm [18 , 19] , we calculated the persistence length to be 13 . 8nm . This result is close to experimentally reported values of approximately 20 nm [30] and 10 nm [31] . The actin rings consist of short actin filaments arranged along the circumference of the axon [3] . The exact configuration of the aligned actin filaments and how they are connected to form the actin rings is not known . It has however been shown that adducin is present in the actin rings [3] probably capping and stabilizing the plus end of actin filaments . We then expect that the minus end of F-actin is stabilized by another protein , probably tropomodulin [21] , while additional cross-linking proteins are possibly involved in the formation of the actin rings [21 , 32 , 33] ( Fig 1A ) . Because the exact molecular structure of the actin rings and whether actin filaments are connected in a side-by-side or an end-to-end arrangement is not known , we adopted a coarse-grain particle model that produces stable actin rings but ignores their specific molecular structure . In this particle model , an actin ring is represented as a collection of 39 beads ( red particles in Fig 1B and insert and in Figure A in S2 Fig ) with a diameter of approximately 35 nm . These beads form a circle with a diameter of approximately 434 nm , which lies within the range of experimental results [3 , 5 , 34] . We chose the diameter of the actin particles to be 35 nm based on values for the RBC membrane skeleton , which comprises short actin oligomers ( consisting of approximately 13 to 15 subunits ) with a length of 33 ± 5 nm [17 , 35 , 36] . Two adjacent actin particles in the same ring connect via a spring potential UAA=1/2kA ( r−reqAA ) 2 , with equilibrium distance reqAA=35nm , and a repulsive L-J potential UrepAA ( S1 Table ) , with Rcut , LJ=reqAA ( shown as purple lines in Figure B in S2 Fig ) . The value of the spring constant kA = 38 . 0 ε/σ2 is determined in computational results in conjunction with the AFM stiffness measurement of the axon plasma membrane . In addition , we employed a finitely deformable nonlinear bending potential that behaves as a finitely extendable nonlinear elastic ( FENE ) potential to maintain the circular shape of the actin rings . The potential has the form Ub=−12kbΔθmaxln[1− ( θ−θ0Δθmax ) 2] , where kb = 3 , 500 KBT is the parameter that directly regulates the bending stiffness of the actin filament and it is determined in S1 Text . θ is the angle formed by three consecutive particles of the same ring . θ0=180° ( 39−2 ) 39=170 . 77° is the equilibrium angle and Δθmax = 0 . 3θ0 is the maximum allowed bending angle . We note that the resistance of the actin ring to small deformations depends on kb/Δθmax since for small deformations the FENE potential is approximated by Ub=12 ( kb/Δθmax ) ( θ−θ0 ) 2 , which corresponds to a harmonic potential with a spring constant kbs = kb/Δθmax . This means that the exact value of Δθmax does not uniquely determines the stiffness of the structure close to equilibrium but in combination with kb . Δθmax defines the maximum deformation of the actin rings but its exact value does not affect the behavior of the system near equilibrium . The employed value of kb produces a bending rigidity of κbend = 7 . 1 × 10−26 Nm2 for a straight stiff filament based on numerical calculations shown in S1 Text and in [37] . The obtained value is similar to the experimentally measured bending rigidity of actin filaments 7 . 3 × 10−26 Nm2 reported in [38 , 39] . To build a mechanically stable network , we first connected each spectrin filament at its two ends to actin particles belonging to consecutive actin rings via a breakable L-J potential ULJAS ( rij ) =4ε[ ( 4σ/rij ) 12− ( 4σ/rij ) 6] , where rij is the distance between actin and spectrin particles ( blue dashed line in Figure B in S2 Fig ) . The equilibrium distance between actin and spectrin is 21/6 ( 4σ ) ≃ 20 nm resulting to an actin junction size of approximately 40 nm [40] . This equilibrium distance corresponds to an end-to-end distance of 145 nm for the spectrin filaments . However , because the exact thickness of the actin rings and consequently the equilibrium distance between actin and spectrin particles are not known , we also considered the cases where reqAS=15nm , 10nm , and5nm ( approximately the diameter of a G-actin monomer [20] ) , which correspond to end-to-end distance of ree = 155nm , 165nm , and 175nm respectively for the spectrin filaments . We note that in this model the actin-spectrin association can break and reform . The association breaks , by setting the attractive force to zero , when the distance between the particles crosses the inflexion point of the L-J potential at rinflexion = ( 26/7 ) 1/6 4σ , indicated by the red circle ( blue dashed line in Figure B in S2 Fig ) . It can reform as the distance between actin and spectrin particles becomes smaller than the capture distance of 2 . 5×4σ . For a stable membrane skeleton , the spectrin-actin junction association energy was chosen to be ε ≃ kBT/0 . 03 ≃ 0 . 86 eV for T = 300°K . Equilibrium measurements have shown that the association energy for the spectrin-actin-protein 4 . 1 complex in normal RBCs is about 17 Kcal/mole = 0 . 74eV [41] . However , for this value the membrane skeleton would be partially broken when the distance between actin rings is set equal to the experimental value of 185 nm . This means that the association energy of the spectrin-actin complex in the axon is most likely larger than in normal RBCs , to maintain stable membrane skeleton with spectrin filaments under tension . Microtubules and neurofilaments are thought to play an important role in maintaining the polarity and structure of the AIS through interactions with the axonal cytoskeleton . Pyramidal neurons , which we used to experimentally determine the axonal membrane stiffness , have microtubules that are not structured in bundles but rather like a string of beads ( in cross-section ) [24 , 25 , 42] and they are at a distance greater than 200 nm from the axonal membrane [25] . Similarly , neurofilaments in pyramidal neurons are not arranged in bundles and are much farther away from the axonal membrane than microtubules [24] . Since the indentation depth of the AFM probe was ~ 100–150nm , microtubules and neurofilaments are not expected to contribute to axonal membrane stiffness in our measurements . In our model , the effect of microtubules and neurofilaments on the structural integrity of the axon was implemented implicitly . We considered that microtubules interact with actin to maintain the equilibrium distance of actin rings at 185 nm . To achieve this , we applied the FENE potential Umt=−12kmtΔdmaxln[1− ( d−deqRRΔdmax ) 2] on all actin particles belonging to consecutive rings . kmt is the parameter that determines the stiffness of the nonlinear spring between two actin rings , d and deqRR=185nm are the distance and the equilibrium distance between the centers of the two actin rings , respectively [3 , 5] , and Δdmax=0 . 3deqRR is the maximum allowed deformation . The position of the center of each ring is calculated by utilizing the mean value of the z–coordinate of the actin particles . The choice of kmt is justified based on the following rationale: for small deformations , the FENE potential is approximated by Umt=12 ( kmt/Δdmax ) ( d−deqRR ) 2 , which corresponds to a harmonic potential with a spring constant ksp = kmt/Δdmax [37] . In this case , we can assume that τ = ELh , where τ is the stress , EL is the longitudinal Young’s modulus of the axon , and h is the strain . The final equation is F/A = EL ( ΔL/L ) , where F=ksptΔL is the force applied on the cross-section of the axon A = πR2 , where kspt=ksp/ ( N−1 ) is the spring constant for the total axon , R = 217 nm is the radius of the axon , ΔL is the elongation of the axon , L= ( N−1 ) deqRR is the length of the axis , and N is the number of springs . Combining the equations above , we determined that ksp=ELπR2/deqRR and consequently kmt = ksp Δdmax = 0 . 3ELπR2 . A reasonable value for the axon's longitudinal Young's modulus is EL ≃ 10 kPa [43] , resulting in kmt≃477KBT/σ≃19 , 822KBT/deqRR , at T = 300°K . The final aspect of the model is the association between the axon membrane skeleton and the lipid bilayer . In RBCs , the membrane skeleton is anchored to the lipid bilayer via glycophorin at the actin junction complexes and via the integral membrane protein band-3 and ankyrin at the middle section of spectrin tetramers [9 , 10] ( S1 Fig ) . Regarding the association between a spectrin filament and the lipid bilayer in RBCs , ankyrin binds at the 15th repeat of β-spectrin near its carboxyl terminus , at the middle section of the spectrin tetramer [7] . At the same time , it binds to the cytoplasmic domain of band-3 [9] , mediating the anchoring of spectrin filaments to the lipid bilayer . In the case of the axon , we considered the following experimental findings: ( i ) The spatial distribution of ankyrin-G is highly periodic in the proximal area of the axon , while ankyrin-B also exhibits a periodic pattern in distal axons [3 , 5] , ( ii ) Nav channels exhibit a periodic distribution pattern in the AIS alternating with actin rings [3] , ( iii ) Nav can bind to subdomains 3 and 4 of ankyrin [12] , and ( iv ) Ankyrin-G and sodium channels are in 1:1 molar ratio in the brain . Based on these findings and on the fact that ankyrin binds near the carboxyl terminus of β-spectrin it is reasonable to assume that Nav channels are arranged in a periodic pattern along the axon via their association with ankyrin in a manner similar to band-3 association with spectrin in the RBC membrane . We also note that by assigning one Nav channel per ankyrin molecule , and consequently per spectrin tetramer , the Nav channel density is approximately 150 channels per μm2 , which lies within the range of 110 to 300 channels per μm2 in AIS [44] . To represent the anchoring of spectrin tetramers to the lipid bilayer , we used the following approach: We connected an ankyrin particle ( depicted as a green particle in Fig 1B and Figure A in S2 Fig ) to the 20th particle of the spectrin filament by the spring potential USK ( rij ) =1/2k0 ( rij−reqSK ) 2 , where the radial equilibrium distance is reqSK=15nm , ( black solid line in Figure B in S2 Fig ) . This distance corresponds to the radius of a spectrin particle ( 2 . 5 nm ) and the effective radius of the cytoplasmic domain of the ankyrin complex connected to an Nav channel ( ~ 12 . 5 nm ) [45] . We also implemented a repulsive L-J potential UrepSK ( S1 Table ) , with Rcut , LJ=reqSK ( dashed black line in Figure B in S2 Fig ) to simulate a steric repulsion between the particles that represent spectrin and ankyrin . For simplicity , we did not use a representation of the lipid bilayer in this model . Instead , we used a spring potential to represent the confinement applied on the motion of ankyrin particles and spectrin filaments by the lipid bilayer . The harmonic confining potential is given by UC ( r ) = 1/2kc ( r − r0 ) 2 , where r is the radial distance of spectrin and ankyrin particles from the central axis of the axon , and r0 is the equilibrium distance from the central axis . We considered r0 to be 217 nm and 232 nm for the spectrin and ankyrin particles , respectively . Because the ankyrin particles are attached to the bilayer , the confinement potential acts on both radial directions ( inwards and outwards ) . However , only the outward motion of the spectrin particles is confined , since the spectrin filament cannot cross the lipid bilayer . In contrast , the inward motion will not face additional constrain . The confinement mild stiffness in this model is arbitrarily chosen to be kc = 0 . 1k0 since it is due to the bending rigidity of the lipid bilayer which is in the range of ( 10–20KBT ) . We finally note that an important consideration in RBC membrane modeling is that mutations can cause disruption of the association between ankyrin and spectrin resulting in stiffer skeleton , local membrane instabilities , and vesiculation [10 , 46–48] . Here , we opted to focus on establishing the membrane model and explore possible membrane skeleton defects and their effect on the axon plasma membrane in a later work . The configuration used in this paper consists of N = 16 , 029 particles , corresponding to an axon length of approximately 1 . 85 μm . The numerical integrations of the equations of motion are performed using the Beeman algorithm . The temperature of the system is maintained at KBT/ε = 0 . 03 by employing the Berendsen’s thermostat [49] , where KB is Boltzmann’s constant and T is the temperature . The model is implemented in the NVT ensemble ( constant number of particles N , constant volume V , and constant temperature T ) . The time scale is ts=mσ2/ε , the time step is dt = 0 . 01ts , and m is the unit mass of the spectrin particles . We selected the temperature to render the conformation time of the spectrin filaments close to expected theoretical values [50] . We gradually brought the model to the equilibrium length and temperature , and then equilibrated it for 15×104 time steps . We performed the measurements during a period of 10×106 time steps after equilibration .
To measure the stiffness of the simulated axon plasma membrane skeleton , we defined a cylindrical Lennard-Jones repulsive potential ULJS ( rij ) =4ε[ ( σ/ ( ri− ( rc−rS ) ) ) 12− ( σ/ ( ri− ( rc−rS ) ) ) 6] , for the spectrin particles and ULJA ( rij ) =4ε[ ( 7σ/ ( ri− ( rc−rA ) ) ) 12− ( 7σ/ ( ri− ( rc−rA ) ) ) 6] , for the actin particles . rc is the distance between the center-line of the axon and the surface of an imaginary cylinder ( Fig 3A ) , ri is the distance between particle i and the center-line of the axon , rS is the radius of the spectrin particle and rA is the radius of the actin particle . The cutoff distances of the L-J potentials are rc + rS and rc + rA for ULJS and ULJA respectively . Then , we gradually expanded the cylindrical potential to apply internal radial pressure to the membrane skeleton of the axon . The total increase of the radius was 10 nm , from 217 nm to 227 nm , which corresponds to approximately 4 . 6% of the initial radius of the axon . An axon , because of its structure , has two well separated areas in terms of lateral stiffness . We expect that the stiffness is lower at the middle region between the actin rings and higher at the region near the actin rings . The radius of an actin particle is rA = 17 . 5 nm and the radius of a spectrin particle is rS = 2 . 5 nm . The capture radius of the actin-spectrin junction is 2 . 5 ( rA + rS ) /21/6 ≃ 44 . 5 nm . Based on this calculation , we chose the width of the stripes at the actin rings ( red stripes ) to be 80 nm and consequently the width of the stripes between the two actin rings ( green stripes ) is 105 nm since the distance between two consecutive actin rings is 185 nm . First , we computed the repulsive Lennard-Jones forces applied to all particles belonging to the green stripes located between consecutive actin rings at each expansion step . The total computed force was divided by the total area of all green stripes to estimate the applied pressure . We measured the pressure every 0 . 5 nm expansion increment . The transition from one radius , where we measured the pressure , to the next one lasted 1000 time steps . At each measurement , we first equilibrated the system for 1000 steps and then computed forces for 8000 time steps . The pressure values were plotted in a histogram that was approximated as a Gaussian distribution ( S16 Fig ( green ) ) . After completion of the entire deformation , we plotted the mean values along with the standard deviations as function of the expansion ( Fig 3B ( green ) ) . We found that the relation between pressure and the change in the radius was linear and we used the least square method to compute the slope of the fitted straight line . Using the linear elastic cylindrical shell theory , we correlated the slope with the corresponding Young's modulus E via the expression E = pR2 / δH , where p is the applied pressure , R is the radius of the shell , H is the thickness of the shell , and δ is the radius change . By using this equation and assuming that the thickness of the axonal membrane skeleton is H = 10 nm , we obtained the stiffness of the membrane at the area between the actin rings to be approximately E = 7 . 22 × 10−4 ε/σ3 which corresponds to E = 1 . 13 kPa . This result is lower than the experimentally measured axon plasma membrane stiffness when the axon was treated with Latrunculin B , which disrupts actin filaments ( Fig 2D ) . We also note that our model does not have free parameters for this section of the axon since the main skeleton filaments that resist deformation are the spectrin filaments , for which the persistence length , geometric configuration , and connectivity to actin are known . While the material parameters of the spectrin filaments and their geometric configuration are known , how the G-actin filaments are connected to form the actin rings is unknown . Here , we assume that G-actin filaments , represented by one particle , are connected to each other to form one-particle thick rings . In our model , we use a spring harmonic potential to maintain the equilibrium distance between two consecutive particles and a bending FENE potential to maintain the included angle between two consecutive bonds formed between three particles . The spring potential resists changes to the radius of the actin rings while the bending potential resists to changes of the circular shape of the actin rings . The spring constant kA is determined below . To measure the axon plasma membrane stiffness in the area near the actin rings , we repeated the same procedure which we followed to measure the stiffness in the area between the actin rings . In particular , we measured the applied pressure to stripes of 80 nm width located over the actin rings ( red stripes in Fig 3A ) . After reiteration , we found that by employing a spring constant of kA = 38 . 0 ε/σ2 the Young’s modulus is approximately E = 53 × 10−4 ε/σ3 . This corresponds approximately to E = 8 . 3 KPa . We note that the actin spring constant corresponds to approximately kA ≃ 0 . 26 N/m which is larger than the value used in previous actin filament simulations [33] . The difference is perhaps due to an enhanced connectivity between the actin filaments that form the actin ring . We finally note that by measuring the pressure applied to the entire axon in relation to the increase of the radius ( Fig 3B , black ) and then employing the linear shell theory , we determined that the average axon plasma membrane Young's modulus is approximately E = 27 × 10−4 ε/σ3 . This corresponds approximately to E = 4 . 23 KPa . Therefore , we conclude that the experimentally determined E depends on both the actin rings and spectrin filaments , with actin sustaining almost six times the applied load compared to spectrin during volumetric expansion . Ankyrin proteins , depicted as green particles in Fig 1B , are connected to spectrin by a harmonic potential . Because the persistence length ( lp ) of a spectrin filament is typically considered to be between 10 nm and 20 nm [30 , 31] , and its contour length ( Lc ) is approximately 200nm , we find , based on the equation 〈ree2〉1/2≅2lpLc , that the end-to-end distance of a spectrin filament at equilibrium ranges from 63nm to 89 . 5nm . Spectrin tetramers of the axon membrane skeleton have an end-to-end distance of approximately 150nm; therefore , they are under tension with a reduced range of thermal motion . To demonstrate this , we equilibrated the model for 15×104 time steps at constant volume and temperature ( Fig 4A ) and recorded the thermal motion of ankyrin particles for 10×106 time steps once every 104 time steps . We note that after 105 time steps the size of the area described by the ankyrin particles hardly changes . The trajectory of ankyrin particles outlined an area with an average radius of ~ 5 . 37 nm . As Fig 4A shows , the ankyrin particles and consequently the connected Nav channels maintained an ordered configuration , in contrast to simulations with spectrin not under tension ( Fig 4B; equilibrium end-to-end distance of 75 nm ) . To clearly distinguish between the two cases , we plotted the distribution of the ratios d ( z ) /Lc , where d ( z ) is the deviation of an ankyrin point from its mean position during its thermal motion along the z-direction and Lc is the mean distance between two consecutive ankyrin points along the z-direction when the spectrin is under tension ( Fig 4C , Lc = 185 . 78 nm ) and when the spectrin is almost at equilibrium ( Fig 4D , Lc = 112 . 32 nm ) . The distribution in Fig 4D is much wider ( standard deviation s = 0 . 091 ) than the distribution in Fig 4C ( s = 0 . 027 ) . As a consequence , when spectrin is under tension the positions of consecutive Nav channels along the axon ( z-direction ) are more ordered ( Fig 4A and 4C ) than when spectrin is at equilibrium ( Fig 4B and 4D ) . We note that previous work has shown that increasing mobility of sodium channels in the AIS by inhibiting actin polymerization alters action potential properties [51] . Therefore , we conjecture that if the spectrin filaments were at near equilibrium , their thermal motion could affect the generation and propagation of a synchronized action potential . We finally note that , as in the case of the RBC membrane [48 , 52–55] , the axonal membrane skeleton is expected to confine the lateral diffusion of channels that are not connected to the membrane cortex within the rectangular “fenced” area between two consecutive actin rings and two neighboring spectrin filaments . Because in the axon the spectrin filaments are under tension with reduced oscillation amplitudes , it is anticipated that escape of diffusing channels via “hop movements” from one compartment to another will be limited compared to the RBC membrane where spectrin filaments are not under tension and the network is not perfect . Spectrin filaments in quiescent normal RBCs are dynamically connected to actin junctions . It is known that ATP-driven dissociation of spectrin filaments from actin junctions [56] results in softening of the RBC membrane [57] and allows reconfiguration of the spectrin network when a spectrin-actin association is momentarily disrupted [58] . Within this framework , we explored if re-association between spectrin filaments and actin junctions is possible in the axon membrane skeleton purely from a mechanics point of view . This is important since inability of spectrin-actin re-association would mean that laceration of the spectrin filaments due to injury will result in a permanent damage of the axon . To examine this question , we considered our axon model where the distance between actin rings is 185 nm and 15 of the 39 spectrin filaments corresponding to each actin ring were severed . We then let the system evolve and reach equilibrium in 104 time steps . After that , we allowed re-connection between severed spectrin filaments and the corresponding actin junctions for the next 104 time steps . The capture radius was set at rcapture = 2 . 5× ( 4σ ) , equal to the cutoff distance of the L-J potential ULJAS between actin and spectrin particles . We observed that none of the disconnected filaments were connected back to its original junction ( Fig 5 ) . This is expected because spectrin filaments were initially under entropic tension and shrunk to their end-to-end distance at equilibrium when they were cut . However , when we considered an axon configuration with only 110 nm distance between actin rings , which approximately corresponds to the end-to-end distance of spectrin filaments at equilibrium , then 85% of the 15 severed filaments re-connected to their original junction in 10 , 000 time steps and 100% in 50 , 000 time steps . This result clearly demonstrates our theoretical prediction that it is very unlikely for a normal axon , where the distance between actin rings is approximately 185 nm , to recover its initial membrane skeleton configuration if spectrin filaments were at some point injured .
We performed AFM experiments to measure the stiffness of the plasma membrane of the soma , dendrites and the axon of rat hippocampal pyramidal neurons . We found that the axon is much stiffer than the soma and dendrites . To understand the mechanical properties of the axon , we introduced a coarse-grain molecular dynamics model for the axon membrane skeleton of non-myelinated neurons . We found that the axon plasma membrane has two distinct Young's moduli that are correlated with its geometric structure , which is characterized by stiff actin rings connected by extended spectrin filaments oriented along the axon . We showed that the model , without using free parameters , predicts a low Young’s modulus , in the region between the actin ring . By using a spring constant for the actin filaments , similar to the one usually employed in actin filament simulations , we found a higher Young’s modulus in the region near to the actin rings . The average value of these measurements agrees with the median value of the AFM measurements of the axon plasma membrane stiffness . In addition , we showed that because the spectrin filaments are under entropic tension , they limit the thermal motion of the attached ankyrin proteins and consequently the thermal fluctuations of the ankyrin-associated sodium channels maintaining them in a ring-like configuration . This may have an effect on the initiation and the rate-of-rise of the action potential . We also note that because spectrin filaments are under tension , axonal injuries that lacerate spectrin filaments will lead to a permanent disruption of the membrane skeleton because of the inability of spectrin filaments to spontaneously connect back to their initial , under-tension configuration .
|
Super-resolution microscopy has suggested that the actin cytoskeleton structure differ between various neuronal subcompartments . To determine the possible implication of the differing actin cytoskeleton structure , we determined the stiffness of the plasma membrane of neuronal subcompartments using atomic force microscopy ( AFM ) . We found that axons are almost ~6 fold stiffer than the soma and ~2 fold stiffer than dendrites . By using a particle-based model for the surface membrane skeleton of the axon that comprises actin rings connected with spring filaments to represent the axonal structure , we show that regions neighboring actin rings are stiffer than areas between these rings . In these in between sub-regions , the spectrin filaments determine stiffness . Our modeling also shows that because the spectrin filaments are under tension , the thermal jitter of the actin-associated ankyrin particles , connected to the middle area of spectrin filaments , is minimal . As a result , we propose that the sodium channels bound to ankyrin particles will maintain an ordered distribution along the axon . We also predict that laceration of the spectrin filaments due to injury will cause a permanent damage to the axon since spontaneous repair of the spectrin network is not possible as spectrin filaments are under entropic tension .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"models",
"Results",
"and",
"discussion",
"Conclusion"
] |
[
"stiffness",
"mechanical",
"properties",
"cell",
"motility",
"medicine",
"and",
"health",
"sciences",
"actin",
"filaments",
"membrane",
"potential",
"electrophysiology",
"neuroscience",
"spectrins",
"materials",
"science",
"nerve",
"fibers",
"cellular",
"structures",
"and",
"organelles",
"neuronal",
"dendrites",
"animal",
"cells",
"axons",
"proteins",
"cell",
"membranes",
"biochemistry",
"cytoskeletal",
"proteins",
"cellular",
"neuroscience",
"cell",
"biology",
"ankyrins",
"physiology",
"neurons",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"material",
"properties"
] |
2017
|
Modeling of the axon membrane skeleton structure and implications for its mechanical properties
|
In Brazil , dengue has been a major public health problem since its introduction in the 1980s . Phylogenetic studies constitute a valuable tool to monitor the introduction and spread of viruses as well as to predict the potential epidemiological consequences of such events . Aiming to perform the molecular characterization and phylogenetic analysis of DENV-2 during twenty years of viral activity in the country , viral strains isolated from patients presenting different disease manifestations ( n = 34 ) , representing six states of the country , from 1990 to 2010 , were sequenced . Partial genome sequencing ( genes C/prM/M/E ) was performed in 25 DENV-2 strains and full-length genome sequencing ( coding region ) was performed in 9 strains . The percentage of similarity among the DENV-2 strains in this study and reference strains available in Genbank identified two groups epidemiologically distinct: one represented by strains isolated from 1990 to 2003 and one from strains isolated from 2007 to 2010 . No consistent differences were observed on the E gene from strains isolated from cases with different clinical manifestations analyzed , suggesting that if the disease severity has a genetic origin , it is not only due to the differences observed on the E gene . The results obtained by the DENV-2 full-length genome sequencing did not point out consistent differences related to a more severe disease either . The analysis based on the partial and/or complete genome sequencing has characterized the Brazilian DENV-2 strains as belonging to the Southeast Asian genotype , however a distinction of two Lineages within this genotype has been identified . It was established that strains circulating prior DENV-2 emergence ( 1990–2003 ) belong to Southeast Asian genotype , Lineage I and strains isolated after DENV-2 emergence in 2007 belong to Southeast Asian genotype , Lineage II . Furthermore , all DENV-2 strains analyzed presented an asparagine ( N ) in E390 , previously identified as a probable genetic marker of virulence observed in DHF strains from Asian origin . The percentage of identity of the latter with the Dominican Republic strain isolated in 2001 combined to the percentage of divergence with the strains first introduced in the country in the 1990s suggests that those viruses did not evolve locally but were due to a new viral Lineage introduction in the country from the Caribbean .
Dengue viruses ( DENV ) are the most important human arboviruses worldwide , transmitted by mosquitoes of the genus Aedes , Aedes aegypti is the main vector . Explosive epidemics have become a public health problem , economic impact , socially and politically significant [1] , [2] . Currently it is estimated that 70 to 500 millions dengue infections occur annually in 124 endemic countries . Nearly 3 . 6 billion people ( 55% of world population ) are at risk of contracting the disease ( DVI ) . The rapid global spread of DENV in the last 50 years resulted in the dispersal of genotypes associated with increased severity [3] . The four serotypes ( DENV-1 , DENV-2 , DENV-3 and DENV-4 ) are closely related yet antigenically distinct and contain a positive-sense RNA genome that is translated as a single polyprotein and post-translationally cleaved into three structural proteins , capsid ( C ) , premembrane ( prM ) and envelope ( E ) , and seven nonstructural proteins , NS1 , NS2A , NS2B , NS3 , NS4A , NS4B and NS5 . The RNA genome is packaged in an icosahedral capsid , and the nucleocapsid is surrounded by a lipid bilayer containing the E and M proteins [4] , [5] . DENV infection causes a spectrum of clinical disease ranging from an acute debilitating , self-limited febrile illness - dengue fever ( DF ) - to a life-threatening syndrome - dengue hemorrhagic fever/dengue shock syndrome ( DHF/DSS ) [6] . Despite the similar disease manifestations , the DENV are genetically diverse with approximately 40% of amino acid sequence divergence . Distinct DENV genotypes can be characterized when the genetic divergence are higher than to 6% [7] . A recent analysis of 1 , 827 complete E gene sequences supported the existence of six genotypes for DENV-2: Asian genotype I , Asian genotype II , Southeast Asian/American genotype , Cosmopolitan genotype , American genotype and the Sylvatic genotype , the most genetically distinct genotype . Furthermore , the Southeast Asian/American genotype's topologies suggested a spatial division of this genotype into two major subclades [8] . In the Americas , the first DHF epidemics in the 80's were due to the introduction of the Southeast Asian/American genotype which replaced the American genotype and more severe cases with higher viremia were reported [9]–[11] . In Brazil , the disease has become a public health problem with explosive epidemics after the introduction of DENV-1 in 1986 in Rio de Janeiro [12] . However , the first DHF/DSS cases were only reported after the DENV-2 introduction in 1990 in the country [13] , [14] . From 1990 until the 26th epidemiological week of 2010 , a total of 5 , 481 , 921 cases , including 17 , 203 cases of dengue hemorrhagic fever ( DHF ) and 1954 deaths were reported in the country [15] . Aiming to perform the phylogeny of the DENV-2 and its impact in the disease severity during 20 years of viral activity in Brazil , strains isolated from DF , DHF/DSS and fatal cases occurred since its introduction in 1990 until 2010 , were analyzed . In this scenario , the partial sequencing ( C/prM/M/E genes ) of 25 DENV-2 strains was performed . To determine whether the evolutionary relationships observed for the C/prM/M/E genes were applicable to the complete genome , we further fully sequenced the coding regions of nine DENV-2 strains . In order to avoid mutations introduced by in vitro passages of the virus in cell cultures we used DENV-2 strains extracted directly from serum or originally isolated from cell cultures .
The strains analyzed in this study belong to a previously-gathered collection from the Laboratory of Flavivirus , IOC/FIOCRUZ , Rio de Janeiro , Brazil , obtained from human serum through the passive surveillance system performed by the Laboratory from an ongoing Project approved by resolution number CSN196/96 from the Oswaldo Cruz Foundation Ethical Committee in Research ( CEP 274/05 ) , Ministry of Health-Brazil . Samples were chosen anonymously , based on the laboratorial results and clinical manifestations input on the Laboratory database . Viral strains consisted of DENV- 2 ( n = 34 ) isolated during epidemics occurred from 1990 to 2010 in six states in Brazil ( Table 1 ) . Each sample was accompanied by identification form containing clinical and epidemiological data . All strains were determined as DENV-2 serotype by reverse transcriptase polymerase chain reaction ( RT-PCR ) and or/virus isolation from DF ( n = 19 ) , DHF ( n = 3 ) , DSS ( n = 1 ) and fatal cases ( n = 4; 1 from DF , 2 from DHF and 1 with no classification available ) . Seven cases were not classified due to data unavailability . Viral RNA was extracted from infected cell culture supernatant or directly from the patients' serum using QIAamp Viral RNA Mini kit ( Qiagen ) following the manufacturer's instructions and stored at −70°C for DENV typing and sequencing . RT—PCR for detecting and typing DENV was performed as described previously [16] . Briefly , consensus primers were used to anneal to any of the four DENV types and amplify a 511-bp product in a reverse transcriptase-polymerase reaction . A cDNA copy of a portion of the viral genome was produced in a reverse transcriptase reaction . After a second round of amplification ( nested PCR ) with type-specific primers , DNA products of unique size for DENV-2 ( 119 bp ) were generated . Virus isolation was performed by inoculation into C6/36 Aedes albopictus cell line [17] and isolates were identified by indirect fluorescent antibody test ( IFAT ) using serotype-specific monoclonal antibodies [18] . Briefly , patients' sera were inoculated into C6/36 Aedes albopictus cell monolayers in L-15 Medium ( Leibovitz , Sigma ) supplemented with 2% fetal calf serum ( FCS , Invitrogen ) and 0 . 2 mM of nonessential amino acids ( Invitrogen ) . Cells were incubated at 28°C for 5 to 7 days and observed for cytopathic effects . Infected supernatant was clarified by centrifugation and virus stocks stored in 1-mL aliquots at −70°C until use . Reverse transcription ( RT ) was performed using 5 µL of extracted RNA in 25 µL of AccessQuick RT-PCR System ( Promega Corporation ) and specific oligonucleotides primers ( Table 1 ) . To amplify the C/prM/M/E region of 2 , 325 bp , specific primers ( 1 to 4 ) were used to produce 4 overlapping amplicons of approximately 900 bp and to amplify the complete coding region ( 10 , 173 bp ) , 15 overlapping amplicons of approximately 900 bp ( 1 to 15 ) . Thermocycling conditions consisted of a single step of 42°C for 60 minutes and 40 cycles of denaturation at 94°C ( 30 seconds ) , annealing at 56°or 63°C ( 60 seconds ) depending on the set of primers , extension at 72°C ( 2 minutes ) and a final extension at 72°C ( 10 minutes ) . Amplification was conducted using a Model 9700 thermal cycler ( Applied Biosystems ) . PCR products were purified from 1 . 0% agarose gels using QIAquick Gel extraction Kit or QIAquick PCR purification Kit ( Qiagen ) and used as template for cycle sequencing . Sequencing reactions were performed as recommended in the BigDye Dideoxy Terminator sequencing kit ( Applied Biosystems ) and the products were analyzed using an automated 3130 DNA Sequencer ( Applied Biosystems ) . Partial sequences ( C/prM/M/E ) and complete coding sequences for the unprocessed polyprotein ( 5′ and 3′ noncoding regions excluded ) were deposited in GenBank ( Table 2 ) . The analysis of similarities , percentage of identity and divergence among the strains analyzed were performed using Megalin Program ( DNAstar , www . dnastar . com ) . The multiple alignment was performed using CLUSTAL W ( http://www . ebi . ac . uk/clustalw/ ) and the phylogenetic analysis by MEGA 4 software ( www . megasoftware . net ) , using the Maximum Likelihood method ( ML ) , according to the Tamura-Nei model , with a bootstrap of 1 , 000 replications . Strains representative from the five genotypes available in Genbank ( www . ncbi . nlm . nih . gov ) were used for the comparison , DENV-1 ( GenBank accession number GU370049 ) , DENV-3 ( accession number EF629369 ) , and DENV- 4 ( accession number AF289029 ) strains were used as outgroup to root the trees ( Table 3 ) .
In this study , the strains BR64022/98 isolated in the 90's and Jamaica 1983 were considered as reference strains for comparison purposes . The percentage of similarity among the 25 DENV-2 strains ranged from 80 . 3 to 99 . 9% when those compared to each other and to strains representative of the different genotypes available on GenBank . The partial genome sequencing analysis characterized the Brazilian DENV-2 strains from this study as belonging to the Southeast Asian genotype , however a distinction of two Lineages within this genotype has been identified . It was observed that strains circulating prior DENV-2 emergence ( 1990–2003 ) belong to Southeast Asian genotype , Lineage I and strains isolated after DENV-2 emergence in 2007 belong to Southeast Asian genotype , Lineage II ( Figures 1 and 2 ) . Furthermore , the latter were more closely related to strains from the Dominican Republic ( DR59/01 ) , representative from the Southeast Asian genotype , Lineage II . When the 25 DENV-2 strains were compared to the strain BR64022/98 , amino acid substitutions leading to change in the biochemical properties were observed on the C and prM genes . On the E gene , a total of twelve substitutions were observed , with nine resulting in a change on the amino acid change of biochemical property ( Supplementary material 1 ) . No consistent differences were observed on the E gene from strains isolated from cases with different clinical manifestations analyzed , suggesting that if the disease's severity has a genetic origin , it is not only due to the differences observed on the E gene . To determine whether possible amino acids differences on other genes were related to disease severity , we fully analyzed ( coding region ) DENV-2 strains ( n = 9 ) , representative of DF cases isolated from 1990 to 1999 and strains isolated from fatal cases occurred after the DENV-2 re-emergence after 2007 until 2010 . The strain 0450/2008 , representative of the DENV-2 re-emergence isolated from a DF secondary case who evolved to death was fully sequenced and its comparison to the strain from the Dominican Republic ( DR59/2001 ) , representative of the DENV-2 re-emergence , showed 22 amino acid substitutions . Likewise , the strain 0690/2008 isolated from a DHF case occurred also during the re-emergence of DENV-2 had nine had amino acid substitutions when compared to the strain DR59/2001 , with seven of those leading to amino acid biochemical property change ( Table S1 ) . The DENV-2 strain 0337/2008 isolated from a newborn presenting a high anti-DENV IgG titer who evolved to death , infected probably due transplacental transmission as his mother was diagnosed with acute DENV infection , showed substitutions on NS2A , NS4A and NS5 , which were shared with the other two strains isolated from fatal cases ( Table S2 ) . The results obtained by the DENV-2 full-length genome sequencing did not point out consistent differences related to a more severe disease . A substitution on E390 ( N→D ) was reported as resulting in a reduction in viral replication in macrophages and dendritic cells [19] whereas E390 ( D→N ) resulted in enhanced replication , maturation and activation of macrophages , enhancement of the immune response with an increased production of cytokines , increased vascular permeability and consequently a greater chance of developing DHF [20] . All DENV-2 strains analyzed presented an asparagine ( N ) in E390 , previously identified as a probable genetic marker of virulence observed in DHF strains from Asian origin . The percentage of identity of the re-emergent DENV-2 with the Dominican Republic strain isolated in 2001 combined to the percentage of divergence with the strains first introduced in the country in the 90's suggests that those viruses did not evolved locally but were due to a new viral Lineage introduction in the country from the Caribbean .
In the Americas , the first DENV-2 was isolated in 1953 in Trinidad [21] and the first DHF epidemic caused by this serotype occurred in Cuba in 1981 after the introduction of DENV-2 genotype originated in Southeast Asia [10] , [22] . Epidemics studies showed that the DENV-2 introduced in Brazil , Colombia , Venezuela and Mexico had a common ancestor with isolates from Southeast Asia , suggesting the direct transmission from that region to the Americas [23] . In Brazil , the first DHF/DSS cases were reported after the DENV-2 introduction in Rio de Janeiro [13] , [24] , [25] , which spread to other states in the country . Phylogenetic analysis of DENV-2 strains circulating at that time confirmed the genotype circulating in Southeast Asia [26] , [27] . This observation was further corroborated in an extensive analysis of viruses from the states of Rio de Janeiro ( 1990 and 1995 ) , Ceará ( 1994 ) , Bahia ( 1994 and 1999 ) , Maranhão ( 1996 and 1998 ) , Mato Grosso ( 1997 ) , Pará ( 1998 ) , Rio Grande do Norte ( 1998 ) , Paraíba ( 1999 ) Sergipe ( 1999 ) , Espiríto Santo ( 1995 and 2000 ) and forty strains isolated in Pernambuco ( 1995–2002 ) [28] , [29] . After seven years without activity in Brazil , DENV-2 re-emerged in April of 2007 in the state of Rio de Janeiro causing the more severe dengue epidemic in the country in 2008 [30] , [31] . Phylogenetic analysis of DENV-2 circulating in 90's and after its re-emergence identified two distinct lineages within the Southeast Asian genotype [32] . In the present study , the analysis based on the sequencing of the C/prM/M/E genes ( 2 , 325 bp ) from 25 DENV-2 Brazilian isolates divided those strains in two distinct groups , one formed by DENV-2 isolated from 1991 to 2003 and another with strains isolated from 2007 to 2010 following the re-emergence of this serotype in the country . Corroborating previous phylogeny [26]–[29] strains isolated from 1991to 2003 were classified as Southeast Asian genotype , Lineage I and presenting similarities with the Brazilian strain BR64022/98 and the strain Jamaica/83 . However , the strains isolated between 2007 and 2010 , showed higher similarity with the strain DR59/01 , from the Dominican Republic , representing the Southeast Asian genotype , Lineage II , corroborating the analysis by Oliveira et al [32] . A study by Aquino et al [33] demonstrated that DENV-2 strains from Paraguay could also be grouped into two distinct lineages within the Southeast Asian genotype and suggested the introduction of a new lineage possibly associated a serotype shift from DENV-3 to DENV-2 , as observed in Brazil in 2007 and 2008 [31] . The absence of DENV-2 circulation in the years prior to its re-emergence and the high similarity observed between those viruses and the strain isolated in the Dominican Republic in 2001 , suggests the introduction of a new lineage of DENV-2 causing the 2008 epidemic in Brazil . Romano et al [34] also demonstrated that DENV-2 strains isolated in Sao Paulo State in 2010 were in a monophyletic group with the strains circulating in Rio de Janeiro in 2007 and 2008 and that those were closely related to strains isolated in Cuba and Dominican Republic , with a small genetic distance , suggesting that this new lineage of DENV-2 re-emerged in of Brazil may have been imported the Caribbean . Although genetic variants of DENV have been implicated in disease severity in the past [35] , [36] , it was with the advance of evolutionary studies based on phylogenetic analysis combined to epidemiological data that genotypes within the distinct serotypes were associated with a greater or lesser disease severity [11] , [37]–[40] . The strain isolated from a DHF case in 2000 ( strain RJ/67922/2000 ) presented an exclusive substitution on prM143 ( T→I ) when compared to the other strains analyzed in this study . However , substitutions related to DHF/DSS cases were identified on prM16 and prM81 [41] . Substitutions were found on the residues E129 ( V→I ) and E131 ( L→Q ) , and these are related to the division of the Southeast Asian genotype in two distinct clades , corroborating the observations that amino acids on E129 and E131 are in critical markers for genetic classification of DENV [33] , [42] . All 34 strains analyzed in this study presented an asparagine ( N ) on E390 , previously characterized as a probable trigger for DHF detected in strains of Asian origin [43] . Mutations on the flaviviruses domain III of E protein can induce virulence or attenuation of the virus to escape from the immune system [44] , [45] and in this study , changes were observed throughout this domain ( aa 297 to 394 ) . The DHF case , which culminated in death ( 59382/1997 ) showed amino acid differences only in the E gene , but those differences were shared with other DF cases strains , when they were compared to the strain BR64022/98 . In this study , a substitution on prM39 was observed on the strain 0690/2008 isolated from a DHF case with a fatal outcome , on the strain 55769/1996 from a DF case and on the strain 0199/2010 . . Catteau et al [46] demonstrated that the intracellular production of M ectodomain of all four DENV serotypes of DENV induce apoptosis in host cells . The carboxy terminus of prM protein with nine amino acids ( aa 32–40 ) of some flaviviruses was designated as Apopto M [46] and appears to play an important role in inducing apoptosis and cytopathic effects [46]–[48] . Several changes were observed along the NS protein genes . Studies conducted by Yábar , [49] show that mutations in NS1 are related to the development of DHF/DSS cases when they were compared to patients with DF . Despite the functional importance of mutations in NS genes remains unknown , future studies can elucidate their role in the emergence of strains and/or pathogenesis of the disease . It was not possible to correlate the role of Lineage II emergence with an increased severity of cases observed in the period between the years 2007–2010 . Furthermore , the occurrence of secondary infection may have been the risk factor for the development of more severe cases . In conclusion , this result shows a temporal circulation of genetically different viruses in Brazil probably due to the introduction of a new viral lineage from the Caribbean which lead to the re-emergence of this serotype after 2007 . In 2007–2008 , DENV-2 was responsible for most severe epidemic already described in the country , with 787 , 726 cases reported and 491 deaths [31] . Moreover , the Caribbean has been suggested as an important region for the circulation of DENV-2 , importation and exportation of strains from and to Central America and South America [42] , [50] , [51] . In the past 20 years , DENV-2 activity in Brazil has contributed significantly to changes in the disease morbidity and sudden age shift [30] . In dengue endemic countries , displacement of DENV serotypes , genotypes and lineages have been reported previously and have been associated with changes in the disease severity [40] , [52]–[55] . This emphasizes the need of straightening virological surveillance to monitor the emergence or re-emergence of DENV strains with pathogenic potential to cause epidemics .
|
In Brazil , the first dengue haemorrhagic cases were reported after the DENV-2 introduction in Rio de Janeiro , which spread to other states in the country . Aiming to perform the molecular characterization and phylogenetic analysis of DENV-2 during twenty years of viral activity in the country , strains isolated from patients presenting different disease manifestations were sequenced . Phylogeny characterized the DENV-2 as belonging to the Southeast Asian genotype , however a distinction of two Lineages within this genotype has been identified . Furthermore , all strains presented an asparagine in E390 , previously identified as a probable genetic marker of virulence . The results show a temporal circulation of genetically different viruses in Brazil , probably due to the introduction of a new viral lineage from the Caribbean , which lead to the re-emergence of this serotype after 2007 , causing the most severe epidemic already described in the country .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"virology",
"biology",
"microbiology"
] |
2013
|
Twenty Years of DENV-2 Activity in Brazil: Molecular Characterization and Phylogeny of Strains Isolated from 1990 to 2010
|
Many pathogens express a surface protein that binds the human complement regulator factor H ( FH ) , as first described for Streptococcus pyogenes and the antiphagocytic M6 protein . It is commonly assumed that FH recruited to an M protein enhances virulence by protecting the bacteria against complement deposition and phagocytosis , but the role of FH-binding in S . pyogenes pathogenesis has remained unclear and controversial . Here , we studied seven purified M proteins for ability to bind FH and found that FH binds to the M5 , M6 and M18 proteins but not the M1 , M3 , M4 and M22 proteins . Extensive immunochemical analysis indicated that FH binds solely to the hypervariable region ( HVR ) of an M protein , suggesting that selection has favored the ability of certain HVRs to bind FH . These FH-binding HVRs could be studied as isolated polypeptides that retain ability to bind FH , implying that an FH-binding HVR represents a distinct ligand-binding domain . The isolated HVRs specifically interacted with FH among all human serum proteins , interacted with the same region in FH and showed species specificity , but exhibited little or no antigenic cross-reactivity . Although these findings suggested that FH recruited to an M protein promotes virulence , studies in transgenic mice did not demonstrate a role for bound FH during acute infection . Moreover , phagocytosis tests indicated that ability to bind FH is neither sufficient nor necessary for S . pyogenes to resist killing in whole human blood . While these data shed new light on the HVR of M proteins , they suggest that FH-binding may affect S . pyogenes virulence by mechanisms not assessed in currently used model systems .
The human complement system plays a key role in the defense against infections , in inflammatory reactions , and in immune responses [1] , [2] . Fulfillment of these roles requires complement activation , which may proceed via either of three pathways , the classical , lectin and alternative pathways . The alternative pathway plays a particularly important role in promoting innate immunity to infections , because it is continuously activated at a low level and includes an efficient amplification loop , allowing rapid activation and attack on an infecting pathogen [3] . Accordingly , the alternative pathway must be tightly controlled to avoid excess complement activation . A major component of this control system is the ∼150 kDa protein factor H ( FH ) , which is present both free in plasma and bound to cell surfaces , where it down-regulates complement activation [4] , [5] . Lack of FH causes uncontrolled activation via the alternative pathway and kidney disease , demonstrating the in vivo importance of this regulator [6] . FH not only binds to host cell surfaces but also binds to surface proteins of many pathogenic bacteria , as first reported for the M6 protein of Streptococcus pyogenes ( group A streptococcus ) [7] . In the currently favored model , FH is recruited to M protein to protect the bacteria from complement attack and rapid killing , in particular through phagocytosis [8]–[10] . It may seem intuitively obvious that this model must be correct , possibly explaining why it is presented as a fact in numerous publications and review articles and even in textbooks [11] , [12] . However , to our knowledge there is no conclusive evidence that FH bound to M protein promotes virulence , i . e . growth in vivo . This situation prompted us to study the interaction between FH and S . pyogenes M protein , with focus on the possible role of bacteria-bound FH in virulence . S . pyogenes is a Gram-positive bacterium that causes a variety of diseases , including superficial throat and skin infections , streptococcal toxic shock syndrome , and the autoimmune disease rheumatic fever [13] . The fibrillar M protein , which is the most extensively studied virulence factor of S . pyogenes , is a surface-anchored coiled-coil protein that prevents phagocytosis [14] . A characteristic feature of this protein is the presence of an N-terminal hypervariable region ( HVR ) , which has a length of ∼50–100 amino acid residues and is a key target for type-specific protective antibodies [14] . The HVR exhibits extreme sequence divergence among M proteins expressed by different strains but is typically stable within a strain , allowing the identification of ∼200 distinct M ( emm ) types [15] . Although the ability of M protein to bind FH has attracted much interest , the location of the binding site for FH has remained unclear . An early study reported that FH binds to the conserved C repeat region of the M6 protein , implying that all M proteins may bind FH via this conserved region [16] . However , another study indicated that FHL-1 , a naturally occurring minor FH splice variant that would be expected to bind to the same site as FH , interacted with the HVR of the M5 and M6 proteins , and did not bind to the M22 protein [17] . Combining these two results , one report suggested that FH and FHL-1 bind to both the HVR and the C-repeat region of an M protein [18] . Thus , it is unclear whether all M proteins bind FH and where FH binds in an M protein . The biological role of FH/FHL-1 bound to M protein has similarly remained unclear [18]–[20] . Analysis with pure proteins indicated that bacteria-bound FH/FHL-1 retains its complement regulatory activity , suggesting that recruitment of the human protein indeed protects S . pyogenes against complement attack and phagocytosis [7] , [17] . However , studies of bacteria suspended in human plasma suggested that , at least for FH , binding is largely blocked by fibrinogen ( Fg ) , implying that FH may bind poorly to M protein under physiological conditions [17] , [21] . In agreement with this finding , the ability of an M protein to bind FH/FHL-1 had little effect on complement deposition , when the analysis was performed in human plasma [18] . The latter study also suggested that binding to the HVR is of limited importance for phagocytosis resistance , but since the authors reported that FH not only binds to the HVR , but also binds to the C repeats , the results did not exclude that binding to the C repeats was sufficient to promote phagocytosis resistance . Thus , the role of FH-binding remains controversial , as witnessed by two recent reports , which suggested that FH-binding indeed promotes phagocytosis resistance [22] , [23] . Here , we studied the ability of different M proteins to bind FH , the site of FH-binding in an M protein , and the biological role of the binding . Using seven highly purified M proteins , we found that human FH binds to some ( but not all ) M proteins and binds solely to the HVR , which represents a distinct FH-binding domain . Unexpectedly , studies in a transgenic mouse model did not support the hypothesis that bound FH promotes virulence during the acute stage of an infection . Moreover , assays in a human whole blood system indicated that FH-binding is neither sufficient nor necessary for phagocytosis resistance .
The proteins studied here are shown schematically in Figure 1A . The FH molecule is composed of 20 short consensus repeat ( SCR ) domains , of which SCR1-4 are essential for complement regulatory activity , while SCR19-20 promote binding to polyanions on human cell surfaces [4] , [5] . A site in SCR7 has been implicated in the binding of M proteins . The presence of histidine ( H ) rather than tyrosine ( Y ) at position 402 in this site reduces the affinity between FH and the M6 protein [24] and increases the risk for the common eye disease age-related macular degeneration ( AMD ) [25] . Some tests were performed with C4BP , which like FH is a major complement regulator present in human plasma . This ∼570 kDa protein down-regulates the classical and lectin pathways , is a member of the same protein family as FH , and binds to many M proteins , which have a C4BP-binding site in the HVR [26]–[28] . The seven highly purified recombinant M proteins studied here were of either class I or class II , the two major classes of M proteins [29] , and had the expected molecular mass , as demonstrated by SDS-PAGE ( Figure 1B ) . Moreover , they had the expected N-terminal sequence , as shown by Edman degradation , demonstrating that their HVRs were intact ( data not shown ) . The class I proteins included M1 and M3 , two of the most common M types associated with invasive infections in the western world [30] , as well as M5 , M6 and M18 , which have been epidemiologically associated with rheumatic fever [29] . Thus , the five class I proteins were of serotypes associated with the two most important life-threatening diseases caused by S . pyogenes [13] . The two class II proteins , M4 and M22 , are clinically common and have been extensively studied [31]–[33] . Although this study was focused on FH , it was of interest to compare several M proteins for ability to bind the two structurally related complement regulators FH and C4BP . For this purpose , the seven purified M proteins were immobilized in microtiter wells and tested for binding of the two human proteins ( Figure 1C ) . Thus , the analysis was performed under non-denaturing conditions . As a control , the M proteins were analyzed for ability to bind fibrinogen ( Fg ) , a ligand that binds to all class I proteins but not to class II proteins , according to current knowledge [34] . In this analysis , the class I proteins M1 and M3 did not bind FH or C4BP but showed binding of Fg , as expected . In contrast , binding of FH was observed for the three class I proteins M5 , M6 and M18 . The two class II proteins showed good binding of C4BP but little or no binding of FH . For clarity of presentation , the data in Figure 1C represent results obtained with a single concentration of human ligand , but the binding was concentration dependent , as shown for FH in Figure S1 . These binding data with FH and C4BP extend and confirm previous studies [7] , [17] , [18] , [26] , [27] , [35] and indicate that ability to bind FH is a property of some but not all M proteins . Our data indicated that several M proteins are unable to bind FH , but did not formally exclude that these M proteins bind FH with such a low affinity that binding was not detected with the standard methods used . However , striking differences clearly exist among the M proteins studied here . Indeed , our data suggest that M proteins may be divided into at least three groups , representing proteins that selectively bind FH , selectively bind C4BP , or bind neither of these human ligands . Subsequent analysis was focused on the three FH-binding M proteins , M5 , M6 and M18 . To analyze whether M protein is the only FH-binding surface protein of the corresponding strains , we incubated wild type bacteria and isogenic M-negative mutants with FH ( Figure 1D ) . After incubation and washes , bound FH was eluted from the bacteria . While the M-positive strains showed good binding of FH , little or no binding was observed for the M-negative mutants , indicating that the M-positive strains express one major FH-binding protein , the M protein . It is of particular interest that the M5-negative strain was completely unable to bind FH because this result indicates that the M5 strain , which was used for infection experiments described later , expresses a single FH-binding protein , the M5 protein . Of note , this analysis with whole bacteria was performed with a relatively high concentration of FH ( 50 µg/ml ) and few washes , suggesting that a low-affinity binding to a surface structure different from M5 might have been detected , but no binding was seen . To identify the binding site ( s ) for FH in an M protein , we first studied the M5 protein , employing a series of isogenic chromosomal deletion mutants of the S . pyogenes M5 strain ( Figure 2A ) . These mutant strains lacked the entire M5 protein ( strain ΔM5 ) or expressed truncated M5 proteins lacking parts of the protein ( strains ΔN1 , ΔN2 , ΔB and ΔC ) . The truncated M5 proteins and the wild-type protein are expressed at similar levels on the surface , allowing direct comparisons of the corresponding strains [20] . Incubation of bacterial suspensions with FH showed binding to all truncated mutant proteins except ΔN2 , which lacks the C-terminal half of the HVR . The simplest explanation for these data is that FH binds to a single site , located in the N2 region of the M5-HVR . To study the FH-binding site in M5 under different conditions , we constructed two M5 proteins with a short deletion in the N2 region , the M5Δ80-86 and M5Δ87-93 proteins ( Figure 2B and S2A ) . Each of these two proteins lacked one coiled-coil heptad ( 7 amino acids ) . Pure preparations of the deletion proteins and of the intact M5 protein were immobilized in microtiter wells and analyzed for ability to bind FH . In this analysis , the two deletion proteins did not bind FH , supporting the conclusion that FH binds solely to the N2 region of M5 ( Figure 2B ) . Because it has been reported that FH binds to the C repeat region of the M6 protein [16] , studies were also performed in that system ( Figure 2C and S2A ) . We hypothesized that FH binds to the C-terminal part of the HVR also in M6 , and studied an M6 deletion protein lacking a short sequence ( 14 amino acids , two heptads ) in this part of the HVR . This deletion protein , designated M6Δ97-110 , was completely unable to bind FH , suggesting that M6 has a single FH-binding site located in the HVR ( Figure 2C ) . Of note , the lack of FH-binding to the M5 and M6 deletion proteins did not reflect a general inability of these mutant proteins to bind a ligand , because they retained ability to bind fibrinogen , which binds to the B repeats of these M proteins ( Figure S2B ) . Thus , the studies of M5 and M6 deletion proteins supported the conclusion that FH-binding M proteins have a single binding site located in the HVR , while FH does not bind to the C repeat region , which was intact in the deletion proteins . Given the early report that FH binds to the C repeat region of M6 , additional analysis was performed with that M protein . For this purpose , we employed a construct designated M6-Crep , which included the C repeats and was of the same length as an M6 fragment proposed to bind FH [16] ( Figure 2C ) . To promote coiled-coil formation , which may be essential for ligand-binding ability [26] , [36] , [37] , the M6-Crep construct was dimerized via a C-terminal cysteine residue . When analyzed by SDS-PAGE under reducing and non-reducing conditions , this construct was pure and migrated as expected ( Figure S2C ) . After immobilization in microtiter wells , M6-Crep was completely unable to bind FH , like the M6Δ97-110 deletion protein , while good binding was seen for the M6 control ( Figure 2C ) . These data provide further evidence that M6 has a single FH-binding site located in the HVR . The early immunochemical study , which suggested that FH binds to the C repeats of the M6 protein [16] , was supported by studies of whole bacteria . In that analysis , a bacterial M6 mutant lacking C repeats showed reduced binding of FH [19] , [38] . Because this result appeared to be at odds with our data , we reanalyzed the role of the C repeats in FH-binding , using S . pyogenes bacterial mutants expressing an M5 or M6 protein lacking C repeats . Of note , each of these C repeat mutant strains produces an amount of M protein comparable to that of the corresponding wild type strain , allowing comparison of binding properties [19] , [20] . When the wild type strains and the deletion mutants were compared for ability to bind FH , the lack of C repeats had no effect in the M5 system but caused a limited reduction of FH binding in the M6 system ( Figure 2D and E ) . However , the effect in the M6 system was smaller than previously reported [19] , [38] . We conclude that the C repeat region is dispensable for FH-binding to whole M5 and M6 bacteria . The limited effect observed in the M6 system might reflect that the HVR probably is located closer to the bacterial cell wall in a mutant lacking C repeats , resulting in steric hindrance . Such a steric effect would not necessarily be seen in the M5 system , because the M5 and M6 strains have cell envelopes of different composition . To analyze whether the HVR of an FH-binding M protein is sufficient for binding , we tested purified recombinant HVRs . These HVRs were dimerized by means of a C-terminal cysteine residue not present in the intact M protein . As indicated above , previous work had indicated that not only intact M proteins [36] , but also domains derived from M proteins , must be present in dimeric form to bind ligands [26] , [37] , [39] . An isolated domain that is not artificially dimerized via a cysteine residue may be too short to promote formation of a dimeric coiled-coil , unlike the intact M protein . Dimerized HVRs were prepared for the FH-binding M5 , M6 and M18 proteins and the non-binding M1 protein ( Figure 3A ) . When these HVRs were immobilized in microtiter wells and analyzed for ability to bind FH , binding was observed for the HVRs derived from the FH-binding M proteins , but not for that derived from M1 ( Figure 3B ) . In this test , binding was strongest for the M18-HVR and weakest for the M6-HVR , but all three HVRs derived from FH-binding M proteins could bind FH , in contrast to the M1-HVR , which was completely negative . Thus , isolated HVRs derived from FH-binding proteins retained ability to bind FH . To analyze the specificity with which the free HVRs interacted with FH , we employed columns containing immobilized HVRs . Whole human serum was passed through such columns , or through a control column without HVR , and bound proteins were eluted . For each of the three FH-binding HVRs , a single protein species dominated in the eluate , as demonstrated by SDS-PAGE ( Figure 3C ) , and this protein was identified as FH by mass spectrometry and western blot ( data not shown ) . No FH was eluted from the control column , but weak SDS-PAGE bands corresponding to polypeptides of lower molecular mass were observed for all eluates , implying that they represented proteins binding to the column matrix . Of note , the eluates from the HVR columns did not contain detectable amounts of the ∼42 kDa FHL-1 protein , which occurs in low concentration in serum and binds to the HVR of the intact M5 protein [17] . Possibly , the HVRs selectively bind FH but not FHL-1 under these conditions . These data show that the three HVRs studied here all bind FH with high specificity , although their sequences show extensive sequence divergence , as seen in an alignment ( Fig . 3D ) . Indeed , no sequence longer than three amino acid residues is shared by all three HVRs . Pathogens commonly show species specificity in their ability to bind a complement regulator , as demonstrated for both C4BP [40]–[42] and FH [43]–[45] . To analyze whether the FH-binding HVRs studied here show species specificity , we applied human , mouse or rabbit serum to columns containing immobilized HVRs and performed the same type of analysis as described in Figure 3C . Binding was only observed for human FH , not for mouse or rabbit FH , as shown for the M5-HVR in Figure 4A . Similar results were obtained for columns containing the M6-HVR or the M18-HVR ( data not shown ) . The lack of recovery of mouse FH was not due to lack of FH in mouse serum , for which the serum concentration of FH is similar to that in humans [6] , [46] . For rabbit serum , the concentration of FH is ∼3-fold lower than in human serum [47] , but that difference cannot explain the complete lack of FH-binding in our analysis . Thus , the HVRs studied here not only show specificity for FH among all proteins in human serum but also show species specificity . The available evidence indicates that an M protein binds to a site in SCR7 of human FH [24] ( Figure 1A ) . To confirm that the M proteins studied here have HVRs that bind in this region , we performed two types of analysis . First , we employed serum from transgenic ( Tg ) mice expressing a chimeric FH , in which only SCRs 6–8 are derived from human FH ( Figure 4B , top ) [48] . The concentration of chimeric FH in these Tg mice is similar to that of FH in wild-type ( wt ) mice [48] . The FH present in Tg serum was compared with FH in wt serum for ability to bind to the M5 , M6 or M18 proteins , which were immobilized in microtiter wells . Bound FH was detected with a monoclonal antibody directed against the SCR1-4 region of mouse FH . In this analysis , the chimeric FH showed binding to all three M proteins , while FH present in wt mouse serum did not bind ( Figure 4B ) . This result confirms the species specificity of the binding and is in agreement with the reports that M proteins bind to a site in SCR7 . Because this analysis employed intact M proteins , the results also indicate that mouse FH does not bind to a site in M proteins located outside of the HVR . In a second type of analysis , we tested whether the binding of one HVR is inhibited by the other HVRs . For this purpose , the three free HVRs were analyzed for ability to inhibit the binding of an intact M protein to immobilized FH ( Figure 4C ) . The results show that binding of each M protein could be inhibited by the homologous HVR and also by the other two HVRs . In contrast , no inhibition was observed with the HVR derived from the non-FH-binding M1 protein . These data indicate that the HVRs studied here , which have highly divergent sequences , bind to the same or overlapping site ( s ) , most likely in SCR7 of FH . Because the isolated HVRs studied here apparently retain their native structure , as indicated by their ability to bind FH , it was possible to directly compare their antigenic properties . Such comparison was performed with rabbit antisera raised against the isolated HVRs ( Figure 5A ) . In this analysis , the M6-HVR did not cross-react with the other two HVRs , while the HVRs of M5 and M18 exhibited limited cross-reactivity . None of the antisera reacted with the non-FH-binding M1-HVR , used as control . The cross-reactivity between M5 and M18 was not surprising , because these two HVRs exhibit the highest residue identity among the three HVRs studied here ( Fig . 3D ) . Thus , the FH-binding HVRs show little or no antigenic cross-reactivity , although they all bind the same ligand . To analyze whether antibodies directed against an HVR block FH-binding , the three FH-binding M proteins studied here were mixed with rabbit antiserum to the corresponding HVR and analyzed for ability to bind FH ( Figure 5B ) . In this analysis , binding of FH was efficiently blocked by anti-HVR antibodies but not by preimmune serum , suggesting that anti-HVR antibodies appearing in an infected human may block the binding of FH to an M protein . This result was expected , but not obvious , because polyclonal antibodies may bind to a bacterial protein without blocking its function [49] . The combined data available in the M5 system suggest that the FH-binding ability of this M protein does not contribute to phagocytosis resistance , as evaluated in the whole blood assay . This conclusion follows from the finding that the M5 mutant ΔN2 completely lacked ability to bind FH ( Figure 2A ) but remained resistant to phagocytosis [20] . To further analyze the role of FH-binding in phagocytosis resistance , we studied the M1 and M3 proteins , which did not show detectable binding of FH in our immunochemical analysis ( Figure 1C and S1 ) . The ability to bind FH was first analyzed for whole bacteria expressing M1 or M3 , and isogenic M-negative mutants . As a control , M5-positive and -negative strains were included ( Figure 6A ) . The procedure employed was similar to that used for the analysis of FH-binding strains reported in Figure 1D , i . e . bacteria were incubated with human FH , washed and analyzed for bound protein . In this analysis , the M1-positive and -negative strains were able to bind FH . This result was expected , because M1 strains express the FH-binding Fba protein [50] , which is unrelated to M proteins [51] . In contrast , no FH-binding was observed for the M3-positive and -negative strains , reflecting the inability of the M3 protein to bind FH and the absence of Fba from M3 strains [51] . These results do not formally exclude that M3 binds FH with an affinity too low to allow detection under the experimental conditions we used , but this seems unlikely , because the analysis was performed with a relatively high concentration of FH ( 50 µg/ml ) and few washes . Phagocytosis tests in whole human blood were performed with the M1 and M3 strains ( Figure 6B ) . The results unequivocally showed that the M-positive strains were resistant to phagocytosis , while the M-negative strains were sensitive , in agreement with the classical identification of M proteins as antiphagocytic . Thus , the M1 protein confers phagocytosis resistance although this M protein does not bind FH , and the M1-negative strain is phagocytosis sensitive , although it binds FH . Moreover , the M3 protein conferred phagocytosis resistance although this M protein did not bind detectable amounts of FH . Together , these data indicate that binding of FH to S . pyogenes is neither sufficient nor necessary for phagocytosis resistance . The specificity with which FH binds to the HVR of some M proteins suggested that FH-binding contributes to bacterial virulence , even if bound FH does not contribute to phagocytosis resistance in human blood . In an attempt to prove this hypothesis , we used the Tg mice described above [48] , which express a chimeric FH that binds M protein ( Figure 4B ) . If recruitment of FH to an M protein promotes bacterial virulence , one would expect the corresponding S . pyogenes strain to be more virulent in Tg mice than in wt mice . The Tg mice were well suited for this analysis , because the serum concentration of chimeric FH is similar to that of FH in wt mice , because the chimeric FH includes the parts of mouse FH implicated in complement regulatory activity , and because the part derived from human FH included the Y402 residue , which may favor binding to M protein [24] . Moreover , the chimeric FH is functional in vivo , as determined by ability to prevent C3 consumption [48] , [52] . For use of the Tg mice in infection experiments , the chimeric FH should have binding properties similar to those of human FH , with regard to M protein . To analyze whether this was the case , we purified the chimeric FH and compared it with pure human FH and also with pure mouse FH ( Figures 7A and 7B ) . Because the chimeric FH was derived from the Y402 allelic variant , pure Y402 human FH was used for the comparison . In SDS-PAGE , the chimeric FH migrated like the human Y402 FH and mouse FH . However , western blot analysis with anti-human FH demonstrated that the three FH proteins had different reactivity , as expected ( Figure 7A ) . While good reactivity was observed for human FH , the chimeric FH reacted weakly , and mouse FH hardly reacted at all under these conditions . When these three pure FH preparations were immobilized in microtiter wells and tested for ability to bind the M5 protein , the chimeric FH and human FH had similar dose-dependent ability to bind M5 , while mouse FH did not bind M5 ( Figure 7B ) . These data indicated that the chimeric FH had the desired properties and confirmed the species specificity of FH binding . Infection experiments with Tg mice were performed with the S . pyogenes M5 strain . Mice were subjected to i . p . infection with a sublethal dose and were sacrificed after 18 h , followed by determination of bacterial counts in the spleen . Thus , the experiment analyzed whether the ability to bind FH enhances bacterial virulence during the acute stages of an invasive infection . No difference was seen between the two groups ( Figure 7C ) . In a second type of analysis , the survival of Tg and wt mice was compared after infection with an ∼LD90 dose of M5 bacteria . Again , no difference was seen between the two groups , which succumbed to infection with the same kinetics ( Figure 7D ) . Because most of the mice died rapidly in this experiment , it seemed possible that too large a dose of bacteria might have been used , obscuring a difference between the two types of mice . However , similar results were obtained in an experiment in which a lower bacterial dose was used and most of the mice survived ( Figure 7E ) . To analyze whether the route of infection influenced the results , we also infected mice i . n . and determined bacterial counts in lungs after 18 or 42 h . Again , no difference was seen between Tg and wt mice ( data not shown ) . Thus , studies with Tg mice did not provide evidence that FH-binding ability promotes virulence during the acute stages of an infection . The Tg mouse model appeared to be optimal for analysis of the role of M protein-bound FH in S . pyogenes infection . Nevertheless , it seemed possible that intact human FH would work better than chimeric FH in promoting S . pyogenes virulence . To analyze this possibility , we used a model in which pure human Y402 FH ( total 200 µg ) or only PBS was administered i . p . shortly before and at infection with the M5 strain . This model could be used , because human FH can act as a complement regulator in the mouse [53] . In this analysis , there was no difference in spleen colonization between the two groups that received pure FH or PBS ( Figure 7F ) . In contrast , administration of whole human serum ( total 400 µl , containing an amount of FH similar to that administered in pure form ) strongly increased growth of the M5 strain in spleens , as compared to control mice receiving whole mouse serum ( Figure 7G ) . This result can most simply be explained by the presence in serum of plasminogen , which is known to strongly enhance S . pyogenes virulence in a species specific manner [54] , but another human serum protein could also have caused the effect . Of note , this analysis indicated that the mouse infection model used here could be employed to detect an enhancement of virulence . In the experiment with pure FH , it is possible that the amount of FH administered was too small , but the results are in agreement with the studies employing Tg mice .
Our studies of seven purified M proteins demonstrated that different M proteins vary dramatically in ability to bind FH and C4BP , the two major complement regulators in human plasma . Indeed , the data suggest that M proteins may be divided into at least three groups , depending on their ability to bind either FH or C4BP , or neither of these proteins . Thus , ability to bind FH is not a general property of M proteins . Although we cannot exclude that binding of FH with very low affinity escaped detection in the assays employed here , the data demonstrate striking differences in binding ability among different M proteins . The demonstration that the M5 , M6 and M18 proteins bind FH is in agreement with previous studies employing purified proteins and mutant strains [7] , [16] , [17] , [55] , and our results suggest that the strains of these M types used here express a single major FH-binding protein , the M protein . For the other two class I proteins studied here , M1 and M3 , the situation has remained unclear . While one study employing whole bacteria showed lack of binding to M1 and M3 strains [21] , another study indicated that M1 strains bind FH , not via the M1 protein but via the Fba protein , which is unrelated to M proteins [50] . The apparent discrepancy between these two studies may be explained by degradation of FH-binding surface proteins by the SpeB protease , resulting in loss of FH-binding , unless the bacteria are grown in the presence of a SpeB inhibitor [56] . Thus , binding studies performed with whole bacteria must be interpreted with caution . Indeed , we have noted dramatic loss of FH-binding ability if S . pyogenes is not grown in the presence of such an inhibitor ( data not shown ) . Although FH does not bind to all M proteins , the ability to bind this ligand may be an important property of some M proteins , e . g . by favoring one type of infection . Such a situation has been described for the variable Plasmodium falciparum PfEmp1 protein , in which the presence of certain regions is associated with specific types of malaria [57] , [58] . For the FH-binding M5 , M6 and M18 proteins , it is of interest that strains of these M types have been epidemiologically associated with rheumatic fever , the major cause of mortality following S . pyogenes infection [13] , [29] . The HVR of an M protein most likely plays a key role in pathogenesis , otherwise it would be eliminated by deletion [59] . Insight into the role of different HVRs is therefore essential for an understanding of S . pyogenes infections [26] , [59] , [60] . Our studies demonstrate that FH binds to the HVR of the M5 , M6 and M18 proteins and strongly suggest that these M proteins do not have a second binding site for FH . In particular , several types of analysis indicated that the C repeat region of M5 or M6 does not bind FH . This result is not in agreement with an early immunochemical study suggesting that FH binds to the conserved C repeat region of the M6 protein [16] , but fits with our finding that FHL-1 , a naturally occurring FH splice variant , binds to the HVR of the M5 protein and probably also the M6 protein [17] . Previously , it appeared possible that FH and FHL-1 bind to different regions of an M protein , but the combined data now strongly indicate that these human proteins solely bind to the HVR of an M protein . The reason for the discrepancy between our results and those reported earlier [16] is unclear , but it is conceivable that the C-terminal M6 fragment used in the early study was contaminated with FH-binding material . Moreover , the peptide inhibition tests reported in that study did not include a control for unspecific inhibition . Concerning binding tests with whole bacteria ( Figure 2D and E ) , the discrepancy between our results and earlier work [19] , [38] is less obvious , because at least one of the earlier reports indicated that a surface-expressed M6 protein lacking C repeats retained some ability to bind FH , although binding was lower than for intact M6 [38] . One possible explanation for the different result reported here is that the mutant M6 protein lacking C repeats might show increased sensitivity to the secreted SpeB protease . We avoided this potential problem by growing the bacteria in the presence of an SpeB inhibitor . Because we only studied a limited number of the ∼200 known M-types , many M proteins probably have an HVR that binds FH . It can be surmised that the FH-binding HVRs of these M proteins will have the same properties as those studied here , exhibiting extensive sequence divergence and little or no antigenic cross-reactivity but identical binding properties . The sequence divergence may have arisen through selection of antigenic escape variants retaining ability to bind FH , an argument implying that bound FH favors bacterial virulence . Interestingly , the FH-binding HVRs could be studied as isolated polypeptides that retained ability to specifically bind FH . Thus , these HVRs correspond to distinct FH-binding domains , a property that allowed direct immunochemical comparisons and may facilitate future biochemical and structural studies . Similarly , other ligand-binding regions of M proteins can be studied in isolated form , as demonstrated for C4BP-binding HVRs [26] , IgA-binding regions [37] , and Fg-binding regions [59] , [61] . These findings suggest that the fibrillar M protein may be envisaged as a string of domains , representing regions that interact with different human proteins . The biological role of FH-binding to M protein remains unclear . Indeed , it is not even clear that binding of FH occurs under physiological conditions , because the known FH-binding M proteins all bind Fg , which may sterically interfere with FH-binding [17] , [21] , [62] . Indeed , ability to bind FH/FHL-1 did not influence complement deposition , when the analysis was performed in human plasma , presumably because the Fg in plasma blocked FH-binding under these conditions [18] . However , immunochemical tests and analysis of complement deposition are of necessity performed under in vitro conditions that are of uncertain relevance for the in vivo situation . This situation focused interest on the use of Tg mice for in vivo analysis , and on analysis of FH-binding in the whole blood phagocytosis system , an ex vivo system believed to reflect the human in vivo situation . Concerning the in vivo role of FH-binding , our studies with Tg mice did not provide evidence that binding of FH to the M5 protein promotes virulence during the acute stages of an infection . Indeed , the Tg mice employed here were not more sensitive than wt mice to the FH-binding M5 strain , as determined by bacterial growth in spleens and studies of survival . Of note , this result indicates that FH-binding does not promote phagocytosis resistance in mouse blood , otherwise one would have expected more bacterial growth in the spleens of Tg animals , because bacteria must pass through blood to reach the spleen . Concerning growth in human blood , several lines of evidence now indicate that binding of FH ( or FHL-1 ) to M protein does not provide a general explanation for phagocytosis resistance . Indeed , M proteins such as M1 and M3 do not bind detectable amounts of FH but nevertheless confer resistance to phagocytosis . This result does not exclude that an FH-binding M protein such as M5 might recruit FH to promote phagocytosis resistance , but the studies of M5 strongly indicate that this is not the case . In particular , the chromosomal M5 mutant ΔN2 was resistant to phagocytosis [20] , although it lacks detectable ability to bind FH , as shown here . While it cannot be formally excluded that this mutant retained ability to bind FH with an affinity that was too low to allow detection , the simplest explanation for our data is clearly that the ability of M5 to promote phagocytosis resistance is independent of FH-binding . This conclusion raises the question how an M protein promotes resistance to phagocytosis . Interestingly , the available evidence suggests that recruitment of human plasma proteins different from FH may play a key role [33] . Work on class I proteins such as M5 has focused interest on the Fg-binding B repeats [20] , [34] , [63] , [64] , and work on the class II protein M22 has focused interest on two adjacent N-terminal regions implicated in the binding of C4BP and IgA [33] . In contrast to our conclusions , two recent papers suggest that FH-binding to M protein indeed promotes phagocytosis resistance in S . pyogenes [22] , [23] . However , some of the strains employed in those studies were sensitive to phagocytosis , making them unsuitable for analysis of phagocytosis resistance , and none of the studies provided evidence that the strains studied expressed an FH-binding M protein . Indeed , both studies were largely focused on M1 and OF positive strains , in which FH-binding most likely was promoted by the Fba protein , which has limited , if any , effect on phagocytosis resistance [50] , [51] . In the first of these papers , the authors made the interesting observation that growth of some S . pyogenes strains in human blood was affected by the Y402H polymorphism in FH , suggesting that binding of FH affects resistance to phagocytosis [22] , but the interpretation of these data is uncertain , because clear results were only obtained with two strains ( of types M1 and st369 ) in which binding of FH most likely was promoted by the Fba protein , not the antiphagocytic M protein . Thus , this finding does not provide information on the role of FH-binding to M protein . In the second study , the authors reported that phagocytosis of S . pyogenes in whole blood was promoted by the addition of an FH fragment assumed to inhibit the interaction between FH and M protein [23] . However , the concentration of inhibitor used was much below the level that would be required to cause inhibition of FH-binding in blood , according to other data reported in the same paper , and it was not excluded that the FH fragment had unspecific effects . Thus , neither of the two studies provided conclusive evidence that FH-binding to M protein promotes phagocytosis resistance . Although FH-binding ability did not promote phagocytosis resistance or virulence in the systems studied here , the specificity of the binding suggests that FH bound to an HVR affects virulence under certain conditions . What could be the function of M protein-bound FH , if it does not promote resistance to phagocytosis ? In one possible scenario , FH promotes virulence by promoting adhesion of pathogens to host cells , not by down-regulating complement [65] , [66] . For S . pyogenes our studies with transgenic mice did not support this hypothesis , because Tg mice were not more sensitive to infection , even when the bacteria were administered via the i . n . route . Moreover , in vitro adhesion tests with epithelial cells did not provide evidence that FH promotes S . pyogenes adhesion in an M-protein-dependent fashion ( data not shown ) . Possibly , FH bound to M protein does not contribute to virulence during the early stages of an S . pyogenes infection but has its major effect later , when down-regulation of complement activation might modulate inflammatory and adaptive immune responses [2] , [67] , [68] . Thus , bound FH might contribute to S . pyogenes virulence by mechanisms not assessed in currently used model systems . This hypothesis is fully compatible with the suggestion that the Y402H polymorphism in FH may affect sensitivity to S . pyogenes [23] , [24] . Because the HVR of an M protein plays a key role in virulence and is a target for protective antibodies , it is of interest to consider what is now known about the ligand-binding properties of different HVRs . Current knowledge is summarized in Figure 8 , with focus on the M proteins studied here . For the M1 and M3 proteins little is yet known about the HVR , but the HVR of M1 was suggested to bind the antibacterial peptide LL-37 [60] , [69] , and the HVR of M3 was reported to bind a collagen fragment [70] . The HVR of the M5 , M6 and M18 proteins binds FH , as reported here . Of note , our data on M5 show that only the C-terminal part of the HVR is absolutely required for FH-binding . It is possible that the N-terminal part of this HVR enhances the affinity and/or the specificity of FH-binding , but it is also conceivable that the HVR has a second function , in addition to FH-binding . Indeed , deletions in the M5-HVR block mouse virulence , although this HVR does not bind mouse FH , suggesting that the HVR makes an FH-independent contribution to virulence [59] . Finally , the HVRs of M4 and M22 bind C4BP [27] , and IgA binds to an adjacent region that also is very variable [31] , [71] . These two ligand-binding regions in the N-terminal part of M4 and M22 have a combined length that is shorter than the total length of the HVR in the other M proteins considered here , supporting the notion that the HVR of an M protein may have more than one function . Determination of the in vivo relevance of in vitro findings represents one of the major challenges in studies of microbial pathogenesis [72] , [73] . Our studies of FH-binding and M proteins underline the difficulty in making such extrapolations . Indeed , our data show that the in vivo role of FH-binding remains unclear , although it has been taken for granted that this interaction promotes phagocytosis resistance and acute virulence in S . pyogenes . This conclusion is particularly surprising , because the binding of a human complement regulator , FH or C4BP , emerges as a property shared by many HVRs , suggesting that these interactions enhance virulence ( Figure 8 ) . Thus , our data provide intriguing new information concerning the HVR in M proteins , while suggesting that new experimental systems may be needed to identify the biological role of bound FH .
The M1 strain S . pyogenes SF370 [74] and its isogenic Δemm1 mutant [75] , referred to here as ΔM1 , were from M . A . Kehoe . The S . pyogenes M3 strain 950771 and its isogenic Δemm3 mutant 296 , referred to here as ΔM3 , were from M . Wessels [76] . S . pyogenes strain M5 Manfredo [77] was from M . A . Kehoe . The isogenic mutant strains ΔM5 , ΔN1 , ΔN2 , ΔB , and ΔC have been described [17] , [20] . S . pyogenes JRS4 ( M6 ) , and its isogenic mutant strains JRS145 ( ΔM6 ) and JRS251 ( M6ΔC ) were from J . R . Scott [19] . S . pyogenes 87-282 ( M18 ) and its isogenic Δemm18 strain 282 KZ ( referred to here as ΔM18 ) were from M . Wessels [78] . All S . pyogenes strains were grown without shaking in Todd-Hewitt broth supplemented with 0 . 2% yeast extract ( THY ) , in 5% CO2 at 37°C . Unless otherwise stated , the S . pyogenes cultures were cultivated overnight in medium supplemented with the SpeB inhibitor E64 ( Sigma ) , used at 10 µM , to avoid degradation of M protein by the secreted SpeB protease that may be present in stationary phase cultures [56] . Escherichia coli XL1 Blue and DH5α were used for cloning and strain BL21 for protein production . E . coli was grown in LB at 37°C with shaking and supplemented with 100 µg/ml ampicillin when appropriate . Human FH was from Complement Technology , Inc . This FH , which contains both the Y402 and H402 variants , was used in all experiments , unless otherwise stated . Pure human Y402 FH was purified by affinity chromatography from human serum containing only Y402 FH , using an immobilized construct derived from M proteins ( manuscript in preparation ) . Pure chimeric FH was similarly isolated by affinity chromatography of serum from Tg mice expressing the Y402 variant . Mouse FH was affinity purified from EDTA-plasma on a HiTrap column ( GE Healthcare ) containing the anti-mouse FH mAb 2A5; protein was eluted with glycine-HCl pH 2 . 5 and immediately neutralized and dialyzed ( C . Harris , in preparation ) . Purification of human C4BP was described in [41] . Human Fg was from Enzyme Research Laboratories . All recombinant M proteins and M protein fragments , except M4 and M22 , were produced as GST-tagged proteins and purified on GSTrap columns according to the manufacturer's instructions ( GE Healthcare ) . After removal of the GST moiety , these recombinant proteins included the N-terminal sequence GPLGS , not present in the original protein . For preparation of the GST-tagged proteins , PCR products were cloned into BamHI-EcoRI cleaved pGEX-6P-2 ( GE Healthcare ) . The genes and gene fragments were amplified from S . pyogenes chromosomal DNA , employing the strains described under Bacterial strains and media , using primers listed in Table S1 as follows: M1-HVR ( M1-F/M1HVR-dim-R ) , M3 ( M3-F/M3-R ) , M5-HVR ( M5-F/M5HVR-dim-R ) , M5 ( M5-F/M5-R ) , M6-HVR ( M6-F/M6HVR-dim-R ) , M6 ( M6-F/M6-R ) , M6-Crep ( M6C-F/M6C-dim-R ) , M18-HVR ( M18-F/M18-HVR-dim-R ) and M18 ( M18-F/M18-R ) . The Pwo DNA polymerase was used for all PCR reactions according to the manufacturer's instructions ( Roche ) . The sequence was confirmed for all cloned PCR fragments . Purification of recombinant M1 protein was described in [79] , and the recombinant M4 and M22 proteins were described in [80] . For the preparation of an M5 mutant protein with a deletion corresponding to amino acid residues 80-86 ( M5Δ80-86 ) , two PCR fragments were first generated , one with the primer pair M5-F and M5Δ80-86REV , the other with the pair M5Δ80-86FWD and M5-R . A longer PCR fragment encoding M5Δ80-86 was generated by overlap extension PCR , using the two first PCR fragments and primers M5-F and M5-R . This fragment was cloned into pGEX-6P-2 . The deletion protein M5Δ87-93 was prepared by a similar procedure , employing the primer pair M5-F and M5Δ87-93REV , and the pair M5Δ87-93FWD and M5-R , followed by overlap extension PCR using primers M5-F and M5-R . This procedure was also followed for the generation of a PCR product encoding a deletion variant of M6 lacking amino acid residues 97-110 ( M6Δ97-110 ) . In this case , the two first PCR fragments were generated with primer pairs M6-F/M6Δ97-110REV and M6Δ97-110FWD/M6-R , respectively , and overlap extension PCR was performed with primers M6-F and M6-R . The recombinant HVRs derived from the M1 , M5 , M6 and M18 proteins contain the first 91 , 121 , 129 , and 108 amino acids , respectively , of the corresponding mature M proteins , while the M6-Crep construct comprises residues 228-363 of the mature M6 protein . To allow covalent dimerization of these purified M protein fragments , the recombinant forms contained a C-terminal cysteine not present in the intact M protein . For this purpose , a cysteine codon was added in the corresponding DNA constructs . After removal of the GST tag , the HVRs were dimerized as described [26] . Antisera against the dimerized M5 , M6 and M18 HVRs were raised by subcutaneous immunisation of rabbits with 100 µg pure protein in complete Freund's adjuvant , followed by two 50 µg boosters in incomplete Freund's adjuvant four and eight weeks after the first immunisation . The rabbits were bled two weeks after the final booster . A similar procedure was used to raise antiserum against highly purified human C4BP [41] . Sheep anti-human FH IgG ( The Binding Site ) was used to detect human FH , and rabbit anti-human Fg ( Dako , Denmark ) was used to detect Fg . Wt mouse FH and chimeric FH expressed by transgenic mice was detected with a mouse anti-mouse FH monoclonal antibody ( designated 2A5 ) targeting mouse SCR1-4 ( C . Harris , in preparation ) . Bound mouse Ig was detected with secondary rabbit anti-mouse Ig ( Dako , Denmark ) . The S . pyogenes bacteria were harvested from overnight cultures , washed twice with TBS-T ( 50 mM Tris , 0 . 15 M NaCl , 0 . 05% Tween-20 , pH = 7 . 4 ) , and resuspended in the same buffer . For each of the dimerized HVRs derived from M5 , M6 and M18 , 600 µg was coupled to a 1 ml HiTrap NHS-activated HP column according to the manufacturer's instructions ( GE Healthcare ) . For the generation of a control column , reactive groups were inactivated with ethanolamine . Outdated human citrate plasma , purchased from Lund University Hospital Blood Centre , was converted to serum by dialysis against 50 mM Tris-HCl pH 7 . 2 , 137 mM NaCl , 2 . 7 mM KCl , 5 mM CaCl2 at 4°C . The clot was removed and the serum was frozen until use . Mouse ( C3H/HeN ) and rabbit sera were obtained after coagulation of freshly drawn blood . Prior to use , frozen sera were thawed and particulate matter was removed by filtration ( 0 . 45 µm ) . Column chromatography was performed at 4°C . The columns were initially equilibrated with 10 column volumes of PBS . The various sera ( 1 . 5 ml for Figure 3C and 0 . 5 ml for Figure 4A ) were diluted 3 times in PBS and applied with a flow rate of 0 . 055 ml/min , followed by washes with 10 column volumes of PBS at a flow rate of 1 ml/min . Protein was eluted in 5 ml 6 M guanidine-HCl , dialysed against PBS and concentrated 10-fold . The assays were performed essentially as described [33] , using hirudin as anticoagulant with freshly drawn human blood from “nonimmune” donors , i . e . blood allowing rapid growth of the M-positive strains studied . The assay employed a very small inoculum of log-phase bacteria , grown in medium without E64 . After rotation at 37°C for 3 h , the multiplication factor was calculated for each strain . Assays were performed with blood from three different donors ( M1 ) or two donors ( M3 ) . The transgenic mice used ( on the C57Bl/6 background , bred as hemizygotes ) express a chimeric human/mouse FH in which SCRs 6–8 are derived from human FH , with a tyrosine residue at position 402 [48] . Infection experiments were performed with the S . pyogenes M5 Manfredo strain , using log-phase bacteria grown in medium without E64 . For analysis of the effect of the chimeric FH on spleen colonization ( Figure 7C ) , male Tg and wt male litter mates were challenged i . p . with a sublethal dose of bacteria ( 2×106 cfu ) . Mice were sacrificed 18 h post challenge , when spleens were homogenized and analyzed for the presence of bacteria by standard pour-plate methods . For analysis of the effect of the chimeric FH on lethal infection , two studies were performed . In one study ( Figure 7D ) , female Tg and wt female litter mates were challenged i . p . with an ∼LD90 dose ( 2 . 0×107 cfu ) of M5 bacteria and survival was followed . In the second study ( Figure 7E ) , the mice used were male and received a lower dose of M5 bacteria ( 0 . 9×107 cfu ) . For studies with non-Tg mice ( Figures 7F and G ) , pure human Y402 FH or whole human serum was administered i . p . to C3H/HeN mice 4 h before and also simultaneously with the i . p . administration of a sublethal dose of bacteria ( 2×107 cfu for mice of this inbred strain ) . Spleens harvested after 18 h were analyzed for the presence of bacteria . In the experiment with pure FH , the mice ( female ) received human Y402 FH ( 2×100 µg ) or PBS; the FH administered together with the bacteria was preincubated with the bacteria for 30 min at RT before challenge . In the experiment with human serum , the mice ( male ) received 200 µl at the two time points . Control mice received mouse serum . Protein G and streptavidin were purchased from Sigma and radiolabeled as described [81] . Biotinylation of pure recombinant M5 , M6 and M18 proteins was performed using the EZ-Link Sulfo-NHS-LC-Biotinylation kit according to the manufacturer's instructions ( Pierce ) . Western blot and detection of bound antibodies with radiolabeled protein G was performed as described [81] . Mass spectrometric identification of purified proteins was performed by the SCIBLU Proteomics Resource Centre at Lund University ( details available on request ) . N-terminal sequencing of proteins was performed by Alphalyse , Denmark . Protein sequence alignments were performed using the ClustalW program ( http://www . ebi . ac . uk/Tools/msa/clustalw2/ ) . Results from binding and phagocytosis assays are presented as mean values with SD from three independent determinations . In Figure 7 , the horizontal lines within the boxes represent the median . The boxes represent the interquartile ranges , IQR , and the t-bars the lowest normal datum still within 1 . 5 IQR of the lower quartile , and the highest normal datum still within 1 . 5 IQR of the upper quartile . To compare cfu numbers between groups , the Mann-Whitney U test was used . The statistical analyses were performed in SPSS Statistics 18 for Windows ( IBM Corporation , Somers , NY , USA ) . Significance ( p<0 . 01 ) is indicated by ** . Studies with human serum employed outdated human citrate plasma , converted to serum as described above . The plasma samples , which were anonymized , were purchased from Lund University Hospital Blood Centre , with permission ( 2012:04 ) . Phagocytosis tests were performed with blood samples obtained from human volunteers , with permission from the Ethical Review Board of the Medical Faculty , Lund University ( 2012/290 ) and with written informed consent from the donors . Animal experiments were performed with permission from the Animal Experimental Ethics Committee at Lund District Court ( M23-08; M286-09; M284-09; M129-11; M34-12 ) . Experimental infections were performed in a level P2 biohazard laboratory within the animal facility of Department of Laboratory Medicine , Lund University , and were governed by the following directive , law and provisions: Council directive EG 86/609/EEC , the Swedish Animal Welfare Act ( 1988:534 ) and the Swedish Animal Welfare Ordinance ( 1988:539 ) . Provisions regarding the use of animals for scientific purposes: DFS 2004:15 , DFS 2005:4 , SJVFS 2001:91 , SJVFS 1991:11 .
|
The human complement system may be rapidly activated upon infection and thereby plays a key role in innate immunity . However , activation must be tightly controlled , to avoid attack on self tissues . A key component of this control system is the plasma protein factor H ( FH ) . Many pathogens bind FH , as first described for Streptococcus pyogenes , and it has been proposed that the surface-localized M protein of this bacterium “hijacks” FH to escape phagocytosis . However , it remains unclear whether FH-binding to M protein indeed protects S . pyogenes against phagocytosis and promotes bacterial growth in vivo . Here , we demonstrate that FH binds to some but not all M proteins and solely binds to the hypervariable region ( HVR ) , a part of M protein important for virulence . Nevertheless , several lines of evidence , including studies with transgenic mice , indicated that FH-binding ability did not contribute to acute virulence or phagocytosis resistance . These data shed new light on the HVR of M proteins but underline the difficulty in determining the in vivo role of a ligand-binding region . Binding of FH may contribute to S . pyogenes virulence by mechanisms not assessed in currently used models .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"bacteriology",
"medicine",
"complement",
"system",
"protein",
"interactions",
"streptococci",
"immunology",
"microbiology",
"host-pathogen",
"interaction",
"group",
"a",
"streptococcal",
"infection",
"bacterial",
"biochemistry",
"bacterial",
"diseases",
"immunochemistry",
"bacterial",
"pathogens",
"immune",
"system",
"proteins",
"infectious",
"diseases",
"proteins",
"medical",
"microbiology",
"microbial",
"pathogens",
"biology",
"pathogenesis",
"immune",
"system",
"gram",
"positive",
"biochemistry",
"plasma",
"proteins",
"immunity",
"innate",
"immunity"
] |
2013
|
Factor H Binds to the Hypervariable Region of Many Streptococcus pyogenes M Proteins but Does Not Promote Phagocytosis Resistance or Acute Virulence
|
Mutations affecting the heritable maintenance of epigenetic states in maize identify multiple small RNA biogenesis factors including NRPD1 , the largest subunit of the presumed maize Pol IV holoenzyme . Here we show that mutations defining the required to maintain repression7 locus identify a second RNA polymerase subunit related to Arabidopsis NRPD2a , the sole second largest subunit shared between Arabidopsis Pol IV and Pol V . A phylogenetic analysis shows that , in contrast to representative eudicots , grasses have retained duplicate loci capable of producing functional NRPD2-like proteins , which is indicative of increased RNA polymerase diversity in grasses relative to eudicots . Together with comparisons of rmr7 mutant plant phenotypes and their effects on the maintenance of epigenetic states with parallel analyses of NRPD1 defects , our results imply that maize utilizes multiple functional NRPD2-like proteins . Despite the observation that RMR7/NRPD2 , like NRPD1 , is required for the accumulation of most siRNAs , our data indicate that different Pol IV isoforms play distinct roles in the maintenance of meiotically-heritable epigenetic information in the grasses .
Plants have two DNA-dependent RNA polymerases , Pol IV and Pol V , in addition to the ubiquitous eukaryotic polymerases I , II , and III . Pol IV and Pol V arose specifically in land plants from an ancient duplication of the catalytic largest and second largest subunits of Pol II [1] . Subunits for these plant-specific RNA polymerases ( RNAPs ) were originally identified in the Arabidopsis genome [2] and subsequently in genetic screens for factors involved in small RNA-mediated transgene silencing [3] , [4] . In Arabidopsis , mutations in the loci encoding the largest or shared second largest subunits of Pol IV and Pol V do not affect viability or development but do have distinct molecular effects on small RNA silencing pathways [5] , [6] . Pol IV is required for the accumulation of 24 nt RNAs while Pol V produces non-coding RNA transcripts at low levels [5] , [7] , [8] . In maize , the largest subunit ( NRPD1 ) of the presumed Pol IV functions with Required to Maintain Repression1 ( RMR1 ) , a Snf2-domain containing protein , and Mediator of Paramutation1 ( MOP1 ) , a putative RNA-dependent RNA polymerase related to Arabidopsis RDR2 , to maintain meiotically-heritable epigenetic states at the purple plant1 ( pl1 ) and colored plant1 ( b1 ) loci [9]–[12] . The pl1 and b1 loci encode transcriptional activators of anthocyanin biosynthesis . Specific alleles of pl1 and b1 , namely Pl1-Rhoades and B1-Intense , exist in distinct epigenetic states characterized by different pigment levels . The Pl-Rh and B-I states are highly expressed and confer dark pigmentation to plant tissues while the Pl' and B' states reflect a corresponding reduction in pigmentation and RNA levels [13] , [14] . When combined in Pl-Rh/Pl' or B-I/B' heterozygotes , alleles originally in the highly expressed Pl-Rh or B-I state heritably acquire the weak expression of Pl' and B' , respectively , and these repressed states are faithfully maintained in subsequent generations [13] , [14] . This interaction between alleles on homologous chromosomes is the hallmark of a process known as paramutation [15] . Normal functions of the nrpd1 , rmr1 and mop1 loci are required in trans to maintain somatic repression of the Pl' and B' states [10] , [16] , [17] . Although B' states are always meiotically transmitted [18] , recessive mutations identifying individual nrpd1 , rmr1 , and mop1 loci allow Pl' states to heritably revert to Pl-Rh at different frequencies [10] , [11] , [16] , [17] . By tracking the behavior of individual Pl1-Rhoades alleles transmitted from plants of Pl'/Pl-Rh genotypes , it appears as though only NRPD1 and MOP1/RDR2 are required to mediate the allelic interactions needed to acquire a Pl' state [10] , [11] , [16] . NRPD1 , RMR1 , and MOP1/RDR2 function in a presumed RNA-directed DNA methylation ( RdDM ) pathway that produces 24 nt small RNAs and maintains cytosine methylation patterns of loci represented by those small RNAs , many of which are repetitive elements [9] , [11] , [19] . Individual nrpd1 , rmr1 , and mop1 mutants display reductions of 24 nt siRNAs levels [9] , [11] , [19] and hypomethylation of cytosines at a repetitive feature 5′ of the Pl1-Rhoades promoter [11] . However , no methylation differences have been observed in this region between the Pl-Rh and Pl' states [11] , and RMR1 is not required to mediate the allelic interaction necessary to acquire a Pl' state [11] . These results indicate that an RdDM-type pathway is not the causative mechanism directing paramutation in maize . The role of NRPD1 and a presumed Pol IV RNAP in effecting paramutation thus remains unclear . Here we show that the required to maintain repression7 ( rmr7 ) locus encodes a protein related to the second largest subunit ( NRPD2a ) of Arabidopsis Pol IV and Pol V . It is unclear whether this presumed subunit ortholog functions exclusively in a maize Pol IV complex because multiple NRPD2-encoding loci were identified in the genome of maize and other grasses . The loss of 24 nt small RNAs in rmr7 mutants parallels the phenotype of maize nrpd1 mutants [9] indicating that this protein is necessary for functions ascribed to Pol IV . However , additional genetic and molecular comparisons between rmr7 and nrpd1 mutants indicate that alternative NRPD1-containing complexes with non-overlapping functions are required for the maintenance of heritable epigenetic information in maize .
Because plants that are homozygous for Pl' states exclusively have weak pigmentation patterns , darkly pigmented mutants are easy to identify . In homozygous condition , all rmr-type mutations phenocopy Pl-Rh homozygotes relative to Pl'/Pl' siblings [10] , [17] . In an ongoing genetic screen for ethylmethane sulfonate ( ems ) -induced rmr-type mutations [10] , [17] , we identified three single locus recessive mutations ( ems9750 , ems98939 , and ems062905 ) that failed to complement each other but which all complemented mutations defining the previously characterized nrpd1 ( rmr6 ) , rmr1 , and mop1 loci ( Protocol S1 and Table S1 ) . Results of these genetic tests indicate that the new recessive mutations define a novel locus , provisionally designated as rmr7 . The ems9750 , ems98939 and ems062905 mutations identify the rmr7-1 , rmr7-2 and rmr7-3 alleles respectively . To begin the evaluation of rmr7 defects on Pl1-Rhoades behaviors , darkly pigmented individuals homozygous for each of the rmr7 mutant alleles were crossed to Pl'/Pl' plants . All 22 F1 plants derived from a total of two crosses with rmr7-1/rmr7-1 parents had a clear Pl'-like anther phenotype ( variegated pigment ) . Similarly , all 12 F1 plants derived from a cross with a rmr7-2/rmr7-2 parent had variegated anthers . Three rmr7-3/rmr7-3 individuals were crossed to a total of 11 different Pl'/Pl' plants and all 176 F1 plants had Pl'-type anthers . These data indicate that the Pl1-Rhoades alleles transmitted from these homozygous rmr7 mutants are not recalcitrant to subsequent paramutation in the next generation and they confirm that the identified rmr7 mutations define recessive alleles . The rmr7 locus was located to the distal half of the short arm of chromosome 2 ( 2S ) using B-A translocations to induce segmental monosomic progeny ( see Protocol S1 ) [20] . Plants carrying rmr7-1 in either homozygous or heterozygous combination were pollinated by a series of Pl'/Pl' plants , each heterozygous for a different B-A translocation chromosome ( TB-1La , TB-2Sb , TB-4Lc , TB-5La , TB-8Lc , TB-9Sd , TB-10L19 ) . Such crosses generate a proportion of progeny that are segmentally monoploid for the respective A segment and can therefore be used to locate recessive mutations to specific chromosome positions [20] . All segmental monoploids generated with this set of B-A translocations display 50% pollen abortion and specific monoploids often have characteristic morphological phenotypes [21] . Although such monoploids were found in the progenies of all crosses , only those generated from the TB-2Sb translocation had anther phenotypes identical to those of rmr7-1/rmr7-1 homozygotes ( Protocol S1 and Table S2 ) . In total , 15 of 138 F1 plants derived by crossing two rmr7-1 heterozygotes by two independent Pl'/Pl'; TB-2Sb heterozygotes had darkly pigmented anthers and all 15 had the plant phenotype and 50% pollen abortion characteristic of 2S monoploids . One putative rmr7-1/- monoploid ( 02-365-54 ) was self-pollinated and 15/15 progeny were fully fertile and had Pl-Rh-like anthers . These data indicate that rmr7 maps to 2S , distal to the TB-2Sb breakpoint ( ∼2S . 5 ) [20] . We tested the possibility that 2S monoploids themselves affect paramutation occurring at the Pl1-Rhoades allele by repeating the B-A crosses using near isogenic A632 females that were either Pl'/Pl' or Pl-Rh/Pl-Rh . We found putative 2S monoploids in all progenies yet none had phenotypes distinct from those of Pl'/Pl' plants ( Table S3 ) . In the absence of any dosage-sensitive compensatory effects , these results indicate there are no loci on 2S that display haploinsufficiency with regard to either establishing or maintaining the Pl' state . Given the molecular identity of MOP1 and other RMR proteins [9] , [11] , [12] , [22] , we hypothesized that rmr7 might also encode a component in a RdDM-type pathway . A BLAST search of the maize genome sequence available on 2S identified a gene model encoding a protein with highest similarity to the second largest subunit of Pol IV and Pol V from Arabidopsis and containing peptide signatures representing all the conserved domains ( A–I ) found in similar subunits of all known RNAPs [23] . Genomic regions representing this candidate gene were amplified via polymerase chain reaction ( PCR ) and sequenced from homozygous mutant plants of all three ems-derived rmr7 alleles and the non-mutant progenitor . Three single transition-type lesions within the putative coding regions of the candidate gene were identified in mutant plants with respect to both the progenitor and B73 genomic sequences ( Figure 1A , Figure S1 ) . Two of these lesions create nonsense codons ( Figure 1B ) . The inferred peptide encoded by rmr7-1 lacks both conserved subunit domains C–I and the metal binding sites known to be critical for Saccharomyces cerevisiae RNAP catalysis and Arabidopsis Pol IV/V function [23] , [24] . The inferred peptide encoded by the rmr7-3 allele lacks the conserved domains that are required for contacts with the largest subunit in S . cerevisiae Pol II [23] . The lesion identified in rmr7-2 predicts an amino acid substitution of a glycine residue that is strictly conserved amongst all RNAP second largest subunits to glutamate ( Figure 1B , Figure S1 ) . These three lesions strongly indicate that rmr7 encodes an NRPD2-type protein hereafter referred to as NRPD2a . Correspondingly , the rmr7 locus and mutant alleles are renamed nrpd2a , nrpd2a-1 , nrpd2a-2 and nrpd2a-3 , respectively . BLAST searches identified two additional NRPD2-encoding gene models in the maize genome , ZM2G133512 on 10S and ZM2G128427 on 10L . 2S and 10L contain duplicated regions retained from an ancient tetraploidy event in maize [25] . We identified synteny between the chromosomal regions around nrpd2a and ZM2G128427 ( Figure 2A ) indicating these genes are homoeologs . No significant synteny was observed between the regions around nrpd2a and ZM2G133512 . Both ZM2G133512 and ZM2G128427 are predicted to encode full-length proteins with high amino acid sequence conservation to that encoded by nrpd2a ( 67 and 94% identity , respectively ) indicating that these loci likely produce functional NRPD2-type proteins . To determine if the expansion of genes encoding NRPD2-type proteins was unique to maize , we identified full-length predicted proteins from other plant genomes including the eudicots grape and poplar and the grasses Brachypodium distachyon , rice , and sorghum . These protein sequences were aligned ( Figure S1 ) , and a maximum likelihood tree was constructed ( Figure 2B ) using the second largest subunit of Pol II , NRPB2 , from Arabidopsis and rice as outgroups . This analysis indicates that retention of duplicated genes encoding NRPD2 proteins has occurred in grasses but not eudicots . Within the grasses , the NRPD2-type proteins fall into two distinct clades . Clade B contains single NRPD2-type proteins from each grass species while loci in clade A have undergone further duplications , one in maize and sorghum and two in the Brachypodium lineage ( Figure 2B ) . This diversity of NRPD2-type subunits implies that , in contrast to Arabidopsis , Pol IV and Pol V-type RNAPs in the grasses may not be defined by a shared second largest subunit . Mutations in nrpd1 reduce 24 nt RNA abundances to approximately 18% of non-mutant levels [9] in immature cobs . Similar profiles assessed on 6-day old seedlings show that the 24 nt RNA class is reduced to similar levels in both nrpd1 and nrpd2a mutants ( 14% and 15% , respectively ) ( Figure 3A ) and parallel profiles are seen in tissues from immature tassels ( Figure 3A ) . These results are consistent with the interpretation that both NRPD1 and NRPD2a are required for Pol IV function . No other NRPD2-type protein appears to compensate for the loss of NRPD2a function and hence this locus appears to provide the sole functional NRPD2-type protein utilized during early seedling and tassel development . This result validates the assignment of rmr7 as encoding a functional NRPD2-type protein . Previously , we found that individual components of the RdDM pathway have different effects on the accumulation of CRM Long Terminal Repeat ( LTR ) -type retrotransposon transcripts . In 4-day-old seedlings , the subclass of CRM2-type transcript levels are increased ∼6-fold in nrpd1 mutants while they are diminished or eliminated in both rmr1 and mop1/rdr2 mutants [26] . However , identical semi-quantitative RT-PCR analyses indicate that loss of NRPD2a has no obvious effect on the accumulation of CRM2–derived LTR RNAs in 4-day old seedlings ( Figure 3B ) . Densitometry measurements of ethidium bromide-stained bands from both prior results [26] and from Figure 3B indicate that the ratios of CRM2-derived LTR RNAs to Aat transcripts are 4 . 2 and 0 . 86 ( +/−0 . 24 s . e . m . ) in nrpd1 and nrpd2a mutants , respectively . Quantitative RT-PCR analyses confirmed that the relative levels of CRM2 RNA normalized to Pol II-derived Aat transcripts are similar in nrpd2a-1 mutants and heterozygous siblings ( Protocol S1 and Figure S2 ) . Applying two-sample z-test statistics to compare average ratios −/+ NRPD1 [26] and -/+ NRPD2a ( Figure S2 ) of Aat-normalized CRM2 LTR RNA levels , there is a significant effect of the nrpd1-1 mutation ( z = 3 . 35 , p<0 . 001 ) but not the nrpd2a-1 mutation ( z = −0 . 35 , p = 0 . 73 ) on CRM2 LTR RNA levels . These results indicate that while the NRPD2a subunit is necessary for 24 nt RNA accumulation , its absence does not completely mimic the loss of NRPD1 . The Pl' state can revert to a meiotically transmissible Pl-Rh state when Pl1-Rhoades is in hemizygous condition [27] , [28] or in all homozygous rmr mutants evaluated to date [10] , [17] . Pl1-Rhoades alleles of Pl-Rh state are often sexually transmitted from rmr1 , required to maintain repression2 , or nrpd1 mutant plants even though Pl1-Rhoades alleles of Pl' state were originally introduced into those mutants . Given that nrpd2a mutant plants—generated by either selfing Pl'/Pl'; Nrpd2a/nrpd2a plants , or by intercrossing Pl'/Pl'; Nrpd2a/nrpd2a and Pl1-Rhoades/Pl1-Rhoades; nrpd2a/nrpd2a siblings—display a plant phenotype indistinguishable from that of Pl-Rh/Pl-Rh plants ( Anther Color Score of 7; [13] ) , we expected that some Pl1-Rhoades alleles would be transmitted in Pl-Rh-like state from such plants . This expectation was not met as crosses between homozygous nrpd2a mutants and a series of Pl-Rh/Pl-Rh tester stocks gave rise to progenies exclusively showing anther phenotypes typical of Pl'/Pl' genotypes ( Anther Color Scores of 1—4; [13] Table 1 ) . Thus , similar to the comparisons made with CRM2-derived RNAs , NRPD2a defects are unlike those of NRPD1 with regards to maintaining the meiotically-heritable feature specific to paramutant Pl' states [10] . Additional tests were made with pollen from a single nrpd2a-2 homozygote crossed to Pl-Rh/Pl-Rh and Pl'/Pl' half siblings ( A632 Pl-Rh/Pl-Rh and spontaneously arising Pl'/Pl' siblings crossed to a W23 Pl-Rh/Pl-Rh stock ) . Both progenies consisted of individuals of exclusively Pl'-like phenotypes ( 28 and 27 individuals , respectively ) . Collectively , these results indicate that Pl1-Rhoades alleles are able to maintain some meiotically-heritable feature in nrpd2a mutants that allows them to retain the ability to facilitate paramutation in the next generation . However , following 5 generations of inbreeding via single seed descent , a similar Pl-Rh/Pl-Rh testcross using an A619 Pl-Rh/Pl-Rh stock gave rise to a progeny set that had 5 of 13 individuals displaying phenotypes typical of Pl-Rh/Pl-Rh plants indicating that reversion of Pl' to Pl-Rh can occur following multiple generations of conditioning in the absence of NRPD2a function . In B-I/B' heterozygotes , the strongly expressed B-I state invariably changes to a transcriptionally repressed B' state [14] . To ask whether B-I would acquire a B' state in the absence of NRPD2a function , B-I and B' states were combined in nrpd2a-1 homozygotes ( Figure 4A and 4B ) and then the B1-I alleles were evaluated for plant pigmentation function following transmission to recessive b1 allele testers ( Figure 4C ) . Reciprocal crosses between a single B-I Nrpd2a/b1 nrpd2a-1 plant and a single B' nrpd2a-1 homozygote were used to generate the progeny plants in which B-I and B' states were combined . As expected , pigment phenotypes of nrpd2a-1/nrpd2a-1 individuals were identical in both B'/B-I and B'/b1 genotypes ( Figure 4B ) . Based on the visual phenotypes of the test cross progenies , only B' states are transmitted from B' nrpd2a-1/b1 nrpd2a-1 plants ( Figure 4C; Table S4 ) . This result indicates that NRPD2a is not required to maintain the meiotically-heritable B' state and is in accord with similar tests of the effects of NRPD2a on the maintenance of Pl' states ( Table 1 ) . When B1-I alleles were transmitted from four individual B-I nrpd2a-1/B' nrpd2a-1 plants , both B'-like and significantly darker B-I-like test cross progeny phenotypes were found in approximately equal numbers ( Figure 4C , Table S4 ) . Since B-I invariably changes to B' in B-I/B' heterozygotes [14] , this exceptional result indicates that paramutation of B-I to B' in a B-I/B' heterozygote depends on NRPD2a function . However , B1-I alleles transmitted from two individual B' nrpd2a-1/B-I nrpd2a-1 plants gave exclusively B'-like testcross progeny phenotypes ( Figure 4C; Table S4 ) indicating just the opposite; that paramutation is not dependent on NRPD2a function . Interestingly , the difference between these two contrasting results correlated with the parent of origin for the B' state . When B' was transmitted through the homozygous B' nrpd2a-1 female , paramutation occurring within the subsequent B' nrpd2a-1/B-I nrpd2a-1 progeny was not prohibited , yet when B' was contributed from the homozygous B' nrpd2a-1 male , paramutation appeared to require NRPD2a function . These results indicate NRPD2a is conditionally required to acquire the B' state and stand in contrast to those of prior tests in which NRPD1 was found to be required for B-I to change to a B' state in B'/B-I , homozygous nrpd1 mutants , even though B' had been maternally transmitted to those mutants [10] . In sharp contrast to NRPD1 defects [9] , [29] , the Pl-Rh-like phenotype seen in homozygous nrpd2a-1 plants is not associated with any obvious developmental abnormalities . Field observations of F2 families segregating the nrpd2a-1 mutation as well as comparison of nrpd2a-1/nrpd2a-1 and nrpd2a-1/Nrpd2a individuals derived from intercrossing nrpd2a-1/nrpd2a-1 and nrpd2a-1/Nrpd2a F5 siblings show relative uniformity of plant morphology . Moreover , all four S4 lines of nrpd2a-1 homozygotes derived by single seed decent had excellent survivorship ( 77/80 seeds gave fertile plants ) and had morphologically normal plants . This stands in contrast to the strong degradation of plant quality documented for NRPD1 mutants [29] that rarely produce any seed past the S3 generation or any morphologically normal plants past the S2 generation . Similarly , field observations of F2 families segregating the nrpd2a-2 mutation as well as comparison of nrpd2a-2/nrpd2a-2 and nrpd2a-2/Nrpd2a individuals derived from intercrossing nrpd2a-2/nrpd2a-2 and nrpd2a-2/Nrpd2a F2 siblings show similar uniformity of type . Height measurements over 3 independent progeny sets show that Nrpd2a/nrpd2a-2 plants average 71 . 4 cm +/− 1 . 1 ( s . e . m . ; n = 24 ) whereas homozygotes average 68 . 3 cm +/− 1 . 1 ( s . e . m . ; n = 25 ) . These measurements are not statistically different from one another ( 2-sample z-test; z = 2 . 0; P>0 . 05 ) and there are currently no other compelling observations to indicate that the locus defined by either the nrpd2a-1 or nrpd2a-2 mutations is required for normal growth and development . These results parallel those of the CRM2 RNA abundances and those measuring the effects of NRPD2a on paramutations at the pl1 and b1 loci in indicating that the functions of maize NRPD2a and NRPD1 do not completely overlap .
Genetic screens in maize have identified two RNA polymerase subunits as required to maintain repressed epigenetic states associated with paramutation in maize . Previously , we identified rmr6/nrpd1 as encoding NRPD1 , the largest subunit of Pol IV [9] , and here we report the identification of rmr7/nrpd2a as encoding NRPD2a , the second largest subunit of Pol IV and , potentially , Pol V . Derived from Pol II , Pol IV and Pol V are functionally distinct RNAPs defined by their largest subunits , NRPD1 and NRPE1 , respectively [1] . The catalytic cores of these respective polymerases are created by physical interaction between the largest and second largest subunits ( NRPD2/NRPE2 ) . In Arabidopsis , Pol IV and V share a single second largest subunit , AtNRPD2a . Additional subunits are shared with Pol II , or exist in Pol IV and/or Pol V-specific forms [30] . nrpd2a is one of three maize loci predicted to encode a protein similar to AtNRPD2a . Based on predicted protein alignments with S . cerevisae RPB2 , the additional maize NRPD2-type proteins are predicted to be functional . Further , all three nrpd2-type loci appear to express RNA more or less constitutively throughout growth and development [31] . This diversity of potentially functional NRPD2-type proteins is conserved throughout other grass species but not the representative eudicots . Previous phylogenetic analysis concluded that NRPD2 derived from a single duplication of RPB2 in the ancestor of land plants [1] . Our analysis of angiosperm NRPD2 sequences from complete or near complete genomes indicates that multiple nrpd2 locus duplications have occurred after the divergence of monocots and eudicots . Arabidopsis is the only representative eudicot with evidence of a nrpd2 locus duplication , yet only one functional locus has been retained from this recent event [3] , [4] , [32] , [33] . An nrpd2 locus duplication in the grass common ancestor resulted in two distinct and well-supported clades , A and B . Unlike Arabidopsis , all of these nrpd2-encoding loci appear to be functional . The relative timing of this duplication corresponds with a whole genome duplication that occurred in the cereal genome prior to the divergence of rice , Brachypodium , sorghum , and maize [34] . Accordingly , the rice clade A ( Os04g54840 ) and clade B ( Os08g07480 ) loci are located in the homoeologous r8-r4 chromosomal segments retained from this duplication [35] . Within clade A , further nrpd2 duplications have been retained in individual species lineages . The two maize clade A loci , homoeologs nrpd2a and ZM2G128427 , are located in regions syntenic with sorghum chromosome 6 [35] , the location of clade A locus Sb06g030300 . However , the additional sorghum clade A locus , Sb01g042100 , is in an asyntenic region on chromosome 1 [35] indicating that the duplication in sorghum occurred independently of that in maize . The maize clade A duplication is consistent with a tetraploidy event which occurred after the divergence of maize and sorghum [36] , [37] while the sorghum clade A duplication corresponds to a small-scale event occurring post-divergence [37] . The origins of the Brachypodium duplications are unclear , as no large scale duplications have been proposed in that lineage , but the high degree of amino acid similarity ( 98 . 5% ) between Bd_6 . 650 and Bd_2 . 4317 indicates that this duplication was relatively recent . While both the eudicot and grass lineages have undergone genome duplication events [38] , only the grasses have retained potentially functional NRPD2-type duplicates . This general observation indicates that grasses have a fundamentally different type of polymerase biology relative to eudicots . One possibility is that the additional NRPD2-type proteins interact with both Pol IV and Pol V , as in Arabidopsis , but in a semi-redundant fashion . Complete functional redundancy is inconsistent with recessive loss-of-function lesions at the nrpd2a locus , but perhaps the individual NRPD2-type subunits overlap only for certain RNAP functions . Alternatively , the A and B clades identified in the phylogenetic tree could represent a functional division between NRPD2 proteins that participate in either Pol IV , Pol V or in RNAPs that are specific for different tissues or developmental time points . Regardless , the grasses clearly support a potentially greater diversity of RNAP complexes than the representative eudicots examined here . Functional analyses of nrpd2a mutations are consistent with the idea that grasses have a greater diversity of functional RNAPs than those found in Arabidopsis . Like NRPD1 , NRPD2a is required for somatic maintenance of Pl' states and approximately 85% of all 24 nt RNA accumulation , consistent with a Pol IV-type function . However , loss of NRPD2a function does not completely mimic the loss of NRPD1 as nrpd2a mutants have unique molecular , genetic , and morphological phenotypes . These contrasting results indicate that NRPD2a is required for only a subset of presumed Pol IV functions and supports the hypothesis that maize , and perhaps other grasses , utilize functionally distinct Pol IV-type RNAPs defined by a shared NRPD1 together with one or the other NRPD2-type subunits . Although the DNA-dependent RNA polymerase responsible for the levels of CRM2 transcript seen here remains unknown , we have previously shown that these non-polyadenylated CRM2 RNA levels are decreased in both rmr1 and rdr2 mutants , thereby indicating that they are primarily products of an RNA-dependent RNA polymerase [26] . We have proposed that the increased poly-adenylated CRM2 RNAs observed in nrpd1 mutants are due to increased Pol II transcription in the absence of Pol IV competition for the CRM2 LTRs [26] . In non-mutant conditions , Pol IV facilitates repression of Pl1-Rhoades by inhibition of Pol II , and we have hypothesized that this interference occurs either through direct competition for initiation sites or by titration of shared RNAP subunits [9] . The results presented here indicate that there are functionally distinct Pol IV-type RNAPs , those that require NRPD2a ( for 24 nt RNA accumulation ) and those that do not ( for inhibition of Pol II ) . Either one or the other NRPD2-type proteins define these functionally distinct complexes or perhaps NRPD1 can act independently of a RNAP holoenzyme . A gain-of-function nrpd2a mutation that could dominantly interfere with all NRPD1-containing complexes would be predicted to have phenotypic overlap with nrpd1 mutants . While no such dominant alleles have been identified in our mutational screens ( 0/15 , 000 M1 plants ) , Sidorenko et al . [31] report on a semi-dominant mutant allele ( Mop2-1 ) identifying the same locus as nrpd2a that predicts a single amino acid change in the terminal domain presumably required for interaction with NRPD1 . Since our evaluation of 2S segmental aneuploids indicate that the nrpd2a locus is haplosufficient , the dominant nature of the Mop2-1 allele is unlikely to be simply due to a dosage effect . Homozygous Mop2-1 mutants do have a developmental phenotype reported to be similar in some respects to that displayed by nrpd1 mutants [31] and this may indicate that the NRPD2a variant encoded by Mop2-1 poisons multiple RNAP complexes . While the identification of NRPD2a has highlighted the additional RNAP complexity of maize relative to Arabidopsis , it is still unclear how a presumed Pol IV ortholog , and the greater RdDM machinery , affects paramutation . Several distinct but overlapping conceptual functions are required for paramutation including 1 ) acquisition of a repressed epigenetic state through unknown trans-homolog interactions , 2 ) somatic maintenance of such acquired repressed states , and 3 ) meiotic transmission of paramutant states [10] . As our forward genetic screens specifically looked for failure to maintain repressed Pl' states somatically , all RNAP and RdDM factors identified to date are required for this function . However , with pedigree analyses of mutant materials , it is clear that these individual factors have different effects on the acquisition and heritable maintenance of paramutant states [10] , [11] , [16] , [17] . Functionally distinct Pol IV-type complexes might help explain why NRPD2a has a different phenotype from NRPD1 with regard to meiotic transmission of Pl' and the acquisition of B' states . Since NRPD2a is required for 24 nt RNA accumulation , the small interfering class of small RNAs ( siRNAs ) do not appear to be essential for the meiotic transmission or acquisition of paramutant states , although cumulative loss of siRNAs over several generations might result in failure to maintain meiotic transmission . Similarly , RMR1 , which is also required for siRNA production , is not required for the allelic interaction required to acquire paramutant states even though it is required to some extent for the meiotic transmission of paramutant states [11] . NRPD1 is required for both siRNA production and meiotic transmission of paramutant Pl' states but its role in repressing Pol II activity may be the more critical function with regard to the acquisition of paramutant states . The apparent conditional requirement of NRPD2a for the establishment of B' states could indicate that an NRPD2a-containing RNAP is necessary in pollen to successfully convert B-I to B' . Alternatively , the parent of origin effect we observed could be due to somatic mosaicism with spontaneous change of the B-I allele to B' in the soma giving rise to the apical tassel but not to the lateral ear shoot , or perhaps the nrpd2a-1 mutation imparts a semi-dominant effect that was manifest in the relatively small sample size evaluated here . While such behaviors remain a possibility , we have not observed these types of effects in similar tests of B' establishment [10] . From the analyses of maize and Arabidopsis mutants , it is clear that the evolution of Pol IV and Pol V-type RNAPs facilitated unique mechanisms for epigenetic repression in plants . While models for Pol IV and Pol V function have been generated in Arabidopsis , it will be important to determine how applicable they will be in the cereal crops . The inferred increased diversity of RNAPs combined with enormous expansion of repetitious sequences in large genome cereals provides a potential basis for the innovation of regulatory novelty . A further understanding of the mechanistic relationship between paramutation and maize RNAP diversity promises to illuminate how such features have been co-opted during evolution and domestication of the grasses .
The maize Pol IV largest subunit has been designated “RPD1” in two prior publications from this laboratory . The addition of the letter “n” to all plant RNA polymerase loci , genes , and proteins was substituted during the production process . Following standard conventions ( http://www . maizegdb . org/maize_nomenclature . php ) , maize loci are designated in lower-case italics ( i . e . pl1 ) . Specific recessive alleles are designated with a dash , followed with a descriptor of the allele , usually the inbred line from which the allele originated ( i . e . pl1-B73 ) . Dominant alleles begin in uppercase lettering ( i . e . Pl1-Rhoades ) . Translocation breakpoints are indicated with a “T” and paramutagenic states with a prime symbol ( ' ) . Plant phenotypes displayed by particular states of Pl1-Rhoades are written in non-italic text ( i . e . Pl' ) . All diploid genotypes are presented with pistillate ( female ) -derived factors first and staminate ( male ) -derived factors second . Hand pollinations were used for all crosses . Pl1-Rhoades expression was visually assessed for each progeny individual as described [13] . BLAST searches identified sequence similar to Arabidopsis nrpd2a on maize BAC c0009N09 ( Genbank accession AC191113 ) on chromosome 2S , and a gene model was predicted using maize EST sequence ( Genbank accession AY104560 ) and FGENESH+ . Oligonucleotide primers spanning the predicted coding region ( Table S5 , Sigma-Genosys , www . sigmaaldrich . com/Brands/Sigma_Genosys . html ) were designed from this sequence and used in PCR reactions with genomic DNA isolated from plants homozygous for the three nrpd2a mutant alleles and the non-mutant progenitor line . PCR amplicons were purified using the QIAquick gel extraction kit ( Qiagen , www . quiagen . com ) and at least two independent amplicons were dideoxy sequenced ( UC Berkeley DNA Sequencing Facility , mcb . berkeley . edu/barker/dnaseq/ ) . Intron/exon boundaries were verified by amplification from cDNA and subsequent sequencing . cDNA sequences can be retrieved from Genbank using accession numbers GQ356034 through GQ356037 . NRPD2-type sequences from diverse plant lineages were obtained through tblastn searches of Phytozome ( www . phytozome . net ) and BrachyBase ( http://blast . brachybase . org/ ) using ZmNRPD2a or AtNRPD2a as queries . When necessary , gene models were predicted with FGENESH+ ( www . softberry . com ) using similarity to either ZmNRPD2a or AtNRPD2a . Some sequence names were altered for clarity in Figure 2 . Full-length names are as follows: AtNRPB2 = At4g21710 , OsNRPB2 = Os03g44484 , AtNRPD2a = At3g23780 . 1 , PtNRPD2 = XP_002324332 , VvNRPD2 = XP_002283296 , for maize sequences add GRM to the beginning of the sequence name , for Brachypodium sequences substitute super for Bd . Sequences were aligned using MAFFT ( http://align . bmr . kyushu-u . ac . jp/mafft/online/server/ ) and a maximum likelihood tree was constructed with Phyml ( http://mobyle . pasteur . fr/cgi-bin/portal . py ) using the JTT amino acid substitution model and NNI+SPR tree topology search operation . The tree was edited with Dendroscope 2 . 2 . 2 [39] . The alignment was edited using GeneDoc 2 . 6 . 04 as previously described [9] . Homoeologous regions were identified using ESTs , simple sequence repeat markers , and genes to identify sequence similarity on chromosomes 2S and 10L . Features used and the corresponding BACs they identify are as follows: AY111545 ( AC206980 , AC190732 ) , nrpd2a ( AC191113 ) , ZM2G128427 ( AC199156 ) , AY112227 ( AC209428 , AC197497 ) , AY110965 ( AC215994 , AC204716 ) , AY105682 ( AC186195 , AC183941 ) , AY109473 ( AC177886 , AC214263 ) , p-umc44b , p-umc44a , b1 ( AC191025 ) , r1 ( AC199387 ) . Sibling mutant and non-mutant whole seedlings were identified by tissue coloration 6 days post-imbibition and pooled for RNA extractions . Tassel branches were collected prior to anthesis from mutant and non-mutant siblings and used for RNA extractions . Small RNA fractions were enriched from total RNA and visualized following polyacrylamide gel electrophoresis ( PAGE ) as previously described [9] . Relative 24 nt RNA abundances were quantified by normalizing to 21 nt RNAs as previously described [9] . Total RNA was extracted from whole seedlings 4 days post-imbibition by Trizol ( Invitrogen ) purification . Oligo ( dT ) -primed cDNA was generated as previously described [9] . Random primed cDNA was generated using 1 µg of total RNA that was reverse transcribed with the Superscript III enzyme ( Invitrogen ) in the presence of 250 pmol of random hexamers in a 20 µL reaction . CRM2 LTR cDNA sequence was then amplified via PCR with previously described primers [40] . The alanine aminotransferase ( Aat ) cDNA was PCR amplified using previously described primers [9] . The PCR program used is as follows , repeated 30 times: 94°C for 30 sec , 57°C for 30 sec , and 72°C for 45 sec . Relative CRM2 abundances were quantified by normalizing to Aat RNAs as previously described [9] .
|
Multicellular plants possess a unique set of DNA–dependent RNA polymerase complexes ( RNAPs ) that prevent certain repetitious regions of the genome from being copied into stable RNAs . Two distinct RNAPs , termed Pol IV and Pol V , are required for this type of genome-silencing behavior in the eudicot Arabidopsis thaliana , but the mechanism by which these RNAPs accomplish this function is still relatively unknown . Using genetic and molecular methodologies , we identified a Pol IV–type subunit protein as being involved in a process of meiotically-heritable gene silencing in the maize plant known as paramutation . Our analyses of the available plant genome sequences indicate that monocots have a greater potential for RNAP diversity due to having duplicate variants of this particular subunit . Consistent with this inferred diversity , comparative analyses with plants defective in a different core Pol IV subunit indicate that the Pol IV–type RNAP in maize has distinct functional isoforms . The mechanistic and biological role ( s ) of these specific RNAPs in mediating genome regulation and heritable gene silencing in large genome cereals should now be tractable by biochemical approaches .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"plant",
"biology/plant",
"genomes",
"and",
"evolution",
"plant",
"biology",
"genetics",
"and",
"genomics",
"genetics",
"and",
"genomics/plant",
"genomes",
"and",
"evolution",
"plant",
"biology/plant",
"genetics",
"and",
"gene",
"expression",
"genetics",
"and",
"genomics/plant",
"genetics",
"and",
"gene",
"expression"
] |
2009
|
Diversity of Pol IV Function Is Defined by Mutations at the Maize rmr7 Locus
|
Important control efforts have led to a significant reduction of the prevalence of human African trypanosomiasis ( HAT ) in Côte d’Ivoire , but the disease is still present in several foci . The existence of an animal reservoir of Trypanosoma brucei gambiense may explain disease persistence in these foci where animal breeding is an important source of income but where the prevalence of animal African trypanosomiasis ( AAT ) is unknown . The aim of this study was to identify the trypanosome species circulating in domestic animals in both Bonon and Sinfra HAT endemic foci . 552 domestic animals ( goats , pigs , cattle and sheep ) were included . Blood samples were tested for trypanosomes by microscopic observation , species-specific PCR for T . brucei sl , T . congolense , T . vivax and subspecies-specific PCR for T . b . gambiense and T . b . gambiense immune trypanolysis ( TL ) . Infection rates varied significantly between animal species and were by far the highest in pigs ( 30% ) . T . brucei s . l was the most prevalent trypanosome species ( 13 . 7% ) followed by T . congolense . No T . b . gambiense was identified by PCR while high TL positivity rates were observed using T . b . gambiense specific variants ( up to 27 . 6% for pigs in the Bonon focus ) . This study shows that domestic animals are highly infected by trypanosomes in the studied foci . This was particularly true for pigs , possibly due to a higher exposure of these animals to tsetse flies . Whereas T . brucei s . l . was the most prevalent species , discordant results were obtained between PCR and TL regarding T . b . gambiense identification . It is therefore crucial to develop better tools to study the epidemiological role of potential animal reservoir for T . b . gambiense . Our study illustrates the importance of “one health” approaches to reach HAT elimination and contribute to AAT control in the studied foci .
Human African trypanosomiasis ( HAT ) or sleeping sickness is a vector borne parasitic disease caused by Trypanosoma brucei gambiense ( T . b . gambiense ) in West and Central Africa and T . b . rhodesiense in East Africa . T . b . gambiense is responsible for 98% of all HAT cases reported in the last decade and remains an important public health concern in sub-Saharan Africa [1] . However , with less than 3000 cases reported in 2015 [2] , HAT elimination seems an achievable goal [3] . A similar situation occurred in the 1960s but , after an early sense of victory , the disease reemerged . Gambiense HAT is generally considered as an anthroponotic disease , but the absence of animal reservoirs has never been demonstrated . The existence of an animal reservoir for T . b . gambiense could be one of the factors that causes reemergence of the disease after successful control campaigns [4] . In Côte d’Ivoire , significant efforts to control the disease over the past three decades have been made and drastically reduced the prevalence of HAT [5] . The last epidemic was contained in the Sinfra and Bonon foci at the early2000s [6–8] . Despite continuous control efforts , few HAT cases are still passively diagnosed from these two foci as well as from historical foci of the Western-Centre part of the country [7–9] . Transmission persistence may be due to the existence of a residual chronic human and/or animal reservoir of T . b . gambiense in these areas where tsetse flies are still present [10–13] . Several studies have highlighted the importance of wild and domestic animals in the transmission cycle of T . b . rhodesiense [14 , 15] , but this is still under debate for T . b . gambiense . Noteworthy , the presence of T . b . gambiense in domestic and wild animals have been reported in Cameroun [16] and Equatorial Guinea [17–19] . In Côte d’Ivoire , such studies are scarce and the last report dates from the early 2000s in which the authors investigated the presence of trypanosomes in pigs in the Bonon HAT focus . High prevalence of T . brucei s . l . was observed but the presence of T . b . gambiense in this animal species remained unclear [20] . It is crucial to increase the efforts in studying the existence of an animal reservoir of T . b . gambiense as this could compromise HAT elimination . No data are available regarding the prevalence of T . b . brucei , T . congolense and T . vivax animal African trypanosomiasis ( AAT ) , in the Bonon and Sinfra foci , despite that animal breeding represents an increasing source of food and income in these areas with high human population densities . In the context of the one health approach that was recently suggested for HAT and AAT control [21] , the aim of the present study was to characterize trypanosomes circulating in domestic animals in the HAT foci of Bonon and Sinfra in Côte d’Ivoire . We used Trypanosoma species-specific PCR assays , microsatellite genotyping and immune trypanolysis ( TL ) with three variant antigenic types ( VAT ) of which two are specific for T . b . gambiense [22] . We show that T . brucei s . l . and T . congolense were the most prevalent trypanosome species in the two foci and that pigs and cattle were the most infected animals , with T . brucei s . l . and T . congolense respectively . Discordant results were observed between the T . b . gambiense specific PCR and TL tests and the existence of an animal reservoir of T . b . gambiense thus remains unclear .
The study was carried out in September/October 2013 in the Sinfra and Bonon areas , which are located in the western-central part of Côte d’Ivoire ( Fig 1 ) . In recent decades , the mesophyle forest has been progressively replaced by cash crops ( mainly cocoa and coffee , but also bananas , cassava , rice and yam ) leading to a favorable environmental context for HAT development in these areas [23] . The evolution of HAT prevalence in Côte d’Ivoire is well documented since the 1950s . The number of cases diagnosed from 2000 to 2010 ( Fig 1A ) shows that these two foci were the most endemic during this period . Control efforts conducted from 1995 till present could largely contain the epidemic [7 , 11] but few cases are still passively diagnosed each year [7] . Based on the last 10 HAT cases who were diagnosed from 2011 to 2013 , we identified 8 and 10 study sites in the Sinfra and Bonon foci , respectively ( Fig 1B ) . These 18 study sites ( less than 10 km from the last detected HAT cases ) are expected to be those where transmission is still active and where we had the highest chance to detect T . b . gambiense in domestic animals . We focused our study on cattle ( Zebu ) , goats , sheep and pigs since they are the most common domestic animals in the study areas . They are mainly bred in the periphery of villages or along the small rivers crossing the villages , where tsetse flies are often abundant [10 , 12 , 24–26] . Generally , sheep , goats and cattle freely graze during the day and are kept in enclosures at night while pigs freely roam day and night . For each animal , 5ml of blood was taken from the jugular vein . Parasitological diagnosis was performed in the field by microscopic examination using the buffy coat technique ( BCT ) [27] . BCT was considered positive when trypanosomes could be visually detected regardless of the species . In addition , 1 ml of plasma and 1 ml of blood were aliquoted and immediately frozen at -20°C during transport and subsequently at -80°C in the lab for PCR and immune trypanolysis testing . DNA from 500 μL of blood was extracted using the DNeasy Tissue kit ( Qiagen , Valencia , CA , USA ) as described previously [28] and subjected to diagnostic PCR assays using Trypanosoma species specific primers for T . brucei s . l . ( TBR1-2 ) [29] , T . congolense forest type ( TCF1-2 ) [30] , T . congolense savannah type ( TCS1-2 ) [31] , T . vivax ( TVW1-2 ) [30] . Positives samples for T . brucei s . l were tested for T . b . gambiense using primers targeting the TgsGP gene [32] . All PCR reactions were carried out using 5 μl of DNA template in a reaction volume of 50 μL 1xPCR reaction buffer comprising 0 . 2 mM of dNTP , 0 . 2 μM of each primer and 2 . 5 U of Taq polymerase . A positive control was added to the corresponding PCR and distilled water was used as negative control . The PCR products were visualized by electrophoresis in a 2% agarose gel stained with GelRed and illuminated with UV light . Samples positive in the T . brucei s . l . specific TBR PCR were further characterized by seven microsatellite markers Ch1/18 , Ch1/D2/7 [33] , M6C8 [34] , Micbg5 , Micbg6 , Misatg4 , Misatg9 [35] , as previously described [36] . Reference stocks of T . b . gambiense ( n = 18 ) , T . b . gambiense group 2 ( n = 3 ) , T . b . brucei ( n = 1 ) and T . b . rhodesiense ( n = 1 ) were included . A neighbor-joining tree was computed under multiple sequence alignment ( MSA ) [37] with Mega 5 [38] on a Cavalli-Sforza and Edward's chord distance matrix [39] as recommended by Takezaki et al . [40] . Plasma samples were analyzed with the immune trypanolysis test ( TL ) using cloned populations of T . b . gambiense variant antigen type ( VATs ) LiTat 1 . 3 , LiTat 1 . 5 and LiTat 1 . 6 as previously described [22 , 41] . LiTat 1 . 3 and LiTat 1 . 5 VATs are reported to be specific for T . b . gambiense , while LiTat 1 . 6 VAT can both be expressed in T . b . gambiense and T . b . brucei [22] . All statistical analyses were done with JMP11 ( SAS Institute ) . Proportions of positive animals for BCT , PCR and TL were compared regarding foci and host species using the Chi-square analysis . Sample collection was conducted within the framework of epidemiological surveillance activities supervised by the HAT National Elimination Program ( HAT NEP ) . No ethical statement is required by local authorities . Any veterinarian may carry out blood sampling on domestic animals , with the authorization of the owner , as it is performed during prophylaxis or diagnostic campaign . No samples other than those for routine screening and diagnostic procedures were collected . Breeders gave their consent for animal sampling after explaining the objectives of the study . For animal care , venous sampling was performed by a veterinary of the Laboratoire National d’Appui au Développement Rural ( Ministry of Agriculture ) . A deworming treatment ( Bolumisol , Laprovet ) was provided free to all animals sampled and those positive with BCT were treated for trypanosomiasis .
Out of the 552 animals sampled , 57 trypanosome infections ( 10 . 3% ) were detected by BCT ( Table 2 ) . Highest prevalence was observed in pigs ( p<0 . 0001 ) with a prevalence of almost 30% . No significant differences were observed between the two foci in the infection rates ( Fig 2A ) . No animals were positive with the TCS specific primers . In total , 109 trypanosome infections ( 19 . 7% ) were detected with at least one PCR ( TBR , TCF or TVW ) with the highest prevalence observed in pigs ( 41 . 6% , Table 2 ) . T . bucei s . l . was the most prevalent trypanosome species ( 13 . 8% ) followed by T . congolense forest type ( 8 . 5% ) and T . vivax ( 2 . 4% ) . No T . vivax infection was detected in pigs . Mixed infections with positive results in at least two different PCR assays were observed in 29 animals . Most were mixed infections with T . bucei s . l . / T . congolense forest type and mainly observed in pigs ( 59% ) . TBR , TCF and TVW PCR results for the different hosts in both foci are presented in Fig 2B , 2C and 2D . Pigs and cattle showed higher TBR and TCF-PCR positivity rates compared to sheep and goats . No significant differences were observed between the two foci except for TBR PCR positivity rates in cattle . The highest PCR positivity rate was observed in pigs in Sinfra ( 43 . 3% ) . All 76 DNA samples positive in the TBR PCR were further analyzed with the T . b . gambiense specific TgsGP PCR and all were negative . Among the 76 DNA samples positive in the TBR PCR , only 10 showed amplification in the microsatellite genotyping assays . The NJTree presented in Fig 3 presents a classical shape ( e . g . [42] ) ) with one monophyletic lineage that gathers all T . b . gambiense reference stocks and rake for other subspecies . No trypanosome genotypes from the domestic animals sampled in Bonon and Sinfra foci are members of this T . b . gambiense lineage . Trypanolysis results for the three VAT types per host in the two foci are presented in Fig 4 . No TL positive results were observed in goats and sheep with LiTat 1 . 3 and 1 . 5 VAT in both foci and only two goats and two sheep were positive with the LiTat 1 . 6 VAT . The LiTat 1 . 6 TL assay showed high positivity rates ( more than 35% ) in pigs in both foci and in cattle ( more than 40% ) in the Bonon focus , confirming the PCR-TBR results . We observed the same pattern , but with lower positivity rates , in the T . b . gambiense-specific LiTat 1 . 3 and 1 . 5 TL assays ( 0% for both 1 . 3 and 1 . 5 in cattle in Sinfra , 17 . 6 and 5 . 9% for 1 . 3 and 1 . 5 respectively in cattle in Bonon and between 15 . 8 and 27 . 6% for both 1 . 3 and 1 . 5 in pigs in the two foci ) . The difference observed in cattle between Bonon and Sinfra is significant for Litat 1 . 3 . The distribution of LiTat 1 . 3 and/or LiTat 1 . 5 TL positive results in cattle ( 7 individuals = 8% ) and pigs ( 39 individuals = 28 . 5% ) is given in Table 3 . Out of the seven cattle which tested positive to either LiTat 1 . 3 or LiTat 1 . 5 , only one was positive to both variants ( 13 . 3% ) . This proportion was much higher in pigs with 21 of 39 ( 53 . 9% ) positive animals tested positive to both variants . From these 21 pigs , 15 ( 10 . 9% ) were also positive to LiTat 1 . 6 and thus positive to the three VAT types . Animals testing positive to LiTat 1 . 6 only were also observed for all domestic animal species but with higher proportion in cattle ( 13 . 8% ) and pigs ( 22 . 6% ) . Nineteen of the 46 samples ( 43 . 3% ) testing positive to Litat1 . 3 and/or 1 . 5 were negative in all species specific PCR assays ( TBR , TCF and TVW ) . Positivity to Litat1 . 3 and/or 1 . 5 was the highest in animals testing positive to the TBR PCR ( 30 . 6% ) , it was zero in animals positive only to TVW PCR , but positive in 4/23 ( 17 . 4% ) animals that were uniquely positive to TCF PCR ( Fig 5 ) .
In this study , we showed that domestic animals are important carriers of trypanosomes in the Sinfra and Bonon HAT foci . T . brucei s . l . infections were highest in pigs in Sinfra and both in pigs and cattle in Bonon . The overall prevalence recorded by PCR was approximately twice the one observed with the parasitological BCT technique . This was expected due to the known higher sensitivity of PCR [28 , 43] . Infection rates were higher in pigs and cattle than in sheep and goats . This may be related to differences in the host-vector contacts associated with different breeding practices and/or to differential host susceptibility to trypanosome infection . Sheep rather graze in the vicinity of the houses where they are partly nourished by the villagers , limiting the contact with tsetse flies . Goats are browsing freely across the vegetation in the periphery of villages and may be more exposed to tsetse flies . Noteworthy , low trypanosome infection rates have already been described for goats and this was attributed partly to the fact that goats are known to express individual defensive behavior against the bites of tsetse flies [43 , 44] . Cattle forage across long distances to reach pastures and are thus potentially more exposed to tsetse flies . However , in the Sinfra focus , the areas crossed by cattle herds are particularly anthropized with lower tsetse fly infestations . In contrast , in Bonon , the forest was more recently exploited and cattle are still in contact with tsetse populations [10] . This probably explains why cattle infection rates in Bonon are much higher than in Sinfra . Pigs roam freely in the more humid and shady areas around the villages or along the small rivers and are highly exposed to tsetse flies . Moreover , pigs have already been described as a preferential feeding host for Glossina palpalis palpalis [26 , 45] , the only tsetse species present in the studied areas [26] . In the Sinfra and Bonon areas , the forest was progressively replaced by cocoa , coffee , banana plantations and food crops offering potential favorable conditions to the introduction of T . congolense savannah type from the north of the country where prevalence of this species is high [46] . However we did not detect this trypanosome which is considered as the most pathogenic congolense type [47] , in the framework of this study . The prevalence of T . vivax was also low with only 13 infections , mainly detected in sheep . Noteworthy , no T . vivax infections were detected in pigs , confirming that this host is generally refractory to this trypanosome species [48] . T . congolense forest type was found in all host animals as previously observed in other forest areas [49] and mainly detected in pigs . T . brucei s . l . was the predominant species detected in our study areas , which is in line with studies conducted in other HAT forest foci from Cameroon and Equatorial Guinea [17 , 50] . Among the T . brucei s . l . infections , T . b . gambiense could not be detected by the TgsGP PCR nor by microsatellite genotyping . At first sight , this could indicate that the human pathogenic trypanosome is not circulating in domestic animals in the HAT foci of Bonon and Sinfra . Similar results were reported in northwest Uganda where authors concluded an apparent absence of domestic animal reservoir for T . b . gambiense [51] . However , we observed high positivity rates ( up to 28% ) with both the T . b . gambiense specific LiTat 1 . 3 and LiTat 1 . 5 TL assays . Positivity to more than one T . b . gambiense variant is an indicator that the host was infected for a sufficient amount of time to allow VSG switching . Together , the high prevalence of LiTat 1 . 3 and/or 1 . 5 positivity observed in pigs and the fact that more than half of the animal tested positive to both variants , are thus pointing out that pigs could be potential reservoirs of T . b . gambiense in the study area . Since very few HAT cases have been identified in these foci in the last years ( Fig 1 ) , the TL results may suggest an active circulation of T . b . gambiense in pigs and/or cattle in the Sinfra and Bonon foci , but with little contact with humans . In most studies describing the presence of T . b . gambiense in wild and domestic animals , prevalence in animals is often higher than in humans [18 , 19 , 50 , 52] . This may be explained by a higher nutritional preference of tsetse flies for animals and by the fact that human are less exposed to tsetse flies in these areas . This is consistent with previous observations describing the protective role of pigs living at the periphery of villages since pigs are the preferential host of tsetse flies [24–26 , 53] . The TL results obtained in our study are consistent with recent population genetics data which showed that natural populations of T . b . gambiense are more important than those evidenced by the classical medical survey , suggesting hidden reservoirs of these parasites [42 , 54] . In the same way , a recent modeling study with animal and human data from a Cameroon focus showed that transmission of T . b . gambiense could not be maintained by humans as the only reservoir [55] . The existence of a domestic animal reservoir for T . b . gambiense could be responsible for the sporadic cases diagnosed in most of the Ivorian forest foci , sometimes a long time after the last epidemic HAT episode [7] . We observed a high discordance between the T . b . gambiense PCR and TL results . The T . b . gambiense specific TgsGP PCR targets a single copy gene [56] and may not be sensitive enough to exclude presence of T . b . gambiense in case of negative result , given the low parasitaemia generally observed in T . b . gambiense infections [57 , 58] . In addition , the microsatellite genotyping assays are known to lack sensitivity [59] . It is thus possible that T . b . gambiense parasites could have been missed by these two methods . In addition , the skin could be an important anatomical reservoir of T . b . gambiense as was recently demonstrated in experimentally infected mice [60–62] . We can thus not exclude that animals with negative PCR results on blood have trypanosomes in other body compartments such as the dermis . It is also possible that the presence of LiTat 1 . 3 and/or LiTat 1 . 5 specific antibodies indicates a previous transient infection . In addition , cross reactions of Litat 1 . 3 and 1 . 5 with other trypanosome species cannot be excluded . Bromidge et al . showed in 1993 that a PCR targeting the LiTat 1 . 3 gene showed a positive result in two stocks described as T . brucei brucei isolated from pigs in Côte d’Ivoire [63] . We thus cannot exclude that T . b . brucei strains circulating in our study areas are expressing the LiTat 1 . 3 VAT resulting in TL positive results . In the context of HAT elimination , it will be crucial to improve the sensitivity of the T . b . gambiense PCR in order to detect this species in biological samples of animals , tsetse flies and human . Further studies are also needed to validate the specificity of LiTat 1 . 3 and 1 . 5 TL assays in animals . Controlled infection experiments in domestic animals will be needed to more fully evaluate the specificity and sensitivity of the currently available tools and evaluate their usefulness in research on the role of animals in the transmission of T . b . gambiense . Our data suggest the existence of a potential domestic animal reservoir for T . b . gambiense HAT and provides indications for areas where the transmission may occur in the Sinfra and Bonon foci: the small wet and shady areas around the villages and the forest relics . Vector control using tiny targets [64 , 65] which are particularly favorable to disrupt the contact between domestic animals and tsetse flies may have a considerable impact on tsetse fly densities and trypanosome transmission in these areas . The concomitant treatment of pigs in the Sinfra focus and both pigs and cattle in the Bonon focus would furthermore help clearing out the potential reservoir of T . b . gambiense but also would contribute to animal trypanosomiasis control . This clearly illustrates the usefulness of applying a one health strategy for both HAT and AAT control . However , these control measures have to be adapted to the study area and epidemiological context . In the endemic focus of Boffa [66] and in the historical focus of Loos Islands [67] in Guinea , domestic animals seem not to play a role in the epidemiology of HAT . In Cameroun [49 , 50 , 68] , Congo [69] , and Equatorial Guinea [17–19] , the presence of human and animal infecting trypanosomes in domestic and/or wild animals could be evidenced , but important differences between study areas were highlighted regarding prevalence in hosts and their geographical distribution . We thus suggest , in low prevalence foci where HAT elimination seems reachable , to conduct animal surveys to define the most appropriate control measures to be implemented .
We have investigated the distribution of animal trypanosomes and the possible existence of a domestic animal reservoir of T . b . gambiense in two hypo-endemic HAT foci in Côte d’Ivoire . Our results show that T . brucei s . l . and T . congolense forest type circulate in the study areas and mainly infect pigs and cattle . Discordant results were obtained on the presence of T . b . gambiense , between PCR and TL methods . PCR did not detect T . b . gambiense while high seroprevalence was observed in TL . In the context of HAT elimination , it will be crucial to further investigate this discordance and to develop better tools and strategies to fully characterize the epidemiological role of an animal reservoir for T . b . gambiense .
|
In Africa , significant efforts to control human African trypanosomiasis ( HAT ) over the past three decades have drastically reduced the prevalence of the disease and elimination seems today an achievable goal . However , potential animal reservoirs of Trypanosoma brucei gambiense may compromise this ambitious objective . In the Bonon and Sinfra HAT endemic foci in Côte d’Ivoire , no recent data are available about the prevalence of animal African trypanosomiasis ( AAT ) . The aim of this study was to identify trypanosomes circulating in domestic animals in these two HAT foci using serological , parasitological and molecular tools . We showed that T . brucei s . l . and T . congolense were the most prevalent trypanosome species and that pigs and cattle were the most infected animals . Discordant results were observed between the T . b . gambiense specific molecular and serological tools and the presence of an animal reservoir for T . b . gambiense remains unclear . Nevertheless , improved control strategies can be proposed based on this study to reach HAT elimination and contribute to AAT control in the study areas .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion",
"Conclusion"
] |
[] |
2017
|
The study of trypanosome species circulating in domestic animals in two human African trypanosomiasis foci of Côte d'Ivoire identifies pigs and cattle as potential reservoirs of Trypanosoma brucei gambiense
|
Human enterovirus 71 ( EV71 ) is a significant cause of morbidity and mortality from Hand , Foot and Mouth Disease ( HFMD ) and neurological complications , particularly in young children in the Asia-Pacific region . There are no vaccines or antiviral therapies currently available for prevention or treatment of HFMD caused by EV71 . Therefore , the development of therapeutic and preventive strategies against HFMD is of growing importance . We report the immunogenic and safety profile of inactivated , purified EV71 preparations formulated with aluminum hydroxide adjuvant in preclinical studies in mice and rabbits . In mice , the candidate vaccine formulations elicited high neutralizing antibody responses . A toxicology study of the vaccine formulations planned for human use performed in rabbits showed no vaccine-related pathological changes and all animals remained healthy . Based on these preclinical studies , Phase 1 clinical testing of the EV71 inactivated vaccine was initiated .
Hand , Foot , and Mouth Disease ( HFMD ) is caused by several members of the human enterovirus A ( HEV-A ) group . It is generally a self-limiting infection affecting mostly children and is characterized by ulcers and vesicles on the hands , feet and oral cavity . However , a more severe form of disease may occur with neurological symptoms such as meningitis , encephalitis , polio-like paralysis , and brain stem encephalitis leading to pulmonary edema and death [1]–[3] . Since the mid-1990s , HFMD infections caused by human enterovirus 71 ( EV71 ) have resulted in significant morbidity and mortality , particularly in the Asia-Pacific region [4] , [5] . China , Viet Nam , and Singapore reported increased activity in January–May 2012 compared to the same period in 2011 , including 22 deaths to date in Viet Nam [6] . In addition , HFMD outbreaks disrupt education and economic activities due to school and childcare center closures in efforts to control disease transmission [7] . Human enterovirus A belongs to the Picornaviridae family of non-enveloped , positive-sense RNA viruses , which also includes polioviruses and rhinoviruses . Members of the HEV-A group which cause HFMD include EV71 and Coxsackievirus A16 ( CAV16 ) [8] . HFMD outbreaks due to EV71 infection have the greatest propensity to cause severe neurological disease . Experimental infection of cynomolgus macaques showed that strains isolated across several decades were all neurotropic , as well as showing a broader tissue tropism than polioviruses [9] . Enterovirus 71 has four capsid proteins ( VP1–VP4 ) and seven nonstructural proteins . In addition to protecting the viral RNA , the capsid proteins recognize receptors on the surface of host cells and contribute to the antigenic profile of the virus [8] . Known human cell surface receptors for EV71 are the scavenger receptor B2 ( SCARB2 ) , and the P-selectin glycoprotein ligand 1 ( PSGL-1 ) [10] , [11] . Although the classical method of typing enteroviruses by serum neutralization defines EV71 as a single serotype [12] , current molecular typing methods reveal that several genogroups have been circulating in the Asia-Pacific region at least since the 1990s [13] . EV71 isolates were previously classified into genogroups A , B , and C and sub-genogroups based on VP1 nucleotide sequences alone [14]; nucleotide sequence identity of the VP1 gene is >92% within genogroups , whereas nucleotide sequence identity between the genogroups is 78–83% [4] . However , whole-genome sequencing resulted in the reclassification of subgenogroup B5 under B4 and addition of genogroup D; the authors suggested that the 3D polymerase sequence together with VP1 better represented whole genomes [15] . Recombination between genogroups and with other Human enterovirus A serotypes also occurs [16] . At present there is no specific antiviral therapy or vaccine available against EV71 . Intravenous immunoglobulin has been used in severe HFMD cases , with some therapeutic benefit suggested by the outcomes but as yet unproven by clinical trials [3] , [17] . Preventive and control measures during EV71 outbreaks are limited to surveillance , closure of educational and childcare facilities , and isolation of patients . Candidate vaccines under development include formaldehyde-inactivated whole-virus vaccine [18] , [19] , VP1 subunit vaccines [20] , [21] , a peptide-based synthetic vaccine [22] , a recombinant bacterial-vectored VP1-based vaccine [23] , a plasmid DNA vaccine expressing VP1 [24] , virus-like particles of EV71 [25] , and a live –attenuated vaccine [26] . Common findings include the necessity for an adjuvant [27] , and the use of whole virus particles as opposed to recombinant proteins or short peptides alone [28] . Human clinical trials of inactivated virus vaccines producing high neutralizing antibody titers have been conducted in China [29] , [30] , Taiwan [31] , and Singapore ( Inviragen , manuscript in progress ) . In the following studies , we describe the development of an inactivated EV71 candidate vaccine in animal models and evaluation of its immunogenicity and safety in preparation for human clinical trials .
Animal studies were performed at the following institutions and approved by the respective animal care and use committees: Alhydrogel adjuvant study at University of Wisconsin-Madison ( approved by UW-Madison Institutional Animal Care and Use Committee , under USDA Animal Welfare Act ) ; determination of optimum immunogenic dose at Shanghai Genomics ( under Chinese regulatory guidelines ) ; rabbit GLP ( Good Laboratory Practice ) toxicology study at Jai Research Foundation ( approved by JRF Institutional Animal Ethics Committee , under “Guidelines for Laboratory Animals Facility” , CPCSEA , India ) . The genogroup B EV71 strain , MS/7423/87 ( GenBank accession number U22522 . 1 ) was selected to prepare the candidate vaccine on the basis of amino acid sequence similarity to highly immunogenic strains as well as high yield in Vero cell culture . The candidate vaccine strain was grown in Vero cells ( WHO Reference Cell Bank 10-87 ) in 10-tier cell factories as described below , using Dulbecco's Modified Eagle's Medium ( DMEM; Sigma-Aldrich , United States ) without serum . Small scale preparations for neutralization assays were grown in Vero clone E6 cells ( ATCC CRL-1586 ) in tissue culture flasks ( BD Biosciences , United States ) using Minimum Essential Medium ( MEM; Sigma-Aldrich , United States ) with 2% fetal bovine serum ( FBS; Biological Industries , Israel ) . Confluent monolayers of Vero cells in 10-tier cell factories ( Nalge Nunc International , United States ) were infected with the EV71 vaccine strain at a multiplicity of infection ( MOI ) of 0 . 1 to 0 . 01 . When cytopathic effect ( CPE ) was complete , the medium was harvested , clarified by filtration , followed by treatment with benzonase ( Merck Chemicals , Germany ) to remove host cell nucleic acid at 20 U/mL , 37°C for 24 hours . The clarified harvest was treated with freshly prepared “binary” ethyleneimine ( BEI ) at 1 . 5% v/v , 37°C for 6 hours to inactivate EV71 infectivity [32] , followed by the addition of 150 mM sodium thiosulfate to hydrolyze unreacted ethylenimine . Inactivation of viral infectivity was confirmed at this stage by blind-passaging the material twice on Vero cells . Tissue culture medium components and low molecular weight proteins were partially removed by concentration and diafiltration against phosphate-buffered saline ( Invitrogen , United States ) containing 0 . 002% Tween 80 ( Merck Chemicals , Germany ) ( PBST ) . The inactivated virus preparation was further purified by chromatography using ion-exchange and size-exclusion columns ( details of the chromatography process are proprietary ) , concentrated using a 30 kDa molecular weight cut-off membrane , and finally sterilized by filtration through a 0 . 2 µm membrane . Purified inactivated EV71 antigen was stored at −80°C in suspension in PBST . The presence and estimated purity of EV71 antigen in samples were evaluated by SDS-PAGE and Western blotting . Briefly , samples were heated to 95°C in lithium dodecyl sulfate buffer ( Invitrogen ) with beta-mercaptoethanol ( Sigma-Aldrich ) and fractionated on duplicate 4–12% PAGE gels ( Invitrogen ) in MES-SDS buffer . For purity estimation , one gel was stained with colloidal blue ( Invitrogen ) followed by image intensity calculations using Quantity One software ( Bio-Rad ) . For detection of EV71 antigens , proteins were transferred to a PVDF membrane using the iBlot semi-dry blotting system ( Invitrogen ) and stained for the presence of VP2 and VP0 using the monoclonal antibody 422-8D-4C-4D ( Merck Millipore , catalogue number MAB979 ) , followed by an HRP-conjugated anti-mouse IgG secondary antibody ( DAKO ) and DAB substrate ( Sigma-Aldrich ) to visualise bands . The physical form of the EV71 antigen present was examined by transmission electron microscopy using a phosphotungstic acid negative stain on carbon formvar grids ( Electron Microscopy Services ) . In the two following mouse studies , male BALB/c mice between 4–6 weeks old were used . Volumes of 100 µL per dose were administered by intramuscular ( IM ) injection in the hind leg as two injections of 50 µL in the same leg , due the limitations on the volume of a single injection in mice . Immunogenicity of EV71 antigen formulated with or without aluminum hydroxide: Groups of mice ( n = 8 ) were injected on Days 0 and 28 with the following doses of purified inactivated EV71 antigen ( 0 . 12 µg , 0 . 6 µg , or 3 . 0 µg ) in PBST only or with aluminum hydroxide ( Alhydrogel “85” , Brenntag Biosector , Denmark ) at 0 . 5 mg ( aluminium content ) per dose . Two groups of control animals ( n = 8 ) were injected with PBST or Alhydrogel ( 0 . 5 mg per dose ) using the same immunization protocol as above . Blood samples were collected on day 0 , 28 , 42 , 56 , 91 , and 120; sera were stored at −20°C until testing for neutralizing activity . Determination of the optimum immunogenic dose: Groups of mice ( n = 10 ) were immunized on days 0 and 28 with the following doses of purified inactivated EV71: 0 . 12 µg , 0 . 6 µg , 3 µg , 9 µg , and 15 µg , formulated with Alhydrogel ( 0 . 5 mg in a volume of 100 µL per dose ) . A control group received Alhydrogel alone ( 0 . 5 mg per dose in a volume of 100 µL per dose ) . Blood samples were collected on day 0 , 28 , and 56; sera were stored at −20°C until testing for neutralizing activity . The toxicology study was conducted according to Good Laboratory Practice ( GLP ) at the Jai Research Foundation , India . New Zealand White rabbits were used as the test species with females to males at a 1∶1 ratio in each group . Low and high dose vaccines were formulated as planned for a Phase I human clinical trial , containing 0 . 6 µg and 3 . 0 µg EV71 antigen respectively , with 0 . 5 mg Alhydrogel in a 0 . 5 mL volume . Twenty rabbits received each vaccine formulation . Control groups received normal saline ( PBS , n = 12 ) or 0 . 5 mg Alhydrogel alone ( vaccine placebo , n = 16 ) . Test articles were administered by IM injection in the left thigh . All animals received a booster injection of the same dose and route on day 28; the animals in the high dose group received a second booster on day 42 . Animals were observed for morbidity and mortality at least twice daily during the treatment and observation periods , in addition to weekly clinical examinations . Parameters monitored were: Injection site reactogenicity ( Draize numerical scoring system ) , rectal temperature , body mass , food consumption ( grams/rabbit/week ) , ophthalmological examination , and respiratory rate . Half the animals of each sex in each group were sacrificed 2 days after the last immunization , either on Day 30 ( low dose , saline placebo , and Alhydrogel placebo groups ) or Day 44 ( high dose group ) ; the remaining animals were sacrificed on Day 56 . Blood samples were collected on Days 0 , 3 , 28 , 42 ( high dose group only ) , and 56 and used for clinical chemistry , hematology , and EV71 neutralizing antibody assays . Gross pathology examinations were performed on all sacrificed animals . A complete necropsy and histopathology examinations were performed on organs from the animals sacrificed on Day 30 ( saline placebo , Alhydrogel placebo , and vaccine low dose groups ) or Day 44 ( high dose group ) . Vero cells were seeded into 96-well microtiter plates at 104 cells per well in 100 µL growth medium ( MEM+10% FBS ) . Individual serum samples were heat-inactivated at 56°C for 30 min . Two-fold serial dilutions of serum samples in assay medium ( MEM+2% FBS ) were mixed with equal volumes of an EV71 ( vaccine strain ) suspension at 2000 units of 50% tissue culture infectious dose ( TCID50 ) per mL and incubated at 37°C for 1 . 5 hours . 100 µL of each serum-virus mixture was added to three wells ( final virus titre 100 TCID50 per well ) . Each well was scored for CPE at 5 days post-infection . The end-point neutralizing titer was defined as the highest serum dilution in which at least two of the three replicates were negative for CPE . Seroconversion was defined as a reciprocal neutralizing titer ≥128 [33] . Data from animal studies were compiled in Microsoft Excel and analyzed using Excel or Prism 5 ( GraphPad , Inc ) . Student's t-test was used on log-transformed reciprocal neutralizing titers to compare immunogenicity between groups . P values ≤0 . 05 were considered significant .
Western blotting with a monoclonal antibody specific for EV71 VP2 and VP0 proteins confirmed the presence of EV71 antigen in purified inactivated material ( samples from a typical batch shown in Figure 1 , left ) . The estimated purity of EV71 , based on image intensity measurements of colloidal blue-stained gels ( same samples in Figure 1 , right ) , was >80% in all batches using this process , typically 85–90% ( calculations not shown ) . Typical yields from the process outlined above were ≈300 µg of protein per liter of harvest . Electron microscopy revealed a mixture of filled and empty icosahedral capsids ( Figure 2 ) , consistent with the observation of VP0 in Western blotting . A particle concentration estimate performed by mixing the purified inactivated EV71 with a polystyrene bead standard ( micrograph not shown ) was approximately 3E+10 particles/mL . In a preliminary experiment we compared the immunogenicity of EV71 antigen inactivated by heat ( 56°C for 6 hours ) or chemically ( BEI ) in mice . The BEI-inactivated EV71 virus exhibited superior immunogenicity , inducing ∼6 . 4-fold higher neutralizing titres at 28 days post-immunization; the difference decreased to ∼1 . 5-fold higher at 56 days post-immunization ( data not shown ) . Subsequently , we tested the immunogenicity of BEI-inactivated , purified EV71 preparations in mice at different concentrations with or without adjuvant . The presence of aluminum hydroxide produced higher seroconversion rates at 28 days after the first dose ( Table 1 ) . Neutralizing antibody titers in groups receiving antigen with adjuvant , compared to groups receiving antigen alone ranged from 11-to-23-fold higher at day 28 ( after 1 dose ) , 4-to 6-fold higher at day 42 ( after 2 doses ) and 2 . 4-to 9-fold higher at day 56 ( two weeks after 2nd dose ) ( Figure 3 ) . The differences were significant at all dose levels ( P<0 . 05 ) . Follow-up to 91 and 120 days showed that high titers were sustained and did not significantly decrease from Day 56 ( Figure 3 ) . Following a single dose , only the 3 µg and the 15 µg dose levels elicited a high neutralizing antibody response by day 28 ( Figure 4 ) . Seroconversion rates for all groups were incomplete , ranging from 10% to 80% depending on the dose ( Table 2 ) . However , a booster immunization induced significantly ( P≤0 . 05 ) higher levels of neutralizing antibodies in all groups of animals ( Figure 4 ) . Twenty-eight days after the boost , only the lowest dose of 0 . 12 µg was significantly less immunogenic than the highest dose of 15 µg; doses from 0 . 6 µg and higher produced equivalent titers . In addition , 100% seroconversion was observed following the booster dose in animals receiving vaccine doses of 3 µg or higher ( Table 2 ) . A toxicological study using this animal model to test low and high dose vaccine formulations was conducted at a GLP contract research organization ( CRO ) . Clinical and histological findings were compiled by the CRO and reported to Inviragen . No morbidity or mortality occurred in any animals , and no skin reactions ( i . e . no erythema or edema ) at the site of injection were found in any animals . Hematology and clinical chemistry parameters remained within the normal range for NZW rabbits . Animals in the EV71 vaccine-treated groups exhibited no differences from the control groups in group averages of body mass , body mass change during the study period , or food consumption . At day 30 , males treated with Alhydrogel placebo had higher thymus weights compared to the saline placebo group . At day 56 , EV71 vaccine-treated females had higher adrenal , ovary/oviduct , and uterus weights compared to the Alhydrogel placebo group . However , no lesions of pathological significance were found on the reproductive or other internal organs . Muscular degeneration at the site of injection , with or without mononuclear cell/eosinophil infiltration , were observed in a sub-population of animals in all groups receiving formulations containing aluminum hydroxide including the Alhydrogel placebo group , but not in animals which received the saline placebo ( Table 3 ) . The lesions had healed or were markedly reduced by day 56 , i . e . 28 days after the last immunization for the Alhydrogel placebo and low dose groups , and 14 days after the last immunization for the high dose group .
Prompted by the successful control of poliovirus by formalin-inactivated vaccines , we produced a purified , inactivated , aluminum hydroxide-adjuvanted EV71 candidate vaccine . The inactivation method used leaves no detectable residual inactivant and completely eliminates viral infectivity within <24 hours while preserving antigenicity ( data not shown ) , thus is more efficient than formalin inactivation , which takes several days at 37°C [34] . BEI also better preserves the immunogenicity of the viral particles compared to heat inactivation , possibly because heating EV71 to ≥50°C triggers a conformational change in the particles [35] , [36] . The purification process using ion-exchange and size-exclusion chromatography does not separate empty procapsids from RNA-containing particles , or immature virions with VP0 from mature virions with cleaved VP2/VP4 , therefore the purified inactivated antigen preparations contain a mixed population of particles . However , empty particles from other EV71 candidate vaccine strains are known to be immunogenic albeit inducing somewhat lower neutralizing titres than full particles , the differences in immunogenicity being lesser at higher doses [34] . We then evaluated immunogenicity of several vaccine formulations using this antigen preparation in mice , followed by a repeated-dose toxicology study in rabbits . The route of administration , dose levels , dosing schedule , and selection of parameters to be monitored in the study were designed according to industry standard recommendations for evaluation of vaccine safety [37]; furthermore , the high dose group received an additional dose beyond the planned human dosing schedule . No clinical or hematological abnormalities , or pathological signs on internal organs were found . Injection site lesions were localised , microscopic , and transient , and consistent with aluminium salt-induced lesions reported by other investigators [38] . These lesions occurred in the Alhydrogel placebo group as well as the low and high dose EV71 vaccine groups , therefore they were considered to be due to the Alhydrogel adjuvant rather than EV71 antigen . The purified inactivated EV71 antigen preparation was shown to elicit neutralizing antibody responses to the homologous virus in mice , which were significantly increased by the addition of aluminum hydroxide adjuvant . Analysis of the neutralizing antibody responses induced by the various antigen concentrations established i ) the requirement of two doses for optimum immunogenicity and reliable seroconversion , and ii ) the selection of low ( 0 . 6 µg antigen per dose ) and high ( 3 µg ) doses . Currently , the protective anti-EV71 neutralizing antibody titer has not been determined for humans . However , it is interesting to note that the neutralizing antibody titers elicited by the inactivated vaccine in animals were well over the target titer of 128 and were sustained at high levels for over 3 months after the last immunization . This titer was the minimum protective antibody dose in a neonatal mouse passive immunization-challenge model of EV71 infection [33] . Subsequent to the studies described in this paper , a Phase I human clinical trial was conducted in adult volunteers ( Inviragen , manuscript in progress ) . A significant challenge in the development and evaluation of candidate EV71 vaccines has been the lack of a suitable animal model that mimics EV71 pathogenesis in humans . Although nonhuman primates are susceptible to EV71 infection [9] , [26] , cost and animal welfare considerations are barriers to use . Some neonatal mouse models have been established [39]–[41] , but most strains of mice become resistant to EV71 infection by a few days of age . The interferon receptor-deficient mouse strain AG129 has been found by other investigators to be susceptible to EV71 infection [42] . The candidate vaccine described in this paper was later tested in the AG129 model in both passive transfer and vaccination studies , demonstrating its efficacy for protecting against EV71 disease [43] . Adult mice that were immunized with 3 µg EV71 in Alhydrogel ( antigen dose equivalent to the planned clinical high dose ) were partially protected against morbidity and mortality following a primary vaccination , and fully protected following one booster . We did not extensively test cross-neutralization of heterologous strains of EV71 by animal serum in vitro; however , preliminary data from the Phase I clinical trial of this candidate vaccine showed increases following vaccination of neutralizing titers against the genogroup B2 vaccine strain in addition to genogroup B4 , C2 , C4 , and C5 isolates ( Inviragen , manuscript in progress ) . While neutralizing titers against heterologous strains may vary , there is not a straightforward relationship between antigenicity and the current system of genotyping especially among genogroup B and C strains [18] , [44] , as some neutralizing epitopes appear to be conformational rather than linear [45] . Antisera raised against various strains have been reported to be broadly cross-neutralizing [18] , [46] , [47] . Molecular studies also show that isolates across all EV71 genogroups share high predicted amino acid sequence identity including at the immunodominant neutralizing epitope in VP1 [14] , [28] . The development of an inactivated EV71 vaccine has three potential advantages over live attenuated vaccines: i ) unlike live attenuated vaccines , the virus cannot revert to a more pathogenic phenotype; ii ) immune responses can be manipulated ( e . g . Th1 vs . Th2 ) by the inclusion of appropriate adjuvant; and iii ) production will be cost-effective and flexible since the virus can be easily grown to high titers in tissue culture cells at scale . The decision to introduce an EV71 vaccine in developing countries with limited financial resources will depend on the balance of the economic value of EV71 vaccination versus EV71 risk . For instance , a recent study has estimated that in China , based on the current EV71 risk , a vaccine with 70% efficacy would be cost-effective at USD 25 per dose for mass immunization [48] . Encouraging results from a Phase 3 study show that high efficacy is in fact achievable with an inactivated vaccine [30] . Efficacy of an inactivated vaccine against HFMD could be further improved by inclusion of a coxsackievirus A16 ( CVA16 ) strain . CVA16 is another major cause of HFMD and cross-neutralization of CVA16 by anti-EV71 antiserum is low , likely due to sequence divergence at the VP1 neutralizing epitope and different conformation of the virion [28] .
|
Enterovirus 71 ( EV71 ) is one of the major viruses causing Hand , Foot , and Mouth Disease , a highly contagious illness which primarily affects young children in the Asia-Pacific region and can sometimes be fatal . No vaccines or antivirals for Hand , Foot , and Mouth Disease are available at this time . We developed an experimental vaccine using inactivated , purified EV71 with an adjuvant to amplify the immune response . When this vaccine was tested in mice and rabbits , they produced large amounts of antibodies that could neutralize the virus . We were reasonably certain that the vaccine would be safe because rabbits given repeated high doses did not develop pathological lesions or clinical symptoms . Based on these results , we proceeded to test the safety of the vaccine in human adults .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2013
|
Preclinical Evaluation of the Immunogenicity and Safety of an Inactivated Enterovirus 71 Candidate Vaccine
|
Coding sequence evolution was once thought to be the result of selection on optimal protein function alone . Selection can , however , also act at the RNA level , for example , to facilitate rapid translation or ensure correct splicing . Here , we ask whether the way DNA works also imposes constraints on coding sequence evolution . We identify nucleosome positioning as a likely candidate to set up such a DNA-level selective regime and use high-resolution microarray data in yeast to compare the evolution of coding sequence bound to or free from nucleosomes . Controlling for gene expression and intra-gene location , we find a nucleosome-free “linker” sequence to evolve on average 5–6% slower at synonymous sites . A reduced rate of evolution in linker is especially evident at the 5′ end of genes , where the effect extends to non-synonymous substitution rates . This is consistent with regular nucleosome architecture in this region being important in the context of gene expression control . As predicted , codons likely to generate a sequence unfavourable to nucleosome formation are enriched in linker sequence . Amino acid content is likewise skewed as a function of nucleosome occupancy . We conclude that selection operating on DNA to maintain correct positioning of nucleosomes impacts codon choice , amino acid choice , and synonymous and non-synonymous rates of evolution in coding sequence . The results support the exclusion model for nucleosome positioning and provide an alternative interpretation for runs of rare codons . As the intimate association of histones and DNA is a universal characteristic of genic sequence in eukaryotes , selection on coding sequence composition imposed by nucleosome positioning should be phylogenetically widespread .
In simple models of molecular evolution , selection on protein coding sequence ( CDS ) is exclusively devoted to optimizating protein function . As such , we expect amino acid choice to be dictated by protein function alone and synonymous mutations to be neutrally evolving . This is now known to be naïve . The protein's mRNA template can be under selection to maintain favourable mRNA structure [1]–[5] or facilitate speedy and accurate translation through usage of certain synonymous codons [6]–[10] . There is also evidence for selection on regulatory motifs in exons required for correct splicing [11]–[14] . Thus , many stages of the protein production chain are subject to their own particular regimes of selective constraint . But is this also the case when protein-coding information is still stored as DNA in its chromosomal context ? In other words , does the way DNA is organized come with its own important requirements on sequence composition , requirements that potentially conflict with optimization of protein function or translation rate optimization or any of the other forces ? One candidate process that might set up selective constraint at the DNA level is nucleosome positioning . Nucleosomes are the elementary units of chromatin organization , at their core comprising a ∼147 bp stretch of DNA tightly wrapped around a histone protein octamer . These core parcels are separated along the chromosome by “linker” regions of variable length [15] . At least two aspects of nucleosome architecture combine to make effects on coding sequence evolution a distinct possibility . First , the histone core has characteristic DNA-binding preferences [16]–[18] , governed by the variable bending and twisting attributes of different sequences [19] . Although nucleosomes can form on any stretch of DNA [15] , relative affinities can differ by several orders of magnitude [20] . In consequence , nucleosome positioning partly reflects the equilibrium state expected under a model in which energy penalties for coercing rigid DNA into a nucleosome state are minimized [21] . For example , nucleosome-free regions are enriched in rigid poly-A and poly-T runs [22] , [23] . Second , selection is likely to favour nucleosomes to be present at particular intra-genic sites and not at others . In particular , well-positioned nucleosomes frequently flank transcriptional start sites thus determining promoter accessibility [23]–[26] . Given that nucleosome formation preferentially occurs on particular sequences , but positioning cannot be entirely opportunistic because it is oriented relative to functional motifs , we might expect coding sequence composition to be biased and its evolution to be constrained to maintain adequate nucleosome architecture . To examine this expectation we make use of a recent high-resolution ( 4 bp ) genome-wide nucleosome map for Saccharomyces cerevisiae [23] . Based on evidence from codon and amino acid usage as well as comparative rates of evolution we identify nucleosome positioning as a novel layer of selection acting on protein-coding DNA .
Based on the experimentally determined S . cerevisiae nucleosome map of Lee and colleagues [23] , we assigned a likely occupancy state ( OS ) to each coding nucleotide . OSs comprise putatively unoccupied linker region , fuzzily positioned nucleosomes , and well-positioned nucleosomes ( see Methods ) . For intra-specific comparison of compositional differences , genes were then “abridged” so that they only contained codons that were predicted to have the same OS ( see Methods ) . Assuming that occupancy is relatively static over the evolutionary time scale analyzed here , we can also study differences in sequence evolution as a function of OS . S . cerevisiae codons from abridged genes that could be assigned to an orthologous codon in S . mikatae were retained for inter-specific comparison . Results of all orthology-based analyses are largely insensitive to choice of close comparator species , with S . bayanus or S . paradoxus orthologues showing the same trends ( data not shown ) . Analyzing evolutionary rates solely as a function of nucleosome occupancy is likely to yield misleading results because covariates common to both nucleosome architecture and sequence evolution are not controlled for . Prominently , selection on translational accuracy , speed , and robustness requires attention . Translational selection has been put forward as the single most important cause of between-gene variation in evolutionary rates in yeast [27] , where highly expressed genes show reduced rates of non-synonymous [28] and synonymous [27] substitutions as well as substantial codon bias [29] . More acutely , expression intensity is linked to promoter-type [30] , which in turn is linked to where , and how , nucleosomes are positioned . Nucleosomes tend to be depleted from promoters [24] , [25] , [31] but enriched over the coding regions [23] of highly expressed genes . In fact , Shivaswamy and colleagues [26] recently demonstrated that poorly positioned , i . e . fuzzy , nucleosomes over the CDS are associated with high transcription rates . Considering genes ( N = 1718 ) for which information is available on evolutionary rates , nucleosome occupancy and protein abundance [32] , we confirm proportional OS composition as a quantitative marker of expression ( Kendall's tau ( %linker∼abundance ) = −0 . 24 , P≪0 . 0001; tau ( %fuzzy∼abundance ) = 0 . 11 , P<0 . 0001; tau ( %wp∼abundance ) = −0 . 07 , P<0 . 0001 ) . Protein abundance is the expectedly strong negative predictor of evolutionary rates ( Spearman's rho ( abundance∼Ka ) = −0 . 47 , P<0 . 0001; rho ( abundance∼Ks ) = −0 . 38 , P<0 . 0001 ) linking OS composition to Ks ( rho ( %fuzzy∼Ks ) = −0 . 06 , P<0 . 0001 ) and , more pertinently , Ka ( rho ( %fuzzy∼Ka ) = −0 . 1 , P<0 . 0001 ) . Consequently , controlling for expression in analyzing the impact of nucleosome occupancy is imperative . The ideal approach to eliminate differences in expression between genes is to compare OS-linked evolution within genes . Within-gene analysis suggests that linker sequence exhibits reduced synonymous and non-synonymous evolution ( ΔKa ( well-positioned v linker ) : 15% , paired t-test: 4 . 37 , P<0 . 0001; ΔKa ( fuzzy v linker ) : 7% , paired t-test: 1 . 61 , P<0 . 11; ΔKs ( well-positioned-linker ) : 10% , paired t-test: 4 . 64 , P<0 . 0001; ΔKs ( fuzzy-linker ) : 12% , paired t-test: 5 . 47 , P<0 . 0001; N = 158; see Methods ) . These results offer preliminary support for the hypothesis that linker sequence is under stronger purifying selection than non-linker sequence at both synonymous and non-synonymous sites . However , within-gene comparisons can only be carried out for a small number of genes ( N = 158 ) because rarely is there sufficient sequence for all OSs within the same gene to obtain reliable rate estimates . Consequently , this sample is biased towards very long genes ( see Methods ) . Further , within-gene comparisons might still not reflect the true relationship between nucleosome occupancy and sequence evolution if there is intra-genic heterogeneity in substitution dynamics . This is because nucleosomes exhibit promoter-specific architectures , in line with their role in regulating promoter accessibility [23] , [25] . As the majority of translational start sites ( ATG ) in yeast are positioned within one nucleosomal rotation of the transcriptional start site [33] , 5′ ends of CDSs show regular occupancy patterns ( Figure 1A ) , which have repeatedly been described in the literature . This intimate association of CDS region and OS only gradually collapses downstream because linker length variation is typically modest [23] . Furthermore , regularities can also be detected across 3′ ends of CDS [26] ( Figure 1A ) . If , then , there existed gene-region distinct evolutionary trajectories , we would expect any analysis of OS-based differences to be biased as a result of the uneven representation of OSs across these regions ( Figure 1A bottom panel ) . To address the issue of regional biases and increase the amount of available sequence , we chose a concatenation-based approach . Eligible codons were concatenated across all genes ≥906 nt ( N = 845 ) by region ( 5′ , core , 3′ ) and OS . The terminal 100 codons were taken to represent 5′ and 3′ regions . For the core region , we analyzed the central 100 codons ( “restricted core” ) as well as all sequence after the termini are removed ( see Methods and Table S1 ) . As depicted in Figures 1B&C , there is indeed a marked regional component to coding sequence evolution , with Ks reduced at the CDS periphery and Ka at the centre of genes . That reduced synonymous substitutions at CDS termini can combine with low amino acid substitutions towards the centre of the gene has been observed previously in bacteria [34] . Selection on translational control mechanisms [35]–[37] and Hill-Robertson effects [38] might be the cause of regionally distinct Ks while the explanation for intra-genic variation in Ka is more elusive . Whatever the cause , the result is a spatial bias likely to confound analyses of nucleosome-related sequence evolution by inflating existing trends . In particular , linker sequence evolves particularly slowly at 5′ ends , where it is most prevalent ( Figure 1A bottom panel ) . Importantly , however , OS-linked differences are still manifest within regions ( Figure 1B&C , Table S1 ) . Thus , regional biases are insufficient to explain why sequences show distinct evolutionary patterns depending on OS . From the described results , a contradictory finding emerges . When comparing evolutionary rates within genes , we found Ka and Ks both reduced in linker sequence , yet in the regional analysis Ka and Ks , oddly , disagree . Ka appears reduced for fuzzy sequence ( Figure 1C ) . This discrepancy , however , might be an artefact of fuzzy sequence being enriched in highly expressed genes , which in turn show elevated levels of amino acid conservation [28] . To evaluate this possibility , sequence concatenated by region and OS was further binned by protein abundance ( see Methods ) . Although noise is substantial , Figures 2A&B illustrate for 5′ regions that controlling for expression recreates a more consistent picture of substitution dynamics . Synonymous but also non-synonymous substitution rates are reduced in linker regions ( Table 1 , Methods ) by ∼6% ( Table S2 ) . Ks but not Ka is also reduced in core regions ( by ∼5% ) while we detect no significant differences in substitution rates between OSs across 3′ regions ( Table 1 ) . Evolutionary rates of sequence associated with fuzzily or well-positioned nucleosomes are virtually indistinguishable ( Table S2 ) . Thus , the reduced Ka for fuzzy sequence observed in Figure 1C is an artifact of the enrichment of fuzzy sequence in highly expressed genes . Patterns of single nucleotide polymorphisms ( SNPs ) suggests that whichever factors have caused OS-linked differences in divergence are still a relevant evolutionary force in current populations of S . cerevisiae . Analyzing polymorphism data from a recent re-sequencing effort of over 30 S . cerevisiae strains ( see Methods ) , we found SNP density in the same set of genes to be reduced relative to random expectation at synonymous ( chi-square test = 35 . 61 , P = 1 . 8E-08 , enrichment: linker: 0 . 89 , fuzzy: 1 . 00 , well-positioned: 1 . 02 ) and non-synonymous sites ( chi-square test = 11 . 48 , P = 0 . 0032 , enrichment: linker: 0 . 95 , fuzzy: 1 . 04 , well-positioned: 0 . 98 ) . These trends become even more clear-cut when expression is controlled for ( data not shown ) . Although the above results support the notion that purifying selection is stronger in linker than in non-linker , this need not be the correct interpretation . Linker sequence might simply be less mutable . This could be for one of two reasons . First , codons enriched in linker are less mutagenic . Second , regardless of codon composition , linker is somehow protected from mutation . As regards the first possibility , codons preferentially employed in linker sequence are noticeably AT-rich ( see below ) . As G and C are typically considered more mutable , this alone may explain low evolutionary rates in linker . We control for this scenario in the following way: for every aligned S . cerevisiae linker codon , we randomly select ( without replacement ) an identical S . cerevisiae codon from the pool of identical codons in the fuzzy and well-positioned concatenated sequences in the same expression/region bin respectively . In the small number of cases where a linker codon could not be matched to a codon in a different OS , a codon was chosen at random . In this way , we end up with sequences of the same length as the linker sequence and virtually identical codon composition . Table 1 reveals that , controlling for codon composition , we find the same pattern of constraints uncovered previously ( also see Table S2 ) . We conclude that the low rates of evolution observed for linker sequence are not more parsimoniously explained by an AT-mutation bias . Could it be that linker sequence is less mutagenic , regardless of codon content ? One can imagine mechanistic models in which this might be possible . For example , Kepper et al . [39] recently explored the links between chromatin fiber conformation and nucleosome geometry . Their models , based on mammalian chromatin , suggest that during higher-order organization of nucleosomes into compact chromatin fibers linker sequence is brought into the core of the chromatin fiber upon binding of linker histone , and might be better protected against mutagens as a result . It has also been shown that the binding of linker histone Hho1p inhibits homologous recombination [40] . As homologous recombination in yeast is thought to be mutagenic [41]–[43] , reduced rates of substitution might be linked to the protective effects of Hho1p binding . Aside from the fact that it is unclear whether yeast chromatin is organized in a mammal-like fashion as far as higher order structure is concerned , it seems unlikely that mutational effects can be the sole explanation , not least because linker sequence shows different rates of evolution as a function of intra-gene position even when overall regional biases are taken into account . The proportional reduction of linker Ks to synonymous rates of nucleosome-bound sequence in the same bin tends to be significantly higher at 5′ ( median reduction = 0 . 114 ) versus 3′ ends ( median reduction = 0 . 026 , Wilcoxon test P = 0 . 04 ) , with the difference to core regions not quite significant ( median reduction = 0 . 057 , P = 0 . 07 ) . If nucleosome positioning is responsible for elevated linker conservation then we might additionally expect to see skews in patterns of codon and amino acid usage . We compared codon and amino acid composition between OSs within the S . cerevisiae genome . As alignability is not an issue in this analysis , we can exploit a substantially larger number of genes ≥906 nt ( N = 1986 ) . Figure 3 shows for core sequence binned by protein abundance that multiple amino acids are depleted or enriched in linker sequence relative to their proportional use across all core sequence ( regardless of OS ) . These skews appear linked to nucleosome occupancy . First , some amino acids are coded exclusively by nucleotide trimers that are unanimously , albeit sometimes weakly , predictive of either nucleosome binding or exclusion as determined by Peckham and colleagues for genomic sequence [44] ( Table 2 ) . If nucleosome positioning was a relevant functional concern , such amino acids should be depleted from linker sequence if all their codons have a positive positioning score , and vice versa , because they have no capacity to negotiate this concern by adjusting their codon usage . This is what we observe . Eight out of eleven amino acids with unanimous positioning score across all codons show skewed usage in the expected direction ( Table 2 , Table S3 ) , while the remaining three show no significant skews . This rule of thumb can explain the majority of cases where amino acids are depleted from linker regions . Amino acids most strongly enriched in linker ( I , L , N , Y ) , on the other hand , show the strongest and most consistent evidence for biased usage of certain codons ( Table 2 ) , and are therefore probably enriched because one or more of their codons is preferentially employed in linker . We tested non-random enrichment/depletion of synonymous codons across OS for each protein abundance bin independently using Fisher's exact test . Of those amino acids ( D , F , I , K , L , N , Y ) where we find an overall trend for certain codons to be significantly enriched or depleted ( Table 2 , Table S3 , see Methods on how significance was determined ) , asparagine ( N ) codons in particular discriminate remarkably well between OSs , with AAT highly enriched in linker sequence ( Genomic ratio: AAT/AAC = 1 . 44 , ratio in nucleosome-bound sequence: AAT/AAC = 1 . 38 , ratio in linker: AAT/AAC = 2 . 5; determined across all bins and regions ) . Finally , we compared codon usage in experimentally determined linker sequence with codon usage in sequences selected for maximum nucleosome exclusion potential from simulated sequences ( see Methods ) and found them to be in good agreement ( Figure 4 ) . In particular , all codons consisting entirely of A and T nucleotides are enriched in both simulated and experimentally determined linker sequence . We identify only one codon , GAT , that is not entirely composed of A or T nucleotides . It is interesting to note here that linker elements proximal to nucleosomes can interact with nucleosome remodeling complexes [46] , [47] and that Song et al . [48] recently reported recognition motifs of the GATA family of transcription factors to be enriched in nucleosome-free regions at the fission yeast centromere 2 , with the binding consensus being centered around the GATA motif . The above evidence is consistent with stronger purifying selection acting on linker to maintain correct nucleosome positioning . Could it be , however , that purifying selection is operating , just not as regards nucleosome positioning ? We consider two alternatives . First , might linker sequence be enriched for transcriptional control elements ? This seems unlikely for several reasons . Whereas in multicellular eukaryotes it is not unusual for transcription control elements to be located within the open reading frame , transcription regulation in yeast is typically governed by upstream regulatory elements alone [49] . For a handful of genes an effect on expression level upon removal/mutation of specific intra-genic elements has been demonstrated experimentally . However , these elements are mostly located in nucleosome-bound regions ( Table S4 ) . A second possibility is that functional mRNA secondary structure , another cause of sequence conservation and biased composition [1] , [4] , [50] , [51] , preferentially maps onto linker sequence . Proposing such a small-scale spatial bias is not unreasonable . We know that nucleosomes are regularly positioned around the promoter , which is also the pivot around which secondary structure facilitating translation initiation is organized [52] . As a result , 5′ regions in yeast are enriched for strong local secondary structures vis-à-vis the remainder of the CDS [51] . Might it be that linker regions and functional secondary structure spatially overlap so that the signature of elevated conservation is really owing to selection on mRNA secondary structure ? We find no evidence for this . The window within which hairpin structures downstream of the start codon have an effect on translation initiation ( +12–+18 nt [37] , [53] , [54] ) typically fall within the CDS region occupied by the well-positioned nucleosome downstream of the promoter rather than linker sequence ( cf . Figure 1A ) . We also examined a set of strong local mRNA secondary structures ( Supplementary Table 1 in [51] ) , but found no preferential mapping onto linker sequence ( Table S5 ) .
The aim of the present analysis was to elucidate whether selection at the DNA level , specifically on nucleosome organization , has affected the evolution of protein-coding sequence . Controlling for intra-genic biases in nucleosome occupancy and , critically , gene expression , we find linker sequence to evolve more slowly , particularly 5′ where constraints are evident on both synonymous and non-synonymous evolution . This is consistent with nucleosome architecture in this region being essential to control gene expression . We estimate that linker sequence across yeast genes evolves approximately 6% slower than sequence bound by nucleosomes . As linker accounts for less than 10% of total genic sequence ( with a regional maximum of ∼15% across 5′ regions ) , the overall reduction in Ks is small ( <1% ) . Note , however , that we almost certainly underestimate the effect of nucleosome positioning concerns on coding sequence evolution . This is because our method of detecting selection is based on differences between OSs . In consequence , if nucleosome-bound sequence were also under selection , as suggested by previous research [26] , [55] , this would lead to an underestimation of the magnitude of selection . Even assuming that overall effects are modest , however , the results are nonetheless important for several reasons . First , as nucleosome formation on genic sequence is a universal process , our finding of OS-linked evolutionary patterns across regions and expression levels implies that nucleosome positioning , and thus selection at the DNA level , could affect coding sequence evolution in most if not all other eukaryotes . This potentially has direct implications for estimating the neutral mutation rate from genic regions , although as noted above , the effects are probably weak so unlikely to cause serious errors . Second , while the overall effects on sequence evolution might be minimal vis-à-vis other determinants of substitution rates , synonymous substitutions might individually be of selective significance . The presence of purifying selection certainly argues that individual synonymous mutations have in the past been weeded out because they introduced sequence-based errors in nucleosome positioning . By implication , and given that nucleosomes are a ubiquitous companion of genic sequence , such mutations might be a novel cause of genetic disease . Third , these results have an important implication for interpreting local patterns of codon usage . Translationally optimal codons are frequently depleted from linker regions ( Table 2 ) . As a result , adaptation for translational efficiency is reduced in linker sequence , as evidenced by a reduced frequency of optimal codons ( FOP ) ( Figure 5; paired t-test for extended core regions: ΔFOP ( well-positioned-linker ) = 11 . 20 , P<2 . 2E-16; ΔFOP ( fuzzy-linker ) = 11 . 73 , P<2 . 2E-16; ΔFOP ( well-positioned-fuzzy ) = −3 . 7 , P = 3E-04 ) and longer runs of translationally non-optimal codons are more likely ( Table S6 ) . Previously , runs of non-optimal codons have been considered in the context of selection on translation regulation [56] . Such runs may , for example , induce ribosomal stalling as non-optimal codons tend to be specified by rare tRNAs . This in turn may affect protein folding [57]–[59] . Specification of linker sequence provides a viable alternative hypothesis for a subset of these runs ( Table S6 ) . Finally , the results are consistent with the idea that nucleosome positioning in CDS is in no small part determined by linker-based exclusion signals in contrast to specific nucleosome binding signals , an idea that has recently grown in appreciation [23] , [44] , [60] . While affinity sequences are more common in coding sequence than expected by chance [55] , this signature is relatively weak [26] . If positioning of nucleosomes on CDS is principally achieved by exclusion signals , this is what we expect . Positioning by exclusion might be a particularly beneficial modus operandi for coding sequence , as it restricts constraints to a small proportion of an already highly constrained class of sequence . Note added during production: the observation that linker sequence evolves more slowly has recently been independently made by Washietl et al . [61]
Likely occupancy states ( linker , fuzzily , and well-positioned nucleosomes ) , across the S . cerevisiae genome were downloaded from http://chemogenomics . stanford . edu/supplements/03nuc/ ( Table S5 ) . S . cerevisiae chromosomes were obtained in GenBank format from the Saccharomyces Genome Database ( SGD ) ( ftp://genome-ftp . stanford . edu/pub/yeast/data_download/sequence/genomic_sequence/chromosomes/fasta/archive/genbank_format_20060930 . tgz; archived versions from 30/09/2006 to match the data of Lee et al . [23] ) . Gene models were extracted and filtered so that only genes with a multiple of three nucleotides , proper start and termination codon , no internal stops or ambiguous nucleotides ( “n” ) were retained . Further , all genes containing introns without consensus splice sites ( GT-AG ) were eliminated . For each nucleotide in each gene , a likely OS was determined by retrieving all tiling probes ( from Lee et al . [23] ) containing this nucleotide and determining the dominant call . For example , if covered by 3 probes called as linker , linker , and fuzzy nucleosome , we considered the nucleotide to be in the linker region; regions with 2-probe coverage , where probe calls can be in conflict , were excluded from the analysis , as we had no biological reason to attribute codons to either category . These cases are rare ( <0 . 2% of codons ) and thus did not warrant inclusion in a separate category . Only genes in which every nucleotide is covered by at least 2 probes were considered . For the filtered set of S . cerevisiae genes , orthologues of S . mikatae were obtained from SGD ( ftp://genome-ftp . stanford . edu/pub/yeast/data_download/sequence/fungal_genomes/S_mikatae/MIT/orf_dna/orf_genomic . fasta . gz ) . Filters for likely protein-coding capacity were applied as above . The remaining orthologue pairs were aligned at the protein level using MUSCLE ( v3 . 6 ) after removal of start and stop codons . Alignments with >5% gaps were discarded . Aligned codons for which S . cerevisiae OS was consistent across all three nucleotides were concatenated by OS across relevant gene subsets as stated in the Results . Ka and Ks were calculated using Li's protocol [62] . Analysis of OS-linked differences in sequence evolution were based on a small number of genes ( N = 158 ) with ≥300 coding nucleotides of each major ( linker , fuzzy , well-positioned ) OS and a sufficient number of degenerate sites to calculate Ks . Relative rate differentials were calculated as ( Ks linker−Ks well-pos ) / ( ( Ks linker+Ks well-pos ) /2 ) . The analysis was repeated excluding genes with Ks or , more likely , Ka = 0 . The results remained qualitatively the same ( data not shown ) . Median gene length is markedly longer ( median = 2787 nt ) than across all yeast genes ( median = 1245 nt , Mann-Whitney U test P<2 . 2E-16 ) , with likely implications for gene function and expression , so that this sample cannot be considered representative . Genes ≥906 nt without alignment gaps ( N = 845 , median CDS length = 1473 nt ) were considered in the analysis of regional differences . Start and stop codons were trimmed off and terminal ( 5′ and 3′ ) and core 100 amino acids concatenated separately . On average , 11010 linker , 54328 fuzzy , and 50780 well-positioned codons were analyzed per region . We chose 100 amino acids as a convenient cut-off as this a ) typically captures well-positioned nucleosomes ( plus linker ) at the start and end of genes ( cf . Figure 1A ) , for which exact positioning is most likely to be of functional significance , and b ) analysis of intra-genic substitution variation in prokaryotes [34] suggests that biases extend at least 50 amino acids into the gene . As we do not know what the causes of this variation are or how substantially they affect yeast , a cut-off of 100 amino acids appears a prudent conservative choice . Defining the core as all sequence left after termini have been removed yields qualitatively identical results ( data not shown ) . As the larger amount of sequence available affords a better resolution when the core is defined in this way , we present results for this definition unless otherwise indicated . Ka and Ks were determined for all aligned concatenates . Significance of differences in evolutionary rates across OSs was tested by repeated random sampling of aligned codon pairs from a region-specific super-concatenate containing all OS concatenates to create 3 ( OS ) ×3 ( regions ) ×10 000 sequences of the same lengths as the original concatenates . Observing Ka ( Ks ) values for the original concatenate more than two standard deviations below the mean of the distribution of randomized sequences is taken to be indicative of evolutionary constraint . Concomitant Ka ( Ks ) values significantly faster than expectation are attributed to the fact that OSs are non-independent . This constraint-guided interpretation is justified because positive selection is expected to be much rarer than purifying selection across the large sample of genes considered here . Coding sequence concatenated by region and OS was split into expression bins based on protein abundance data from Newman and colleagues [32] . Starting with the gene whose protein was least abundant , sequence from individual genes was allocated to bins of increasing protein abundance . A new bin was generated once the previous bin contained at least 400 codons of the rarest OS , linker . Sequence from any one gene was never split between bins . The results are robust for smaller bins ( minimum 250 linker codons ) but we decided to prioritize reducing sampling noise for Ka ( Ks ) estimates rather than achieving equal coverage of successive expression ranges . The final bin ( highest protein abundance ) was discarded because mean average deviation was disproportionally large and the minimum number of codons criterion frequently violated . Differences in evolutionary rates were assessed by analysis of covariance ( ANCOVA ) . OS-specific slopes were shown not to differ significantly , as a prerequisite for assessing the importance of OS as a covariate ( Table S2 ) . Average differences in evolutionary rates were quantified by comparing the intercepts of OS-specific slopes ( Table S2 ) . We tested enrichment/depletion of synonymous codons ( Table 2 ) for each protein abundance/region bin independently using Fisher's exact test . At the p<0 . 05 level we expect N*0 . 05 bins to show codon skews by chance . With 64 ( 73 , 32 ) bins in the 5′ ( core , 3′ ) region , we thus expect to see 3 . 2 ( 3 . 65 , 1 . 6 ) bins with skewed codon usage by chance . Further , there are multiple codons for which significant skews in both directions are observed . This could be owing to both noise in the data and chances of a codon to function as part of linker sequence being dependent on its sequence context . We therefore took a conservative approach to judging whether codon usage is significantly skewed across OS for any one amino acid in that we required A ) the difference between numbers of enriched and depleted bins in the core region , for which most data are available , to be 5 or greater and B ) the direction of skews not to be inconsistent across regions , e . g . not to find a codon more often enriched than depleted in 5′ regions but more often depleted than enriched in 3′ regions , regardless of whether the relative enrichment in either region was significant on its own . To evaluate whether codon usage differences across OSs are parsimoniously explained by nucleosome positioning ruled by intrinsic binding affinities , we generated sequences ( k = 10 000 ) of equal length to the region bound by the histone core ( 147 bp = 49 codons ) , picking codons at random according to their approximate genomic usage ( http://www . kazusa . or . jp/codon/cgi-bin/showcodon . cgi ? species=4932 ) . Nucleosome formation potential of these short sequences was scored by assigning a weight to each sequence based on the additive occurrence of all nucleotide k-mers evaluated for their predictiveness in nucleosome positioning by Peckham et al . [44] . Weights corresponded to the receiver operating characteristic ( ROC ) scores calculated by Peckham et al . [44] . ROC scores reflect the capacity of a k-mer to discriminate between two sets it is differentially represented in , with k-mers of no discriminative power scoring 0 . 5 , a perfect classifier 1 . 0 ( see Peckham et al . [44] and references therein for a more detailed explanation ) . Overlapping and embedded k-mers were scored as in the following example: 4-mer AAAA was assigned 4× the score for “A” , 3× the score for “AA” , 2× the score for “AAA” , and once the score for the full motif “AAAA” . The overall score was divided by the number of motifs detected . Cross-validation with an alternative algorithm [63] suggests that this approach does , in fact , recover sequences with high and low nucleosome formation potential ( Figure S1 ) . Codon usage was compared between the highest and lowest scoring 5% of sequences using a chi-square test . Chi-square cell values were chosen as an approximate measure of codon bias for individual codons ( Figure 4 ) . Codon usage bias towards translationally optimal codons was calculated as the frequency of optimal codons ( FOP ) [64] using codonw ( J . F . Peden ) with S . cerevisiae default parameters . SNP analysis is based on data from the Saccharomyces Genome Resequencing Project available at http://www . sanger . ac . uk/Teams/Team71/durbin/sgrp/index . shtml . Table S7 contains gene names for all S . cerevisiae genes used for each major analysis , together with identifiers for orthologous S . mikatae ORFs ( if applicable ) . Custom scripts , for example to map nucleosome calls onto coding sequence , are available on request from the authors .
|
Why do some parts of genes evolve slower than others ? How can we account for the amino acid make-up of different parts of a protein ? Answers to these questions are usually framed by reference to what the protein does and how it does it . This framework is , however , naïve . We now know that selection can act also on mRNA , for example , to ensure introns are removed properly . Here , we provide the first evidence that the way DNA works also affects gene and protein evolution . In living cells , most DNA wraps around histone protein structures to form nucleosomes , the basic building blocks of chromatin . Protein-coding sequence is no exception . Looking at genes in baker's yeast , we find that sequence between nucleosomes , linker sequence , is slow evolving . Both mutations that change the gene but not the protein and those that change gene and protein are affected . We argue that selection for correct nucleosome positioning , rather than differences in mutational processes , can explain this observation . Linker also exhibits distinct patterns of codon and amino acid usage , which reflect that DNA of linker needs to be rigid to prevent nucleosome formation . These results show that the way DNA works impacts on how genes evolve .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"genetics",
"and",
"genomics/microbial",
"evolution",
"and",
"genomics",
"genetics",
"and",
"genomics/comparative",
"genomics",
"evolutionary",
"biology/evolutionary",
"and",
"comparative",
"genetics",
"evolutionary",
"biology/bioinformatics",
"genetics",
"and",
"genomics/epigenetics"
] |
2008
|
The Impact of the Nucleosome Code on Protein-Coding Sequence Evolution in Yeast
|
Surgery for trachomatous trichiasis ( TT ) is a key component of the SAFE Strategy for trachoma control . Unfortunately , recurrent TT following surgery is common , probably due to various surgical and disease factors . To develop strategies to reduce recurrence rates it is necessary to understand its pathological basis . In this study we investigated the relationship between recurrent trichiasis and the expression of various cytokines and fibrogenic genes during a two-year follow-up period . Individuals undergoing surgery for TT were examined at baseline ( pre-operative ) , 6 , 12 , 18 and 24 months . Conjunctival swab samples were collected from the tarsal conjunctiva for RNA isolation on each occasion . Individuals who developed recurrent TT with at least 3 lashes touching the eye on one or more occasion were designated “cases” and an equal number of “controls” were randomly selected from those without recurrent TT , frequency matched for age and baseline TT severity . The expression of the following genes was measured by quantitative RT-PCR: S100A7 , IL1B , CXCL5 , TNFA , NOS2A , CTGF , MMP7 , MMP9 and MMP12 . Thirteen hundred individuals were enrolled and underwent surgery . By two years 122 had developed recurrent TT with at least 3 lashes touching the eye . Recurrent TT was consistently associated across multiple time points with about a 2-fold increase in S100A7 expression ( p = 0 . 008 ) . Clinically visible conjunctival inflammation was associated with increased S100A7 , IL1B , CXCL5 , MMP9 and MMP12 expression . Increased S100A7 expression was associated with trachomatous conjunctival scarring and may be linked to the pathophysiology of recurrent TT . S100A7 expression could be a potential biomarker for this disease process . As part of the epithelial innate immune response S100A7 has multiple actions , potentially contributing to a chronic pro-inflammatory response , which may lead to ongoing tissue damage and increased scarring .
Trachoma remains the leading infectious cause of blindness worldwide [1] . Trachomatous Trichiasis ( TT ) is the potentially blinding stage of this disease in which the eyelashes scratch the surface of the globe , resulting in corneal opacification [2]–[4] . It is the result of a progressive scarring process of the tarsal conjunctiva , which is initiated in childhood by recurrent episodes of Chlamydia trachomatis infection that are characterised by inflammation . However , the pathophysiology of this scarring process is poorly understood; it is unclear which immuno-fibrogenic mechanisms are most important and what factors drive disease progression , particularly in the late stages when C . trachomatis can rarely be detected [2] , [5] . Surgery is performed to correct TT . Unfortunately , TT frequently recurs , particularly when performed under operational conditions [2] , [6]–[8] . Recurrent TT is a significant problem in preventing blindness from trachoma , undermining the potential success of surgical programmes . Prospective long-term data suggest that TT recurrence can be broadly subdivided into two phases: early and late [2] , [9]–[12] . Early recurrence , which may be defined as that developing within the first six months following surgery , appears to occur with a greater incidence rate than late recurrence [9] . Early recurrence is probably attributable to a combination of factors including pre-operative disease severity , how well the surgery was performed , and possibly early post-operative wound healing responses [2] , [13] . Early trichiasis recurrence rates may be reduced by improving the quality of surgery and refining the operative procedure [8] , [14] . Inter-surgeon variation in recurrence rates has been reported , suggesting that operative factors can significantly affect results [2] , [12] , [15] . Currently , the World Health Organization ( WHO ) endorses the bilamellar and posterior lamellar tarsal rotation procedures ( BLTR and PLTR ) [8] , [16] . The BLTR has been the subject of several clinical trials comparing it to alternative procedures ( other than the PLTR ) and was found to give the best results [10] , [17] , [18] . Currently , it is not know whether the BLTR and PLTR are equally effective . There may also be scope for improving results through influencing the course of the post-operative wound healing process by regulating excessive contractile scarring , analogous to glaucoma filtration surgery . However , these early biological events have yet to be studied . Late recurrence is likely due to immune-mediated conjunctival scarring disease , however , the pathological basis of progressive cicatrisation in trachoma remains to be elucidated . It may be driven by recurrent infection , yet , it is relatively unusual to detect C . trachomatis infection in people with scarring trachoma , and it has not been found to be a risk factor for recurrent TT [2] , [5] , [6] , [11] , [19] . In contrast , other non-chlamydial bacterial pathogens are identified relatively frequently and have been associated with recurrent trichiasis [2] , [19] , [20] . We have previously found such infections to be associated with altered gene expression patterns for inflammatory cytokines and tissue modifiers such as matrix metalloproteinases ( MMP ) [20] , [21] . The potential of post-operative oral azithromycin to reduce recurrence has been investigated . In one trial from Ethiopia , a region with generally high C . trachomatis prevalence , recurrence was reduced , although this was not linked to C . trachomatis [11] . In a separate trial from The Gambia with a lower prevalence of C . trachomatis , post-operative azithromycin did not affect the recurrence rate [2] . Similarly , in a study from Nepal , the recurrence rates were comparable in the azithromycin and placebo groups [22] . A better understanding of the immuno-fibrogenic basis of recurrent TT may help in developing interventions that reduce recurrence that is attributable to the scarring disease process . We have previously examined the conjunctival transcriptome in scarring trachoma in a case-control study [5] . This identified various factors that are differentially expressed in scarred conjunctival tissue compared to normal controls: pro-inflammatory cytokines and several MMPs were particularly prominent . The gene with the largest relative increase in expression was the pro-inflammatory , anti-bacterial peptide S100A7 or Psoriasin , which was increased 15-fold in trichiasis cases before surgery , compared to controls [5] . We have also previously reported the conjunctival expression level of several factors at a single time point one year after TT surgery and found recurrent TT was associated with a reduced MMP1/TIMP1 ratio [20] . Recently we reported the results of a randomised controlled trial of absorbable ( vicryl ) versus silk sutures for TT surgery; there was no difference in outcome between the two treatment arms [12] . During this trial we re-examined participants at six-monthly intervals for two years . On each occasion conjunctival swab samples were collected for gene expression analysis . Here we report the expression of various pro-inflammatory cytokines and MMPs in a longitudinal comparison of trial participants with and without recurrent TT . We examined the hypothesis that some of these factors are linked to recurrent disease and sought to identify biomarkers associated with recurrent TT .
This study adhered to the tenets of the Declaration of Helsinki and was approved by three science and ethics committees: the National Health Research Ethics Review Committee , Ministry of Science and Technology , Ethiopia , the London School of Hygiene and Tropical Medicine Ethics Committee ( UK ) and Emory University Institutional Review Board ( Atlanta , USA ) . Potential participants were provided with both written and oral information in Amharic about the trial . For those agreeing to participate , written informed consent in Amharic was required prior to enrolment . If the participant was unable to read and write , the information sheet and consent form were read to them and their consent recorded by witnessed thumbprint , which was approved by the Ethics Committees . This nested case-control study was conducted within a surgical trial of absorbable versus silk sutures in the treatment of upper eyelid TT in Amhara Region , Ethiopia , which has been reported separately [12] . In brief , individuals aged 18 years or more with previously un-operated major trachomatous trichiasis ( >5 trichiatic lashes ) of the upper eyelid were recruited through surgical treatment campaigns in rural areas . Trichiasis was considered to be due to trachoma in the absence of another obvious cause for the trichiasis , such as trauma , malignancy , involutional changes or severe blepharitis . Following the baseline pre-operative clinical assessment ( described below ) , participants were randomised to one of two surgical intervention groups , which differed only in the type of sutures that were used . The posterior lamellar tarsal rotation procedure was used in all cases to correct upper eyelid TT , using the randomly allocated suture material: ( 1 ) PLTR with 4/0 silk sutures ( Mersilk , Ethicon ) or ( 2 ) PLTR with 5/0 polyglactan-910 ( Vicryl undyed , Ethicon ) [8] , [12] . The PLTR was chosen because this is the standard procedure used in Amhara Region . Five general nurses performed all the surgery . They had been trained in TT surgery by the Amhara Regional Trachoma control programme , and had been performing surgery regularly for more than 1 year . They were selected on the basis of their good performance and underwent additional refresher and standardisation training , under the supervision of a senior Ethiopian ophthalmologist [12] . Postoperatively , the operated eye was padded for a day and then tetracycline eye ointment was self-administered twice a day for two weeks . Participants were re-examined at 6 , 12 , 18 and 24 months after surgery . To be eligible for analysis in this study a complete follow-up series was required; cases and controls were only selected from those individuals who were examined on all occasions . Individuals who developed any recurrent TT ( defined as one or more lashes touching the eye ) at any point during the course of the two-year follow-up period were designated recurrent TT “cases” and those without any trichiasis recurrence or evidence of epilation at any time point were designated non-recurrent “controls” . We further limited recurrent TT cases to those that had 3 or more lashes touching the eye at some point during follow-up , as this provided just over 100 recurrent TT cases for analysis . The non-recurrent controls were selected at random from operated individuals who had not developed recurrent TT at any time point during follow-up; these controls were frequency matched with the recurrent cases for age and baseline trichiasis disease severity ( determined by the number of lashes touching the eye before surgery ) . At all assessments participants were examined for clinical signs of trachoma , graded using 2 . 5× binocular loupes according to the detailed WHO trachoma grading system [23] . The baseline ( pre-surgery ) , 12-month and 24-month follow-up examinations were conducted by a single ophthalmologist ( SR ) ; the 3 , 6 and 18-month examinations were conducted by a single ophthalmic nurse ( EH ) . Recurrent trichiasis was defined as one or more lashes touching the eye . In preparatory exercises the examiners were standardised to each other and showed strong agreement for the presence of trichiasis ( kappa = 0 . 86 ) . The number of lashes touching the eye in primary position was counted . Individuals with clinical evidence of epilation were considered to have recurrent trichiasis , even if no lashes were touching the globe on examination . Significant conjunctival inflammation was defined as the presence of papillary inflammation grades P2 or P3 of the detailed WHO Trachoma Grading System [23] . Conjunctival swab samples were collected by the examiner from the upper tarsal conjunctival surface for RNA isolation at all time-points , except 3-months . The ocular surface was anaesthetised with preservative-free proxymetacaine 0 . 5% eye drops ( Minims , Chauvin Pharmaceuticals ) . A dacron polyester-tipped swab ( Hardwood Products Company ) was passed horizontally across the conjunctival surface 4 times with a quarter turn between each pass . Swabs were then placed directly into a tube containing 0 . 3 ml of RNAlater ( Life Technologies ) . Samples were kept on ice packs until frozen later the same day at −20°C . The abundance of nine selected transcripts was estimated by quantitative RT-PCR . The choice of targets was informed by our previously published conjunctival transcriptome analysis conducted on pre-operative samples from TT patients in the same population and matched normal controls [5] . These targets broadly fall into three groups: ( i ) pro-inflammatory cytokines , chemokines or effector molecules ( interleukin-1β [IL1B] , tumour necrosis factor-α [TNFA] , psoriasin-1 [S100A7] , chemokine ( CXC ) ligand-5 [CXCL5] , nitric oxide synthase-2A [NOS2A] ) , ( ii ) various matrix metalloproteinases ( MMP7 , MMP9 and MMP12 ) and ( iii ) connective tissue growth factor ( CTGF ) . Total RNA was extracted from the swab samples using the RNeasy Micro Kit ( Qiagen ) . Reverse transcription was performed using the QuantiTect Reverse Transcription Kit ( Qiagen ) according to the manufacturer's instructions . Multiplex real-time quantitative PCR was performed on a Rotor-Gene 6000 ( Corbett Research , Cambridge , UK ) using the QuantiTect Multiplex NoROX Kit ( Qiagen ) , according to the manufacturer's instructions . Multiplex assays of up to four separate targets ( including HPRT-1 as the reference gene ) were designed by Sigma Life Science ( www . Sigma . com/designmyprobe ) using Beacon Designer 7 . 60 ( Premier Biosoft International , Palo Alto , CA , USA ) . The thermal cycle protocol used the following conditions: 95°C for 15 minutes , followed by 45 cycles of ( 1 ) denaturation at 94°C for 30 seconds , ( 2 ) annealing and extension at 60°C for 30 seconds . Fluorescence data was acquired at the end of each cycle . The relative efficiency of the component reactions was assessed using standards containing all targets in a sequence of tenfold serial dilutions . Reactions were performed in duplicate , in a total volume of 25 µl , which contained 2 µl of sample or standard . Probe and primer sequences are available on request . We aimed to analyse sample sets from at least 100 cases and 100 controls . This sample size was estimated to have 80% power and 95% confidence to detect a factor with an odds ratio of 2 . 5 that is present in 50% of the controls . The maximum number of lashes touching the eye during follow-up was used to stratify the recurrent TT group . The transcript abundances for genes of interest were standardised relative to that of HPRT-1 in the same reaction using the ΔΔCT method and were successfully normalised by log10 transformation [24] . This corrects for variations in the total RNA collected by the swabs . The only exception was CTGF , which was not possible to multiplex with these other targets; therefore the quantitation for this target relative to HPRT is based upon standard curve analysis . Data were managed in Access ( Microsoft ) and analysed in STATA 11 ( StataCorp ) . Cases and controls were comparable in terms of age , sex and pre-operative trichiasis severity , therefore , the relative level of expression of each target was compared between them using unadjusted unpaired t tests . Multivariable linear regression models were fitted for the expression level of each target at baseline and the following potential explanatory variables: sex ( female ) , age ( in years ) , case-control status , conjunctiva inflamed ( P2/P3 ) at baseline and the number of lashes touching the eye at baseline . A stepwise selection process was performed to fit each model , retaining terms if the p-value for omission was <0 . 2 and their p-value in the model was <0 . 2 , with p-values assessed by the likelihood ratio test . Similar models were fitted for each follow-up time point . There was no evidence of a differential effect over time , assessed by likelihood ratio testing with and without an interaction with time ( p-values 0 . 19 to 0 . 74 ) . Therefore , we combined the data from the four follow-up time points , using random-effects linear models . An un-conditional logistic regression model was developed for the association between recurrent trichiasis and baseline conjunctival inflammation and the expression levels of all nine genes . Again a stepwise selection process was performed to fit each model , retaining terms if the p-value for omission was <0 . 2 and their p-value in the model was <0 . 2 , with p-values assessed by the likelihood ratio test . The correlation in the expression of the various targets at each time point was investigated using biplots and partial correlation coefficients . To adjust for multiple comparisons , we calculated critical significance thresholds for each table using the conservative Bonferroni correction . Although we make several comparisons , these are un-likely to be truly independent of each other , as one would expect some of these genes to interact in biological networks .
A total of 1300 individuals with major TT were recruited into the trial and received trichiasis surgery [12] . As there was no difference in the TT recurrence rate between the two alternative sutures , subjects for this nested case-control study were drawn from both trial arms and data combined [12] . In all , 901/1300 ( 69 . 3% ) were assessed at all five time-points . Only those seen and sampled on all five occasions were eligible for this nested study . Comparing the eligible and excluded people: there was no difference in the proportion who were female ( p = 0 . 3 ) , the eligible group was slightly younger ( 49 years vs . 51 years , t-test p = 0 . 02 ) and had slightly more severe baseline trichiasis ( mean number of lashes: 8 . 0 vs . 7 . 3 , Wilcoxon rank-rum p = 0 . 006 There were 122 people ( seen and sampled five times ) who developed recurrent trichiasis of the upper eyelid with three or more lashes touching the eye during the follow-up period; these participants were designated as recurrent “cases” . There were 740 people ( seen and sampled five times ) who had no evidence of recurrent TT at any point during the follow-up period . Overall , the non-recurrent group tended to be younger ( mean age 48 . 3 years , compared to 54 . 1 years , t-test p<0 . 0001 ) and had less severe pre-operative trichiasis ( mean number of lashes 7 . 1 , compared to 13 . 8 lashes , Wilcoxon rank-rum p<0 . 0001 ) compared to the recurrent TT cases . To ensure that the non-recurrent “control” group was of a similar age and preoperative trichiasis severity , 122 individuals were randomly selected from the potential 740 , constrained by frequency matching from within the same combination of age group ( 18–29 , 30–39 , 40–49 , 50–59 , 60–69 , 70+ years ) and pre-operative trichiasis severity group ( 0 ( epilating ) , 1–5 , 6–9 , 10–19 , 20+ lashes ) as the 122 recurrent TT cases . The baseline distribution of age and trichiasis severity groups were the same for cases and controls , except in one instance , where there was one too few controls to frequency match ( Table 1 ) . There was no difference in gender , age , baseline trichiasis severity or suture randomisation arm between cases and controls ( Table 2 ) . All cases and controls had tarsal conjunctival scarring at baseline . However , individuals who subsequently developed recurrent trichiasis were more likely to have had tarsal conjunctival inflammation ( P2/P3 ) than the controls pre-operatively . A total of 1220 samples had been collected across the five time points from these 244 participants . The nine targets were measured in each of these . There were a small number of samples from each time point for which it was not possible to perform quantitative PCR due to an inadequate RNA sample ( Baseline four samples; 6-months three samples; 12-months eleven samples; 18-months six samples; 24-months four samples ) . For the purpose of this analysis these samples are treated as missing data . There was no significant difference between the two trial arms in the expression of any of the targets at any of the time-points . Therefore , the data are not adjusted for trial arm . Recurrent TT was consistently associated with about a 2-fold increase in S100A7 ( at the 0 . 05 level ) , on 4 out of 5 occasions . No other targets had a consistent association with recurrent TT ( Table 3 ) . Clinically apparent conjunctival inflammation ( P2/P3 ) , irrespective of whether there was recurrent TT or not , was consistently associated with increased expression of pro-inflammatory factors ( S100A7 , IL1B , CXCL5 ) and matrix metalloproteinases ( MMP9 , MMP12 ) across multiple time points ( Table 4 ) . Multivariable linear regression models for each target were fitted for baseline and a combination of the follow-ups ( random-effects model ) , Table 5 . Recurrent trichiasis remained significantly associated with increased expression of S100A7 after adjusting for other factors in these models , both before and after surgery . In an un-conditional logistic regression model for recurrent trichiasis and baseline factors , only conjunctival inflammation and S100A7 expression were significantly associated with subsequent recurrence ( Table 6 ) . Partial correlation coefficients were calculated for each target with each other for each time point . The total number of significant partial correlations at the 0 . 05 level is represented in Figure 1 . Consistent associations were found on 4 or 5 occasions between ( 1 ) S100A7 and NOS2A , ( 2 ) IL1B and CXCL5 , ( 3 ) IL1B and TNFA , ( 4 ) NOS2A and MMP7 , and ( 5 ) MMP9 and MMP12 .
Recurrent trichiasis is a challenging problem for prevention of blindness programmes as it renders the surgical intervention less effective , increasing the risk of sight loss . Multiple factors likely contribute to recurrence , which can broadly be subdivided into disease related and surgery related factors . Refinements of the operative procedure and improvements in surgical quality are anticipated to lead to a reduction in early recurrent trichiasis [2] , [14] . However , the risk of recurrence is also related to the severity of pre-operative disease and probably individual variations in early wound-healing events and therefore attributable to the underlying cicatricial disease process [2] , [12] , [25] . In this study we aimed to identify immuno-fibrogenic factors associated with recurrent TT developing over a two-year period . The choice of targets for this longitudinal gene expression study was guided by findings from the baseline case-control study of the conjunctival transcriptome , which compared individuals with established TT to people without disease from the same population [5] . This earlier study used a two-stage process to identify potential disease biomarkers and pathways: ( 1 ) transcriptome-wide microarray analysis , followed by ( 2 ) quantitative PCR in a large sample set to confirm findings . This identified a variety of pro-inflammatory ( IL1B , TNFA , S100A7 , CXCL5 , NOS2A ) and tissue remodelling factors ( MMP7 , MMP9 and MMP12 ) , which were associated with conjunctival scarring and trichiasis before surgery . Therefore , these were chosen as candidates to be investigated in this longitudinal study of recurrent trichiasis following surgery . In the present study the only gene found to have consistently increased conjunctival expression ( both before and after surgery ) in individuals who developed recurrent trichiasis was S100A7 ( Psoriasin ) . There was an approximately 2-fold increase in its expression on four out of the five time-points; this association remained significant after adjustment was made for other potential explanatory factors . Clinically visible conjunctival inflammation ( P2/P3 ) was also associated with increased expression of S100A7 . Multivariable models indicated that the associations between recurrent trichiasis , S100A7 expression and inflammation were independent . Unsurprisingly , the expression of several pairs of factors showed a degree of association on multiple occasions . We previously found S100A7 to be the most strongly up-regulated gene in the conjunctiva in TT cases relative to normal controls , in the same Ethiopian population and in Tanzanians with conjunctival scarring in the absence of TT [5] , [26] . The increase was more marked when the conjunctiva appeared inflamed ( P2/P3 ) [5] , [26] . We have also found S100A7 expression to be significantly increased in children with signs of clinically active trachoma [21] . However , it is difficult to determine in this longitudinal study following trichiasis surgery the potential contribution that S100A7 might make to the pathophysiology of recurrent trichiasis or progressive conjunctival scarring in general . At present there are no suitable animal models for this scarring process that can be manipulated to investigate its potential role . Psoriasin is a member of the S100 family , a diverse group of calcium binding , low molecular weight proteins , which are expressed in multiple tissues , particularly epithelial surfaces [27] . S100A7 was initially identified in psoriatic skin lesions , and linked to the inflammatory pathophysiology of the disease [28] . In healthy skin it is expressed only at minimal levels . Subsequently , it was found to be increased in other inflammatory skin conditions [27] . A number of epithelium-associated malignancies have increased expression of S100A7: carcinoma of the breast , lung , prostate , stomach and cutaneous squamous cell carcinoma . Aggressive tumour behaviour ( invasion and metastasis ) has been particularly associated with its expression [29]–[33] . S100A7 appears to mediates these effects through increasing inflammation and activation of MMPs , which have parallels in trachoma [34] , [35] . More recently , S100A7 has been identified as a potential biomarker for Alzheimer's disease , with marked increases in both cerebrospinal fluid and brain tissue [36] . The functions of S100A7 have only recently begun to be explored in detail . It is an anti-microbial peptide , protecting epithelial surfaces from bacterial infection [37]–[40] . The mechanism by which S100A7 exerts its direct antimicrobial effect is not fully understood; it is possibly through pore-formation , which permeabilises the bacterial cell membrane , or through zinc sequestration [37] , [38] . S100A7 also has some chemokine-like actions and may be particularly important in driving an innate immune response . It is chemotactic to both neutrophils and T lymphocytes [41]–[43] . It has been shown to stimulate neutrophils to produce various pro-inflammatory factors ( TNFα , IL-6 , IL-8 , CCL3 , CCL4 , CCL20 ) , produce reactive oxygen species ( ROS ) through NADPH oxidase , and to degranulate , releasing myeloperoxidase [44] . S100A7 has also been shown to cause keratinocytes to produce cytokines that probably promote Th1 and Th17 responses ( TNFα , IL-1α , IL-23 , MIP2 , RAGE ) [43] . Interestingly , stimulation of keratinocytes with a combination of IL-17A , IL-22 and TNFα increased S100A7 production , which may form a feed-back loop , amplifying inflammation in psoriasis and other conditions [43] . Little is known about the regulation of S100A7 . Some bacterial components , such as flagellin , trigger increased production of S100A7 , probably through TLR5 [45] . In addition , S100A7 production can also be stimulated by pro-inflammatory cytokines ( TNFα , IL-1α , IL-6 , IL-17 , IL-22 ) [33] , [43] , [46] . In breast cancer cell lines , IFNγ down-regulates S100A7 expression , through STAT1 transcriptional activity [47] . This may be of relevance to trachoma , as Th1 responses ( characterized by increased IFNγ ) are thought to be associated with a less scarred outcome , which could conceivably be partly mediated through suppression of S100A7 [48] . Chronic conjunctival inflammation is an important component of the pathophysiology of trachoma and it is a relatively frequent finding in people with cicatricial disease [2] , [49] . Repeated or persistent inflammation in childhood and early adult life is associated with scarring complications later in life [50] , [51] . The expression of IL1B , CXCL5 , MMP9 and MMP12 were increased in the presence of clinical inflammation on several occasions . We have previously reported associations between IL1B and MMP9 and conjunctival inflammation following surgery [20] . These factors are plausibly involved in inducing and regulating inflammation in the conjunctiva and may contribute to the underlying disease process , although in this study they were not associated with recurrent trichiasis . The expression of TNFA was not consistently associated with inflammation ( only one occasion ) , which is a pattern we have found in two earlier studies [5] , [20] . This study has a number of potential limitations . The outcome of interest was recurrent trichiasis . This can be a variable sign especially if the patient is practicing epilation . However , careful note was made of any evidence of epilation on examination . Secondly , measuring the expression of a gene does not necessarily equate to functional activity of its protein product . Positive findings will need further validation with alternative approaches , such as immunohistochemistry to detect the protein . We did not perform PCR testing for C . trachomatis because in this population ( which has an active azithromycin mass distribution programme ) at baseline we have previously reported that infection was very rare in adults with and without TT ( 0 . 1% ) and therefore considered it unlikely to be informative [5] . Anti-scarring therapies are increasingly used in ophthalmic surgery to limit cicatricial complication [52] . For example , specific inhibitors of TGFβ and MMPs are being investigated for use in glaucoma filtration surgery . We did not identify obvious potential therapeutic targets , such as a MMPs associated with recurrent trichiasis . It is possible that the MMPs play a role in the outcome of early post-operative wound healing events , although this was not the focus on this study . The observation that increased expression of S100A7 was consistently associated with recurrent trichiasis indicates that it may have a role in this disease process . In the light of new information becoming available about the contribution of S100A7 to other diseases , our findings suggest that this molecule warrants further investigation in trachoma .
|
Trachoma causes blindness through corneal damage from in-turned eyelashes ( trachomatous trichiasis [TT] ) . Trichiasis is treated surgically to correct the anatomical abnormality . Unfortunately , TT frequently returns following surgery , which again puts the person at risk of sight loss . Recurrent trichiasis is multifactorial . We investigated the possible role of various immuno-fibrogenic factors . To do this we operated on 1300 people with TT and followed them up every six months for a two-year period . On each occasion a conjunctival swab was collected for human gene expression analysis . We measured various factors that are thought to be important in inflammation and scarring diseases . The gene expression profile of people who developed recurrent TT was compared to a sample of those that did not have a recurrence . Recurrent TT was associated with increased expression of psoriasin ( S100A7 ) before surgery and on multiple occasions during a two-year follow-up period . S100A7 is able to promote inflammation and may contribute to the development of the scarring process in trachoma .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"neglected",
"tropical",
"diseases",
"trachoma"
] |
2012
|
Post-Operative Recurrent Trachomatous Trichiasis Is Associated with Increased Conjunctival Expression of S100A7 (Psoriasin)
|
The nascent polypeptide-associated complex ( NAC ) is a highly conserved but poorly characterized triad of proteins that bind near the ribosome exit tunnel . The NAC is the first cotranslational factor to bind to polypeptides and assist with their proper folding . Surprisingly , we found that deletion of NAC subunits in Saccharomyces cerevisiae rescues toxicity associated with the strong [PSI+] prion . This counterintuitive finding can be explained by changes in chaperone balance and distribution whereby the folding of the prion protein is improved and the prion is rendered nontoxic . In particular , the ribosome-associated Hsp70 Ssb is redistributed away from Sup35 prion aggregates to the nascent chains , leading to an array of aggregation phenotypes that can mimic both overexpression and deletion of Ssb . This toxicity rescue demonstrates that chaperone modification can block key steps of the prion life cycle and has exciting implications for potential treatment of many human protein conformational disorders .
Protein synthesis is an essential process undertaken by all organisms , but its necessity also presents cells with a myriad of challenges . An extensive network of molecular machines is active throughout translation , folding , and degradation in order to preserve protein homeostasis ( proteostasis ) . Perturbations to that machinery can have ripple effects that impact many cellular systems . Misfolded proteins are one such challenge to proteostasis . Improperly folded proteins are generally non-functional; thus , the correct folding and trafficking of polypeptides is essential to the maintenance of cellular viability [1] . Protein misfolding can lead to the induction of cellular stress responses , apoptosis , and cell death . In humans , protein misfolding diseases include Alzheimer’s disease , Parkinson’s disease , Huntington’s disease , and prion diseases such as Creutzfeldt-Jakob disease [1–3] . The complexity of protein folding is mirrored by the complexity of these incurable diseases; thus , increased understanding of the molecular basis of folding and misfolding will be crucial to improved treatment of various pathologies . Prions are a subset of misfolded proteins that are self-templating and stably propagated from cell to cell . In yeast , the intrinsically disordered domain of the translation termination factor Sup35 misfolds and aggregates to form the [PSI+] prion , which is cytoplasmically inherited via amyloid seeds [4–6] . [PSI+] is toxic under certain circumstances , including Sup35 overexpression , due to severe disruption of proteostasis as a consequence of excessive aggregation of Sup35 [7 , 8] . Nascent polypeptides begin to fold cotranslationally before protein synthesis has been completed by the ribosome [9] . Sup35 , not unlike other proteins , faces folding challenges immediately upon emergence from the ribosome exit tunnel . Proteins are protected from early misfolding by ribosome-bound protein chaperone families [10] . First , the nascent polypeptide-associated complex ( NAC ) interacts with nascent chains [11] , followed by the ribosome-associated complex ( RAC ) and the Hsp70 Ssb [12–15] . Cotranslational chaperone factors are of keen interest in the area of protein aggregation , as they have a bias towards substrates that are intrinsically disordered and amyloidogenic [16] . The NAC and Ssb-RAC systems are components of a larger molecular chaperone network within Saccharomyces cerevisiae [10 , 17] . However , the interactions between cotranslational folding factors and other players in the chaperone network have yet to be fully elucidated . Here , we describe a surprising mechanism for preventing aggregation-related cytotoxicity by manipulating cotranslational folding pathways . We utilized the [PSI+] prion as a model for protein misfolding and a reporter for the activities of the chaperone network . We screened for factors that , when disrupted , rescued the prion-dependent toxicity and restored viability . Surprisingly , disruption of β-NAC , a subunit of the NAC chaperone complex , was identified as one of the rescuing mutants in our screen . This counterintuitive result suggests that depletion of chaperones can , in some cases , rescue defects associated with misfolded proteins . Indeed , we found that deletion of NAC subunits has significant impact on the localization and activity of other cytosolic chaperones , the Hsp70 family in particular . We propose that altered localization and activity of chaperones can aid cells in the ability to maintain proteostasis when faced with severe folding challenges . As such , inhibition of the NAC presents a novel avenue for investigation into therapeutics to treat protein conformational disorders that may slow further aggregation of amyloidogenic proteins and suspend disease progression .
We set out to identify factors that modulate the toxic misfolding environment associated with the [PSI+] prion . Though [PSI+] is generally well-tolerated by cells , the overexpression of Sup35 in [PSI+] cells is cytotoxic [7] . To identify factors that could rescue this toxicity , we overexpressed Sup35 from a copper-inducible promoter and screened for colonies that overcame the toxicity phenotype while retaining the “strong” variant of [PSI+] ( S1A Fig ) . Upon sequencing , two toxicity-suppressing candidate colonies contained single gene disruptions of EGD1 . The EGD1 gene encodes Egd1 , the β-NAC subunit ( Fig 1A ) . The NAC is comprised of three subunits: Egd1 ( β subunit ) , Egd2 ( α subunit ) , and Btt1 ( β’ subunit ) , which are together known to play an important role in cotranslational folding and protein homeostasis ( proteostasis ) [18] . Our results indicate , for the first time , that deletion of NAC subunits may help to improve cellular health in the face of misfolding stress . Intrigued by this result , we tested whether deletion of other NAC subunits also rescued the [PSI+]-dependent toxicity caused by Sup35 overexpression [7 , 8] . We theorized that double- or triple-deletions , which would not be recovered by our screen , may exhibit stronger phenotypes . We created yeast strains containing combinatorial deletions of all NAC subunits , hereafter referred to as “NAC deletion strains , ” and tested them for growth in the presence of toxic Sup35 aggregates . We found that two double deletions , egd1Δegd2Δ and egd1Δbtt1Δ , strongly rescued the toxicity caused by the overexpression of Sup35 in [PSI+] cells ( Fig 1B ) . Interestingly , other deletion combinations and deletion of the whole NAC did not detectably overcome the toxicity , potentially due to individual subunit interactions that are not yet understood ( S1B Fig ) . It has been previously shown that impaired translation termination is responsible for the toxicity phenotype in [PSI+] cells overexpressing Sup35 [7] . Therefore , we hypothesized that a decrease in stop codon readthrough may be responsible for the toxicity rescue in the NAC deletion strains . To test this , we utilized a well-characterized genetic assay: the ade1-14 allele , which contains a premature stop codon in the ADE1 open reading frame . Yeast carrying the ade1-14 allele are unable to complete adenine biosynthesis , resulting in accumulation of a red pigment in cells grown on rich media that have faithful translation termination . The nonsense suppression that occurs in ade1-14 [PSI+] cells leads to completion of the adenine biosynthesis pathway , thus the colonies are white in color and are able to grow on media lacking adenine ( SD-Ade ) [19] . We spotted [PSI+] NAC deletion strains onto rich media ( YPD ) and SD-Ade plates to assess their nonsense suppression phenotypes . All NAC deletion strains formed white colonies and grew strongly on SD-Ade ( Fig 2A ) , indicating similar levels of nonsense suppression in all strains in the context of endogenous Sup35 . To assess nonsense suppression quantitatively , we measured stop codon readthrough via the expression of β-galactosidase from a set of reporter plasmids [20] . Quantification indicated that there was no significant change in nonsense suppression in any of the NAC deletion strains relative to the WT [PSI+] control ( Fig 2B ) . We concluded that a decrease in nonsense suppression was not the mechanism by which the NAC deletion strains rescued [PSI+]-associated toxicity . We considered the possibility that a global change in translation was playing a role in toxicity rescue . The quintuple deletion of subunits in a nacΔssbΔ strain has been demonstrated to cause a defect in ribosome biogenesis [21] , and fewer translating ribosomes may allow cells to tolerate reduced Sup35 function and withstand toxicity by improving the ratio of translation termination factors . We analyzed ribosome profiles for all NAC deletion strains and found no differences in the peak heights or integrated peak areas ( Fig 2C ) , nor did we observe the formation of ribosomal half-mers . Thus , translation is not globally perturbed in the NAC deletion strains relative to WT . We next questioned whether the toxicity rescue indicated a change in prion variant of the NAC deletion strains . To assess the Sup35 aggregates biochemically , we used semi-denaturing detergent agarose gel electrophoresis ( SDD-AGE ) . We found that the overall distribution of SDS-sensitive population of Sup35 aggregates did not change following growth in media without copper ( Fig 2D ) , confirming that all NAC deletion strains retained the strong [PSI+] prion variant . Thus , the toxicity rescue phenotype exhibited by the NAC deletion strains was not due to loss or weakening of the [PSI+] prion . We were surprised that the NAC deletions rescued [PSI+]-related Sup35 overexpression toxicity without observable changes to the prion or to nonsense suppression . To further investigate the reduction of Sup35 toxicity , we examined the aggregation and solubility of the Sup35 prion aggregates in all NAC deletion strains . We sought to do so by a method that would allow observation of intact aggregates in cells , rather than simply the SDS-resistant aggregates detected by SDD-AGE ( Fig 2D ) . We transformed a copper inducible , GFP-tagged Sup35 ( pRS314CUP1 Sup35GFP ) into the WT and NAC deletion strains . We induced Sup35-GFP expression by the addition of small amounts ( Vf = 50μM ) of CuSO4 to the culture media and monitored GFP localization by fluorescence microscopy . Sup35-GFP exhibited a diffuse pattern of fluorescence in [psi-] cells ( Fig 3A ) , consistent with non-aggregated Sup35 . By contrast , Sup35-GFP was observed in a single fluorescent focus in the WT [PSI+] strain ( Fig 3A ) . Most NAC deletion cells also contained one major fluorescent puncta . However , the egd1Δegd2Δ strain harbored multiple fluorescent puncta throughout the cytoplasm ( Fig 3A , S2A Fig ) . Interestingly , this phenotype was not apparent in the toxicity-rescuing egd1Δbtt1Δ deletion , suggesting that a similar change in aggregate distribution was not the rescuing effect . However , it is plausible that there is an altered aggregation pattern that is too subtle to be detected via fluorescence microscopy . Importantly , the WT and all NAC [PSI+] deletion strains exhibit insoluble Sup35 at steady-state levels of Sup35 expression ( Fig 3B ) , as expected with the presence of the [PSI+] prion . We then asked if the multiple aggregates in the egd1Δegd2Δ strain were due to altered joining of monomeric Sup35 to existing amyloid structures . To address this question , we induced the expression of Sup35-GFP and tracked its solubility over time . In lysates of WT [PSI+] strains , an abundance of Sup35-GFP appears in the pellet fraction at two hours post-induction of Sup35-GFP expression due to formation of insoluble aggregates ( Fig 3C , S2B Fig ) . Remarkably , we did not detect Sup35-GFP in the pelleted fractions of egd1Δegd2Δ or egd1Δbtt1Δ strains up to four hours post-induction ( Fig 3C ) , consistent with at least transiently enhanced solubility of Sup35 . The amount of induced Sup35-GFP was consistent between all tested strains ( S2C Fig ) . One interpretation of this result is that the joining of newly synthesized Sup35 to prion aggregates is delayed in the egd1Δegd2Δ or egd1Δbtt1Δ strains . We hypothesize that the structure of nascent Sup35 in the NAC deletion strains renders Sup35 impaired in joining to pre-existing aggregates due to altered , and possibly improved , folding of nascent Sup35 . This is supported by the decrease in de novo [PSI+] induction in the toxicity-rescuing NAC deletion strains ( S2D Fig ) . We next considered the possibility that the putative changes to Sup35 structure were brought about by altered interactions with chaperone proteins . Sup35 is a client of many cytosolic chaperones [22–24] . We reasoned that NAC deletion may spur a compensatory response of other molecular chaperones such that the deletion strains may , in fact , be folding some proteins more efficiently than WT . When the NAC is depleted , the first major chaperone to interact with nascent chains is the Hsp70 Ssb , in conjunction with the ribosome-associated complex ( RAC ) [16 , 21 , 25] . Ssb has dual functions as a ribosome-bound and cytosolic chaperone . In addition , it has a cytosol-only Hsp70 homolog Ssa [23] . Both chaperones have been shown to interact with Sup35[26–28] . We hypothesized that NAC deletion would impact the abundance , presence , or activity of the Hsp70s . We assessed total levels of Ssa and Ssb in each [psi-] and [PSI+] NAC deletion strain and found no difference relative to the wild type ( S3A Fig ) . We theorized that NAC deletion may increase the role of Ssb-RAC in cotranslational folding , as this protein complex is next in line to receive nascent chains following the NAC . This increased folding pressure upon Ssb-RAC as a result of NAC deletion might also localize a larger fraction of Ssb to the ribosome and nascent chains while titrating Ssb away from the available cytosolic pool . The titration would reduce the overall availability of cytosolic Ssb relative to Ssa , thereby creating a chaperone imbalance . We began by assessing the general effects of imbalanced Hsp70s in strains with intact NAC subunits . We challenged proteostasis by overexpressing Sup35 in a WT [PSI+] strain with endogenous Ssa expression and found that the cells grew as expected , with a moderate toxicity phenotype and consistent Sup35 expression ( S3B Fig , Fig 4A ) . However , recapitulating an imbalance by deleting Ssb1 from this strain resulted in enhanced toxicity ( Fig 4A ) . This indicates that cells are sensitive to chaperone balance in the presence of folding-challenged substrates . We then sought to verify that the balance of Ssb1 relative to Ssa1 was specifically affecting toxicity . To do so , we exacerbated the Hsp70 imbalance by overexpressing Ssa1 in WT and ssb1Δ strains . We again challenged proteostasis with overexpression of Sup35 , but at a lower level than in the previous experiment . Slightly imbalanced Hsp70s ( WT strain with SsaOE ) led to poor growth , and severely imbalanced Hsp70s ( ssb1Δ with Ssa1OE ) led to pronounced toxicity ( Fig 4B ) . Reintroduction of Ssb1 rescued the phenotype ( Fig 4B ) . We concluded that cells are sensitive to the balance between Ssa and Ssb , and that the severity of the toxicity phenotype correlates with the severity of the imbalance . We then sought to extend our analysis to the NAC deletion strains . As above , we overexpressed Ssa1 from a constitutive promoter in the WT and all NAC deletion strains; we observed no change in either growth or prion phenotype in the absence of Sup35 overexpression ( S4 Fig and Fig 4C , YPD plates ) . However , when we subjected the same strains overexpressing Ssa to minor Sup35 overexpression , we observed a severe toxic phenotype in the egd1Δegd2Δ and egd1Δbtt1Δ strains ( Fig 4C , selection plates ) that mimicked the strong toxicity exhibited by ssb1Δ strains under the same conditions . Overexpression of Ssb1 did not cause cytotoxicity ( Fig 4C ) . Ssa1 overexpression in the WT and single deletion strains did not perturb growth as severely ( S5 Fig ) , possibly because the chaperone imbalance remained within a tolerable range . However , the toxicity in the egd1Δegd2Δ and egd1Δbtt1Δ strains indicates that Ssa1 overexpression pushes an already-imbalanced system into a highly detrimental state . This suggests that NAC deletion mimics Ssb deletion phenotypes despite the unchanged expression levels of all tested chaperones ( S3 Fig ) , thereby supporting the concept of cellular localization changes and an imbalance of Ssb relative to Ssa . We next questioned whether NAC deletion could phenocopy a chaperone imbalance in the absence of Sup35 overexpression . We tested the growth of [PSI+] NAC deletion strains under conditions that are disadvantageous to the ssb1Δ strain . We spotted the NAC deletion strains onto plates containing 120μg/L HygromycinB ( HygB ) , as it is known that ssb1Δ yeast are sensitive to the fungicide [29] . Though WT yeast grew on HygB plates , several of the NAC deletion strains , including egd1Δegd2Δ and egd1Δbtt1Δ , exhibited poor growth consistent with depleted Ssb ( Fig 4D ) . The growth similarities between NAC and Ssb deletion strains support the possibility that the NAC-induced chaperone imbalance may be altering the functionality of Ssb in ways that mimic its deletion . Though some NAC deletion strains exhibited phenotypes related to Ssb depletion , all strains exhibited WT levels of all tested chaperones ( S3 Fig ) . We hypothesized that NAC subunit deletion led to alterations in Ssb localization and availability due to an additional requirement for Ssb at the ribosome . We reasoned that the loss of NAC subunits would cause Ssb to assist in the folding activities that were typically controlled by the NAC . To test this , we returned to our ribosome profile analysis ( Fig 2C ) and assessed the proteins present in the peak fractions . We theorized that more Ssb would be present in the polysome fractions of the egd1Δegd2Δ strain relative to the WT , because folding-challenged nascent chains would require more extensive cotranslational interactions with Ssb . We probed for the presence of Ssb in the polysome and monosome peaks and normalized to the amount of ribosomal protein Rpl3 detected in the same peaks ( Fig 5A ) . The amount of Ssb in the polysome fractions was indeed increased in the egd1Δegd2Δ deletion strain relative to the WT , indicating a greater proportion of Ssb comigrating with polysomes . Thus , the localization of Ssb is altered in the NAC deletion strains relative to the WT , potentially modulating nascent Sup35 folding and related cytotoxicity . To further probe the theory of Ssb relocalization , we sought to assess Ssb binding to aggregated Sup35 . We hypothesized that an increase of Ssb binding to nascent chains would lead to a corresponding decrease in the pool of available ( non-polysome bound ) Ssb . This would cause decreased Ssb binding to Sup35 aggregates . We performed co-immunoprecipitation experiments in [PSI+] cells using a Sup35 antibody and probed for the presence of Ssb1/2 . We found that the amount of Ssb1/2 that co-immunoprecipitated with Sup35 was reduced in several of the NAC deletions ( Fig 5B ) , with the effect being most pronounced in egd1Δegd2Δ ( 50 . 0% reduction in co-immunoprecipiation , ±8 . 1% ) . The steady state levels of Ssb1/2 were not reduced in the NAC deletion strains ( S3 Fig ) ; thus , the decreased interactions with Sup35 represent a change in the binding between Ssb and its pool of post-translational substrates . This result , in combination with the enhanced presence of Ssb at translating ribosomes , indicates that Ssb localization shifts as a consequence of NAC subunit deletion . We hypothesized that the imbalanced Hsp70s may impact other molecular chaperones in the NAC deletion strains . Ssa and Ssb both interact with the disaggregase Hsp104 , which is required for the propagation of all but one of the known yeast prions [19 , 30] . We found no observable difference in steady state levels of Hsp104 ( S3 Fig ) . Hsp104 activity was also not different between the WT and NAC deletion strains by thermotolerance tests ( S6 Fig ) . Therefore , the observed toxicity rescue was not due to a change in presence or functionality of Hsp104 . Inhibition of Hsp104 is thought to cure prions by preventing fiber fragmentation , which impairs inheritance of seeds [19] . Overexpression of Hsp104 specifically cures [PSI+] , and its curing ability is influenced by the Hsp70s . Ssb overexpression in conjunction with Hsp104 overexpression promotes loss of [PSI+] , while Ssa1 overexpression prevents Hsp104-mediated curing [23 , 31 , 32] . Therefore , we predicted that Hsp104 curing efficiency may be altered in the NAC deletion strains due to the imbalance of Ssa and Ssb . Hsp104 overexpression efficiently cures the [PSI+] prion , an effect that is inhibited by simultaneous Ssa overexpression . We wondered whether the Hsp70 imbalance in the NAC deletion strains would mimic this phenotype . We transformed each of the NAC deletion and WT strains with a plasmid that constitutively overexpresses Hsp104 and verified that Hsp104 levels were increased while not altering the growth of the cells or amount of Sup35 expressed ( S7 Fig ) . We then phenotypically characterized the transformants by streaking them onto rich media . We again took advantage of the red/white colormetric assay that allowed us to track the presence of the yeast prion: white colonies harbor Sup35 aggregates while red colonies have been cured of [PSI+] . Surprisingly , all NAC deletion strains ( with the exception of egd1Δ ) were resistant to Hsp104-mediated curing of [PSI+] , as a significant proportion of their colonies remained white on rich media on first pass ( Fig 6A and 6B ) . This result was confirmed by performing an SDD-AGE assay to visualize the SDS-resistant Sup35 aggregates . Following continued growth with Hsp104 overexpression , aggregated Sup35 was still present in the NAC deletion strains but not the wild type ( Fig 6C ) . This demonstrated that prion curing was less efficient in strains where NAC depletion was affecting chaperone balance and mimicked Ssa1 overexpression . We then hypothesized that a heritable change in [PSI+] conformation or structure may have created Sup35 aggregates that were poor Hsp104 binding partners . These altered aggregates may thus be resistant to refolding by Hsp104 activity independent of a chaperone imbalance . We performed cytoduction experiments to test the curing of prions from NAC deletion strains in a WT genetic background [33] . We transferred Sup35 aggregates from WT and NAC deletion strains into wild type [psi-] strains by cytoplasmic transfer so that the resulting yeast were genetically WT but contained [PSI+] from the cohort of NAC deletion strains . We then induced Hsp104 overexpression and found that all of the cytoduced strains were cured as efficiently as WT ( Fig 6D , S8A Fig ) . Thus , the heritable Sup35 aggregate structure was not the cause of differential Hsp104 curing; rather , the curing resistance exhibited by the deletion strains was a consequence of the genetic disruption of the NAC . As the NAC deletion strains exhibited resistance to curing by Hsp104 overexpression , we wondered if they would also resist curing by Hsp104 inactivation . To test this , we passaged the NAC deletion strains on media containing 5mM guanidine hydrochloride ( GdnHCl ) , a strong inhibitor of Hsp104 [34] , which cures all known yeast prions . The WT and NAC deletion strains demonstrated equal curability on GdnHCl plates ( S8B Fig ) . Taken together , these results suggest that the differential effects of the NAC interactions between Hsp104 and Sup35 are related the activity of co-chaperones . Given the toxicity rescue phenotype resulting from loss of NAC subunits and altered chaperone activity , we considered that NAC deletion may bring about a broader modification to proteostasis and the cellular response to protein misfolding . First , we probed the ability of the NAC deletion strains to manage global protein misfolding . We challenged cells with canavanine , an arginine analog that induces misfolding [35] , and found that most NAC deletion strains were able to survive high levels of the compound ( Fig 7A ) , indicating that loss of NAC subunits allows cells to better tolerate the adverse effects of misfolding . To determine how NAC deletion strains tolerate elevated protein misfolding , we asked whether misfolded proteins were differentially acted upon by protein quality control machinery in the NAC deletion strains . We questioned whether the NAC deletion strains resist canavanine-induced misfolding due to increased activity of the ubiquitin-proteasome system ( UPS ) . We assessed the presence of ubiquitinated species in the NAC deletion strains in the presence and absence of canavanine and found no differences between the WT and the most stabilized NAC deletion strains ( Fig 7B ) . Additionally , challenging the ubiquitin-proteasome system with heat stress or the proteasome inhibitor MG132 showed no differences between the WT and the NAC deletion strains ( S6 Fig and S9A Fig ) . Thus , cellular viability in the presence of canavanine is not related to increased protein degradation as mediated by the UPS . We considered the possibility that NAC deletion strains may package misfolded proteins into insoluble aggregates , rendering them nonfunctional but nontoxic . We performed solubility assays to visualize total protein aggregation in the NAC deletion strains , and observed no changes relative to the wild type ( Fig 7C , S9C Fig ) . Therefore , we concluded that there is no gross global difference in the way proteins are packaged or degraded in the NAC deletion strains relative to the WT , indicating that the toxicity rescue phenotype is not related to enhanced stress response or turnover of misfolded proteins . Rather , like the prion-dependent effect , the chaperone imbalance renders cells generally resistant to misfolded proteins .
Here , we show that the NAC affects the localization and activities of other molecular chaperones . Our model ( Fig 8 ) suggests that deletion of the α- and β-NAC subunits cause relocalization of the Hsp70 Ssb away from the available pool of cytosolic chaperones and to translating ribosomes , creating an imbalance that mimics Ssb deletion phenotypes . This depletion of Ssb from Sup35 aggregates serves to change the interaction of the [PSI+] prion with other chaperones , including Hsp104 , which is less able to efficiently cure [PSI+] in the NAC deletion backgrounds . The deletion of the NAC does not impact expression levels for any of the proteins examined in this study ( S3 Fig ) ; thus , the observed phenotypes are due to changes in localization and functionality . These chaperone modifications correspond to an alteration in the aggregation pattern of the [PSI+] prion and the reduced ability of newly synthesized protein to join pre-existing aggregates . This alteration can be beneficial when cells are challenged with a toxic prion , presumably due to more active engagement of chaperones with nascent polypeptide chains where there is a high risk for misfolding [16] . We suggest a model of cotranslational folding that recruits cytosolic chaperones to nascent polypeptides in a manner that can rescue the toxicity associated with proteins prone to misfolding . Additional mechanistic studies will be necessary in order to determine the extent of Ssb activity on nascent chains in response to NAC deletion and how Ssb may differentially respond to the presence or absence of NAC subunits . The toxicity rescue effect of NAC subunit deletion was a surprising result . Deletion of cotranslational folding factors would not be expected to rescue toxicity associated with a prion-forming protein . We propose that deletion of NAC subunits creates an environment where the ribosome-associated complex and Ssb ( RAC-Ssb ) are the first fully-functional chaperones that interact with nascent polypeptides . Nascent polypeptides encountering the RAC-Ssb system in the NAC deletion strains are presumably less protected or folded than in WT cells . The RAC-Ssb system , compensating for loss of NAC subunits , might retain Ssb on nascent polypeptides , thereby reducing free Ssb elsewhere in the cell . Interestingly , deletion of the whole NAC did not recapitulate all of the phenotypes associated with the double deletions ( summarized in S1 Table ) . In particular , whole-NAC deletion did not rescue [PSI+]-related Sup35 overexpression toxicity . Deletion of the entire NAC may exacerbate the chaperone imbalance in such a way that leads to a harmful depletion of the cytosolic pool of Ssb . However , in other cases , the triple NAC deletion did mimic phenotypes associated with the double deletions . For example , NAC deletion confers resistance to hygromycin B and increases resistance to Hsp104-mediated curing . This leads us to hypothesize that there is a function for each of the NAC subunits that can persist independent of the complex . For example , hygromycin B resistance is demonstrated by every strain that has a deletion of EGD1 , indicating that this subunit may be particularly important in modulating the interaction between nascent chains and molecular chaperones . Further , in the NAC deletion strains , any subunits that remain in the cell might act independently on nascent chains or upon misfolded cytosolic proteins . The precise function of all of the NAC subunits remains unclear and we look forward to future studies that will shed light on this dynamic complex . The ability of NAC deletion strains to mimic Ssb deletion phenotypes led us to question Ssb functionality in a NAC-depleted background . However , we have demonstrated that Ssb deletion is toxic in the presence of Sup35 overexpression , indicating that chaperone function is necessary . Further , perturbations that prevent Ssb association with the ribosome have been shown to enhance yeast sensitivity to Sup35 overexpression [36] . We suggest that the toxicity rescue observed in the NAC deletion strains is due to a shift in Ssb activity to nascent proteins . This change in localization and/or activity leads to a decrease in available Ssb relative to available Ssa . Though we suggest an increase in Ssb localization to nascent polypeptides as a result of NAC deletion , we did not observe the distribution of Ssa to be affected . Ssa does not have an established role in cotranslational folding and is not ribosome-associated . Thus , in NAC deletion backgrounds in which Ssb becomes relocalized , the shift in the amount of available cytosolic Ssb relative to Ssa creates local imbalances between the Hsp70s at the ribosome , in the unbound cytosolic pool , and at prion aggregates . Thus , these strains can simultaneously exhibit phenotypes mimicking Ssa depletion ( reduced de novo [PSI+] formation , S2 Fig ) , Ssa overexpression ( resistance to Hsp104-mediated curing , Fig 6 ) , Ssb deletion ( sensitivity to HygB , Fig 4 ) , and Ssb overexpression ( prion toxicity rescue , Fig 1 ) . This spectrum of effects suggests that neither Ssa nor Ssb has inhibited activity in the NAC deletion strains . We were surprised that Ssa1 overexpression resulted in a toxicity phenotype in conjunction with slight Sup35 overexpression in [PSI+] NAC deletion strains . We hypothesize that Ssa overexpression sequesters an essential cofactor or substrate from Ssb; for example , an Hsp40 or a nucleotide exchange factor , which would in turn reduce Ssb’s folding capabilities . In the NAC deletion strains , the enhanced requirement of Ssb to fold nascent polypeptides would cause any perturbation of Ssb to be harmful to proteostasis and cellular health . Future studies will examine the competition between Hsp70s for multiple cofactors , a relationship that is not fully understood [37 , 38] . Titrating Ssb away from the free pool of molecular chaperones has several effects on the cell and on the [PSI+] prion . Increasing the contact between Ssb and nascent polypeptides may cause Ssb to interact earlier with unfolded or misfolding Sup35 . This would in turn inhibit the ability of nascent Sup35 to efficiently join aggregates , as Ssb overexpression is known to promote loss of [PSI+] [23] . In the most extreme case , the egd1Δegd2Δ strain , this joining defect manifests as fractured Sup35-GFP aggregates as viewed microscopically . This reorganization of aggregates may either release or reduce interaction with cofactors that are toxically sequestered during normal amyloid formation [7] , leading to the rescue phenotype exhibited most strongly by the egd1Δegd2Δ strain . It is likely that other NAC deletion strains undergo similar chaperone reorganization , but to a lesser extent depending on which NAC subunits remain to act upon nascent chains . This slight chaperone imbalance would lead to weaker phenotypes that evade detection . For example , the egd1Δbtt1Δ strain does not show altered Sup35-GFP aggregation , yet exhibits a joining defect via solubility assays . This strain also rescues prion-related toxicity , albeit to a weaker extent than the egd1Δegd2Δ strain . The stable propagation of [PSI+] by nontoxic Sup35 aggregates indicates that NAC deletion , and the subsequent chaperone imbalance , slows the toxic addition of Sup35 monomer to existing aggregates . Retention of aggregates without toxicity has implications for mammalian protein misfolding disorders that are spread via oligomeric species [39] . By reducing monomer joining onto existing amyloid , NAC deletion is blocking a key step in the prion life cycle . The resistance to global protein misfolding induced by canavanine in the NAC deletion strains ( Fig 7A ) indicates that depletion of the NAC , and subsequent functional substitution by other chaperones , can protect cells against non-prion misfolding . Future studies are needed to determine the effects of NAC deletion on the propagation and stability of additional fungal prions and other amyloidogenic proteins . In the context of human disease , amyloid plaque formation may be slowed or stopped if a similar mechanism can be unveiled . The fact that chaperone deletion can be beneficial to cells , even in the face of protein misfolding stress , is a counterintuitive result . However , there is a growing body of evidence to support chaperone inactivation as a mechanism via which disease progression may be slowed . Many of these studies have focused on cancer [40] , but recent research has found that Hsp70 imbalance leads to increased aggregation of the Alzheimer’s-related protein tau [41] and that Hsp70 inhibition can promote tau clearance [42] . Taken together , these studies highlight the importance of chaperone balance on maintaining cellular health , and implicate genetic and pharmacologic inhibition of chaperones , Hsp70s in particular , as a promising therapeutic avenue . Our work builds upon the recent discoveries that the NAC can delay protein aggregation and provide feedback to translation machinery [43] , assist with general protein folding and ribosome biogenesis [21] , and that individual subunits have distinct functionalities related to protein folding and rescue of aggregation [44] . Together with our findings regarding the role of NAC subunits in regulating chaperone balance , this research points to the NAC as a major component in the protein homeostasis network . The NAC’s known significance in yeast and its ubiquity in Eukarya should motivate further investigation this multifunctional and essential complex .
Yeast were cultured and transformed using standard techniques [45] . All deletion strains were created from the same 74D-697 [RNQ+][PSI+] parent . Genetic deletions were made by replacing the coding regions of EGD1 , EGD2 , and BTT1 with KANMX4 , loxP-HIS3MX6-loxP , and loxP-URA3MX-loxP; knockouts were confirmed by auxotrophic markers and colony PCR . Strains and plasmids are available in the supplemental experimental procedures . Cells expressing pCUP1-SUP35NM-GFP were grown overnight in SD-Ura and Sup35NM-GFP expression was induced by the addition of CuSO4 to a final concentration of 50μM . After 2 hours of induction , cells were washed twice in 1X PBS , resuspended in 1X PBS , transferred to a 12-well glass slide ( Erie ) , and observed with an Olympus FV1200 laser scanning confocal microscope fitted with a 100x oil immersion objective . SDD-AGE and colorimetric assays were performed as previously described [46] . Antibodies utilized in this study are available in the supplemental experimental procedures . Cytoduction was performed similarly to previous studies [47] to transfer a medium strain of [RNQ+] into [rnq-][psi-] strains ( S2 Table ) . Putative cytoductants were selected on SGly-Ura and examined for accuracy by assessing auxotrophic markers . Prion transfer was confirmed by color on YPD and growth on SD-Ade plates . Haploid cytoductants retained the WT nucleus of the recipient strain and the strong [PSI+] prion aggregates of the donor strain . Ribosome fractions were collected as previously described [48] . Protein precipitation of fractions was performed by TCA precipitation , followed by SDS-PAGE and Western blotting for the presence of Ssb and Rpl3 . Quantification was performed with ImageJ , and background signal was subtracted from each measurement . The detected protein in the polysome fractions was divided by the detected protein in the monosome fraction as a way of controlling for the total amount of detected Ssb bound to ribosomes . We performed this analysis for Ssb , and scaled the quantification by the same method for the ribosomal protein Rpl3 . This controlled for the total amount of protein in the polysome versus the monosome and allowed us to quantify changes in Ssb relative to the total ribosomal protein . The fraction of polysome-associated Ssb was normalized to the amount of polysome-associated Rpl3 from the same blot for both WT and egd1Δegd2Δ in triplicate . Thus , the quantification metric was calculated in this manner: ( SsbinpolysomeSsbinmonosome ) ( Rpl3inpolysomeRpl3inmonosome ) Yeast strains transformed with pCUP1-SUP35 were grown overnight in SD-Trp media . At t = 0 , CuSO4 was added to the media to a final concentration of 50μM . Aliquots were removed at indicated time points post-induction and cells were washed , pelleted , and frozen in liquid N2 prior to use . Cell lysis and protein extraction was adapted from the ball mill method [49] . Yeast strains were grown overnight in 10ml YPD or SD media . Cells were lysed in buffer ( 50mM Tris pH 8 , 150mM NaCl , 1mM EDTA , 0 . 2% Triton X-100 , protease inhibitors ) with acid-washed glass beads ( Sigma ) for 2x3 minutes in a multi-tube vortexer ( Scientific Industries ) . Lysates were incubated with 1μl Sup35 antibody at 4°C for 2 hours , and 40ul of a 50% slurry of Protein G sepharose beads ( GE ) and lysis buffer was added and tubes were incubated at 4°C overnight . An unbound fraction was retained and beads were washed 3X in lysis buffer and resuspended in SDS-PAGE sample buffer as the bound fraction . Fractions were boiled in sample buffer for 5 minutes before SDS-PAGE ( 10% polyacrylamide ) and Western blotting with enhanced chemiluminescence ( G-Biosciences ) and film ( GeneMate ) . Bands were quantified with ImageJ and normalized to immunoprecipitated Sup35 . Yeast were cultured in rich media under homeostatic conditions . Cells were lysed in buffer ( 100mM Tris pH 7 . 5 , 200mM NaCl , 1mM EDTA , 5% glycerol , protease inhibitors ) with glass beads as described above . A “total” fraction was retained , and then lysates were centrifuged at 250 , 000xg in a TLA100 rotor in an Optima TLX Ultracentrifuge ( Beckman Coulter ) . The supernatant was retained as the “soluble” fraction , and then the pellets were washed in lysis buffer and centrifuged again . Supernatant was discarded and the pellets were resuspended in a 1:1 ratio of lysis buffer to RIPA buffer ( 50mM Tris pH 7 , 200mM NaCl , 1% Triton X-100 , 0 . 5% Na deoxycholate , 0 . 1% SDS ) . Following SDS-PAGE , gels were stained with coomassie blue to visualize the total protein content of each fraction .
|
Misfolded proteins can be toxic to cells , causing pathologies such as Alzheimer’s disease , Parkinson’s disease , prion diseases , and ALS . One mechanism by which organisms combat protein misfolding involves molecular chaperones , proteins that help other proteins fold correctly . Here , we describe a novel role for a family of chaperones called the nascent polypeptide-associated complex ( NAC ) . The NAC is a group of proteins that exist in all multicellular organisms , yet we do not fully understand its function . Using yeast as a model system , we have found that deletion of NAC subunits can reduce the toxicity associated with misfolded proteins . This work has implications for human protein misfolding diseases , as modulation of the NAC may present a viable therapeutic avenue by which to slow the progression of neurodegeneration and other protein conformational disorders .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"polyribosomes",
"toxicology",
"toxicity",
"materials",
"science",
"chaperone",
"proteins",
"protein",
"structure",
"molecular",
"biology",
"techniques",
"cellular",
"structures",
"and",
"organelles",
"protein",
"misfolding",
"polypeptides",
"research",
"and",
"analysis",
"methods",
"proteins",
"hyperexpression",
"techniques",
"molecular",
"biology",
"molecular",
"biology",
"assays",
"and",
"analysis",
"techniques",
"ribosomes",
"gene",
"expression",
"and",
"vector",
"techniques",
"biochemistry",
"solubility",
"peptides",
"cell",
"biology",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"material",
"properties",
"macromolecular",
"structure",
"analysis"
] |
2016
|
Prion-Associated Toxicity is Rescued by Elimination of Cotranslational Chaperones
|
Genome-scale metabolic models have become the tool of choice for the global analysis of microorganism metabolism , and their reconstruction has attained high standards of quality and reliability . Improvements in this area have been accompanied by the development of some major platforms and databases , and an explosion of individual bioinformatics methods . Consequently , many recent models result from “à la carte” pipelines , combining the use of platforms , individual tools and biological expertise to enhance the quality of the reconstruction . Although very useful , introducing heterogeneous tools , that hardly interact with each other , causes loss of traceability and reproducibility in the reconstruction process . This represents a real obstacle , especially when considering less studied species whose metabolic reconstruction can greatly benefit from the comparison to good quality models of related organisms . This work proposes an adaptable workspace , AuReMe , for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines . At each step , relevant information related to the modifications brought to the model by a method is stored . This ensures that the process is reproducible and documented regardless of the combination of tools used . Additionally , the workspace establishes a way to browse metabolic models and their metadata through the automatic generation of ad-hoc local wikis dedicated to monitoring and facilitating the process of reconstruction . AuReMe supports exploration and semantic query based on RDF databases . We illustrate how this workspace allowed handling , in an integrated way , the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae . Among relevant applications , the latter reconstruction led to putative evolutionary insights of a metabolic pathway .
The emergence of technologies able to produce massive data in all omics sciences has raised new challenges to handle , connect , exploit and distribute such information . Genome-scale metabolic models ( GSMs ) represent a successful application of integration of various types of omics data . GSMs are structured knowledge bases describing a specific organism metabolism [1] . They are characterized by two main features . First , they describe an organism metabolic network by incorporating biochemical reactions at a genome scale and associating them to the corresponding enzymes and their coding genes . Second , through formal mathematical formulation they can exploit this knowledge and predict the state of the network in different growth scenarios [2] . Prediction of phenotypes has allowed GSMs to be applied for several purposes such as , guiding metabolic engineering efforts to achieve an increased production of target metabolites [3] , identification of drug targets [4] and more recently , prediction of interactions in microbial communities [5] . To accomplish such applications successfully , an effort to correctly incorporate all the relevant available information must be made first , so that the quality of the reconstructed GSM is the best possible . To this end , a well-described protocol for generating high-quality GSMs has been made available [6] . Additionally , there are several databases such as KEGG [7] , BioCyc [8] , BiGG [9] or Model SEED [10] that aggregate metabolic data on which GSMs can be built [11 , 12] . For instance , the contents of the MetaCyc database have been curated from 54 , 000 articles [8] . Moreover , many independent methods have been developed to generate GSMs , mostly based on the aforementioned databases , including some toolboxes and workspaces . These latter allow a user to chain several tools into pipelines and have proven their efficiency in building high-quality GSMs . Among them are Pathway Tools [13] , the Raven Toolbox [14] and The SEED [10] . Pathway Tools was the first platform connecting biological metadata by relying on the BioCyc database via their own internal format . It also ensures traceability and reproducibility for the tools implemented in the platform . The Raven Toolbox integrated multiple data source types to encompass the variability of available data in reconstruction processes . The SEED proposed a complete automatic generation of models for prokaryotes and plants . There are also online workspaces such as Galaxy [15] or KBase [16] that enable the creation and customization of pipelines , while exploiting their own intrinsic databases . Most of these platforms internally trace the source of every reaction and metabolite in a GSM , that we call process metadata . For instance , Pathway Tools supports the use of evidence codes , citations , and user comments to document the origin and reason why information is included in a model . This information allows reports to be produced for comparing different versions of a model provided that the model construction is completely built within a single platform . Thiele and Palsson’s protocol steps are generally followed during reconstructions although customized to the availability of data sources and tools . As an example , the study of non-model organisms may involve comparisons to genomes and metabolisms of several taxonomically-related organisms . In such cases , the output of a main platform requires adjustments assisted by a choice of specialized tools . In this sense , as illustrated in Table 1 ( see Results ) , many recent GSMs were obtained by combining a major platform with additional methods relying on several databases , leading to “à la carte” reconstruction pipelines . The output of such personalized pipelines is exported in standard formats such as SBML or stoichiometric matrices , leaving out information about sources of reactions ( e . g . , the method and reason why a reaction was added to a model ) and process metadata which cannot be recovered with versioning systems . The reproducibility of these personalized reconstructions is threatened by the lack of metadata availability caused by the use of multiple methods and toolboxes in “à la carte” pipelines . Hence , tracking of process metadata is needed when using tools that accomplish dedicated subtasks of metabolic model reconstruction without being part of an existing platform . This would allow transparency throughout the reconstruction process , as discussed by Heavner and Price [17] . To circumvent these issues , we present the workspace AuReMe ( AUtomatic REconstruction of MEtabolic models ) . AuReMe is designed to house “à la carte” reconstructions and analysis of GSMs while ensuring that their metadata is properly stored and can be efficiently explored and distributed . In particular , it can complement reconstructions provided by external platforms , such as Pathway Tools for which the AuReMe import preserves the existing process metadata . The AuReMe workspace encompasses tools ( e . g . Cobrapy [18] , PSAMM [19] , OrthoMCL [20] , Inparanoid [21] , Pantograph [22] and its own internal tools: MeneTools , PADMet-utils ) useful for essential steps in GSM reconstruction ( import of annotation-based networks , template-based orthology predictions , gap-filling , manual curation ) . GSM analysis during the manual curation can be undergone with both flux-based and graph-based criteria . These tools can be connected through customized pipelines suited for diverse user needs , and offer different levels of flexibility in terms of supported input data , used tools and their interconnection as well as the possibility to perform manual intervention at different steps of the process . The customized pipeline can be safely run and reproduced thanks to log files which describe the exact chaining of tools used within the pipeline together with their parameters . Data management using the newly developed PADMet python package allows storing information about which method was used to include a reaction in the model ( process metadata ) together with classical information about reactions , compounds and pathways attributes ( the so-called biological metadata ) . All of them , along with the model itself , can be automatically integrated in a local wiki interface dedicated to monitoring and facilitating the reconstruction process . By structuring and linking data ( methods used in pipelines , reactions , compounds , pathways , genes , etc . ) , and by integrating semantic search functionalities , this view offers a user friendly solution to iteratively explore GSM produced with personalized pipelines and poorly interoperable tools . The generation is made locally to assist the user during the reconstruction . GSM updates are made through the use of assisted manual curation forms rather than by wiki edition for the sake of traceability . Once the model is fully reconstructed , it can be shared either with the SBML files , through online deployment of the generated wiki , or integrated in other platforms via several output formats provided . From a technical point of view , AuReMe can be viewed as a workflow controller allowing GSM ( possibly initiated with a major platform ) to be customized with “à la carte” pipelines of dedicated tools while keeping a record of the methods used . This ensures the reproducibility of the GSM customization procedure . The workflow controller , based on the Docker technology , is associated with a local data manager ( PADMet python package ) . It monitors and facilitates the ongoing reconstruction via a wiki , which is a view of the model linked metadata and is automatically generated with the MediaWiki technology . We illustrate the benefit of our approach on several case-studies . Among them , we show why the combination of heterogeneous information is absolutely necessary to elucidate the specificities of Tisochrysis lutea , a eukaryotic microalga currently used in oyster farming and studied in the context of bio-fuel applications . Its metabolic network was reconstructed by relying both on annotations and orthologies with four different template metabolic networks . This analysis strongly suggests that T . lutea has the same capability as Chlamydomonas reinhardtii to produce carnosine , a specific antioxidant dipeptide consisting of beta-alanine and L-histidine . Beta-alanine is produced through two distinct pathways , including one initiated by aspartate . On the contrary , only one of them was identified in the macroalga Ectocarpus siliculosus . Interestingly , the missing pathway producing beta-alanine from aspartate was identified in a symbiont of its algal wall: Candidatus Phaeomarinobacter ectocarpi [23] , paving the way to the study of organisms communities at the metabolic level .
We surveyed the reconstruction procedures of 19 published GSMs listed in Table 1 . These GSMs were selected because they cover main phylogenetic branches including eukaryotes ( ascomycete yeasts , green and brown algae , terrestrial plants and human ) , eubacteria ( cyanobacteria , proteobacteria , spirochaetes and firmicutes ) and euryarcheote archea . GSMs were selected to include highly studied organisms such as Saccharomyces cerevisiae and non-model species as well , such as Acidithiobacillus ferrooxidans . Thus , we avoid bias related to the level of information available for the reconstructed organisms or its phylogenetical cluster . We compared these GSMs in terms of metabolic model content , selected databases , available metadata and reconstruction processes . We first observed that most models display “biological” metadata , i . e . , metadata related to the model itself ( gene associations , external references , etc . ) and to its connection to other resources of knowledge ( template metabolic networks , protein or chemical databases , etc . ) . This information is currently provided in SBML files , the most widespread export format . Then we compared these GSMs in terms of selected databases and reconstruction processes . We observed that annotation , use of one or several ( up to 15 ) template models , gap-filling and manual curation are four widely shared steps , which are consistent with the general methodology described by Thiele and Palsson [6] . However , we also noticed that different tools and methods were used in the reconstruction , which confirms the hypothesis of “à la carte” reconstruction pipelines . In addition , we noted that several databases of reactions were usually used for reconstructing models ( presence of identifiers related to the main databases: KEGG , BiGG , BioCyc , Model SEED ) . In fact , one database and one method of reconstruction is rarely enough to obtain a model . For example , Bosi et al [24] , included information from all the aforementioned databases to reconstruct 64 models of Staphylococcus aureus . Notice however that use of multiple reaction database requires a consolidated curation procedure to avoid duplicate reactions [25] . Starting from these observations , we investigated further the possibility of tracing the origin of reactions . This is a main part of what we define as “process” metadata ( Supp . Fig A in S1 File ) , related to the reconstruction processes: steps at which reactions were added ( automatic reconstruction , gap-filling , manual curation ) , and the information sources they depend on ( annotation , orthology , etc . ) . These metadata make it possible to i ) locate and connect the studied model along with other models and knowledge resources and ii ) trace the reconstruction processes and ensure their reproducibility . Our study of the 19 GSMs highlighted that , when available , the process metadata of reactions were provided on multiple supports that were often neither machine-readable ( pdf files , Excel files , notes in SBML files ) nor suitable for further exploration . There was often no means to decipher at which step of the reconstruction and the reason why a particular reaction was added , making reproducibility of the model generation more difficult . In particular , manual curation was not always explicit . The only evidence that this process had been conducted was the presence of reactions with unreferenced identifiers , which do not match identifiers in the database ( s ) described as being used for the reconstruction process [26] . We concluded that missing metadata , particularly the process-related ones , is mainly attributed to the unstandardized and unrecorded passing through multiple tools used during model reconstruction . This causes the lack of traceability of reactions origin when studying the output models Thus , this survey advocates the need for tracking and storing metadata and for ways to explore and/or distribute these metadata along with the GSM . As described previously , current methods allow the reconstruction of high-quality GSMs but do not always take into account the need for metadata storage and exploitation to facilitate the study and reproducibility of models . We designed a unified workspace AuReMe ( AUtomated REconstruction of MEtabolic models ) to house the “à la carte” reconstruction of GSMs ( Fig 1 ) . This workflow controller , based on the Docker technology is the conductor handling the order of methods used in personalized pipelines . It is associated with a local data manager , the Python package PADMet ( Python library for hAndling metaData of METabolism ) , whose role is to store information related to the sequence of tools used in the pipeline . Finally , PADMet-utils encompasses several tools and methods to curate , analyze a GSM considering topological or flux modeling , generate wikis , produce reports and export the models . AuReMe gathers academic-free tools and enables the design of reconstruction pipelines that are flexible and can suit various available data sources ( genome annotation , template GSMs , protein sequences , etc . ) while storing metadata to ensure reproducibility of reconstructions ( Supp . Fig A in S1 File ) . It can follow four major steps of reconstruction processes: annotation or orthology-based modelings , gap-filling and manual curation . In addition , AuReMe supports most processes of the Thiele and Palsson protocol [6] by proposing tools and methods that facilitate analysis and storing of the results at each step related to experiments or exploration of literature . In particular , the refinements to reconstruction are strongly related to the management of metadata performed in AuReMe . Manual curation is assisted and formalized within forms to be filled before being integrated in the pipeline treatment . Additionally , analysis tools based on flux or topology are also included in the workspace . Contrary to existing platforms , AuReMe works with three major databases that are freely or academic-freely available: BiGG , ModelSEED and MetaCyc , for which some versions are already included . AuReMe also works with the KEGG database provided that the user has the appropriate licence . The use of those databases facilitates the open-data initiative , that our workspace wants to promote . At any time during the reconstruction process , the visualization of model data and associated metadata is available through the generation of a local wiki , that can be connected to the MetaCyc , the BiGG or theSEED database used for reconstruction . Each run of AuReMe requires to select a main reference database but reactions from other databases ( such as those predicted with orthology-based methods from models using alternative database identifiers ) can be inserted in the reference database after a mapping operation based on the MetaNetX dictionary [45] . The first feature of the AuReMe workspace is its adaptability to various input data and databases . The PADMet package format ensures the interoperability of knowledge , tools and data ( Fig 1 ) . A wide range of input data types and the three pre-set databases ( see S1 File for details ) enable the exploitation of all genomic and metabolic exploration within the workspace . The second feature of the AuReMe workspace is the customization of a pre-set pipeline . For example , the result of an annotation-based reconstruction can be imported into the workspace with the purpose of being merged with one or several orthology-based network ( s ) , or other pre-existing models . Gap-filling and topological or flux analysis can then be performed . All of these steps can be personalized through the pipeline creation ( see S1 File ) . The PADMet data manager stores all the necessary information about the methods used to add reactions to the final network . It also stores in a log file how tools are chained and parameterized in order to allow the automatic reproduction of the reconstruction process . Metabolic model version tracking can be done by using a network comparison command line which reports all the differences ( genes , reactions , compounds and pathways ) between several GSMs , including several versions of a model . The third feature of the AuReMe workspace is the possibility to reuse assisted and tracked manual curation in further versions of a GSM . These modifications to the model for including expert knowledge of biologists as well as ad-hoc literature are needed to enrich the quality of a model reconstruction . As done in KBase and Pathway Tools for instance , manual curation ( creation , modification and deletion of metabolites/reactions ) is assisted via the use of forms . All manual update operations are stored internally by the PADMet data-manager . This allows a user both to trace the reasons for adding the reactions and to automatically include ongoing manual curations in a future version of the model . The purpose is to ensure the consistency and sustainability of metadata , especially when the pipeline has to be run again , due to the availability of a better version of input data ( new genome assembly for instance ) , of updated version of databases or of new version of tools included in the customized pipelines . The last feature of the AuReMe workspace is to be opened and complementary to other platforms in order to facilitate further analyses . Connections can be made to use external analysis and reconstruction tools ( Fig 1 ) . Exports to SBML [46] ( |v|3 format by default , |v|2 if needed ) , including biological metadata and some process metadata , or stoichiometric matrix formats suit most tools that work with metabolic models . Exported models created within the workspace can then be used in Cobra Toolbox [47] , Raven toolbox [14] , Cytoscape [48] , etc . When a model is obtained by enriching an initial model produced with Pathway Tools ( respectively , Kbase ) , the final model can be imported back in Pathway Tools ( respectively , Kbase ) , in order to undergo further analysis and publication in BioCyc . For example , the E . siliculosus GSM presented in the results was initiated with a PGDB produced by Pathway Tools ( 1661 reactions ) , then enriched with 440 reactions using orthology , topological gap-filling and manual curation . The resulting model was imported in Pathway Tools in order to create a functional and manually curated PGDB ( both PGDBs are available in supplementary materials , the tutorial is available in S1 File ) . Finally , all the information contained in the model can be exported in a RDF database to be explored using recent computational techniques such as semantic query languages [49–52] . In addition to the easy connection to graph-visualization tools such as Cytoscape [48] , PADMet-utils proposes several solutions for exploring GSMs such as the creation of pathway-completeness text reports or graphic reports . As a main originality , a local wiki containing all the information related to the model , including its process metadata and links to external online databases ( See Supp . Fig A in S1 File ) can be created . For the sake of traceability , we favoured the use of assisted manual curation forms to perform GSM updates . Therefore , the wiki cannot be edited . Its main advantage is to provide a multi-page browsable exploration of thousands of heterogeneous entities related to a GSM together with a semantic search module . This convenient and user-friendly view based on linked data allows to concentrate and trace all the information of the GSM components and the reasons why they were introduced in the network , and also widens it to external information on the web . As depicted in Fig 2 , the user can browse the contents of the GSM starting either from reactions , genes , metabolites and pathways ( when available , which is the case for models relying on Metacyc database ) or from the methods used to create the network . In the E . siliculosus example , the GSM contained 1977 reactions in total: 1661 were recovered from genome annotations , 440 were deduced from orthology-based tools , 85 were added by gap-filling tools and 65 were manually corrected in order to fill biologically-relevant pathways which had a few missing reactions , according to the pathway completeness-rate , after annotation and orthology-based procedures . The wiki can be generated at any step of the pipeline . It is not meant to be edited but automatically re-generated at every step of the reconstruction , for the sake of curation traceability . When the ongoing wiki exploration leads to further insights on the metabolic network reconstruction , such as the need to manually curate the network or use gap-filling tools to complete particular pathways , or the need to include new models for orthology-based completion of the GSM , the user may either decide to integrate a new method in the pipeline or use the assisted manual curation forms to report the corrections . After an update of the model with AuReMe , the wiki generation procedure can be run again in order to produce an updated metabolic model which can be further explored with the updated wiki . The wiki exploration is particularly well suited to compare and distinguish the origin of reactions . This is useful to analyse the different components of a metabolic network . For instance , as discussed later , the computation and browsing of orthologs for four different species was a key feature to elucidate the specificities of T . lutea , for anti-oxidant production . Here we designed four different pipelines for the genome-scale metabolic reconstruction of a brown alga ( Ectocarpus siliculosus ) , a microalga ( Tisochrysis lutea ) , and two bacteria ( Sulfobacillus thermosulfidooxidans strain Cutipay , used in metal extraction processes ( biomining ) and Enterococcus faecalis ) . GSMs resulting from these pipelines possess the metadata associated with the reconstruction process; enabling the classification of every reaction according to the step that led to its addition in the model , as well as biological metadata . We surveyed all the reconstruction procedures of the four aforementioned organisms to measure the added value from each step of the reconstruction pipelines described in Fig 3 . At each step , we gathered information about the model ( Supp . Table A in S1 File ) . Main reconstruction steps for each organism included annotation and/or orthology , merging of models , gap-filling and/or manual curation . Templates for orthology were selected either i ) for being the best curated GSMs for organisms that are models in a taxonomic rank of the studied organism , and or ii ) for being well-curated models available for taxonomically close organisms . For instance , for S . thermosulfooxidans str . Cutipay , Clostridium ljungdahlii was chosen as a template because it is the phylogenetically closest microorganism in BIGG database . Although C . ljungdahlii is the closest microorganism , it is anaerobic . As S . thermosulfooxidans is aerobic , Bacillus subtilis was chosen as a representative for aerobic microorganisms and also because of its high-quality published model . S . thermosulfooxidans and B . subtilis both belong to the phylum Firmicutes . Finally , Acidothiobacillus ferrooxidans was also selected as a template model because it contains the best description of iron and sulfur metabolism , which are of interest regarding S . thermosulfooxidans . Biologically , it can be argued that horizontal transfer can be observed in this kind of microorganisms , so it makes sense to think they could share common reactions regarding iron and sulfur metabolism . E . siliculosus input data was annotation-based reconstruction from Pathway Tools and a template model for orthology-based reconstruction ( Arabidopsis thaliana , [42] ) . Final manual curation allowed us to account for expert knowledge and remove two useless reactions . 78 . 5% of the reactions were associated with gene product information . E . faecalis and S . thermosulfooxidans str . Cutipay GSMs were built only using orthology-based reconstruction from three different organisms’ template models ( Fig 3A , 3B and 3C ) . C . ljungdahlii iHN637 [34] , B . subtilis iYO844 [53] and C . ferrooxidans iMC507 [30] were used for S . thermosulfidooxidans . Their merging enabled the production of most targets but manual curation was needed to complete the model and simulate growth through Flux Balance Analysis ( FBA ) . 73 . 3% of the reactions were associated with gene product information . Escherichia coli str . K-12 substr . MG1655 [54] , Lactobacillus plantarum WCFS1 [35] and Bacillus subtilis subsp . subtilis str . 168 [53] were used as templates for E . faecalis . Half of the targets were producible after performing orthology , and manual curation enabled the completion of the model for growth simulation with FBA . Finally , T . lutea GSM was reconstructed with four template models: Arabidopsis thaliana , a land-plant model organism in system biology [42] , Synechocystis sp . PCC 6803 , a well-studied cyanobacteria [29] , Ectocarpus siliculosus , a brown macroalga model organism [41] and Chlamydomonas reinhardtii , a well-studied microalga [40] , annotation-based reconstruction from Pathway Tools and a manually created core-model ( Fig 3D ) . Gap-filling procedures were undergone both to restore the biomass producibility and to fill several pathways which were missing a few reactions according to their pathway-completeness rate . Manual curation was performed by selecting relevant reactions from a small-scale network of primary metabolism of T . lutea called primary network , enabling growth simulation through FBA . Special attention was paid to a carotene-related production pathway ( PWY-6475 ) , which was initially incomplete due to insufficient genome annotation and could later be filled by manual curation after assessing orthologue-based information , pathway completeness information and biological information provided by external links . This pipeline customization highlights that using all the available sources of data and combining them lowers the need for gap-filling and manual curation . The benefit of tracking process metadata during the combination of orthology , annotation and gap-filling is also noticeable at the pathway scale . Complementary methods that exploit all available data can retrieve several reactions from pathways , resulting in the reconstruction of complete or near exhaustive pathways . The wiki page associated with a given pathway describes all the methods of the pipeline providing ( multiple ) -evidences for the presence of each pathway reaction in the considered species GSMs and allows browsing databases to search for new possible evidences with other species . An example of such pathway completion can be observed during the reconstruction process of E . siliculosus ( Fig 4 ) . Having access to metadata allows the user to check the origin of every added reaction of a pathway . The 6-hydroxymethyl-dihydropterin diphosphate biosynthesis I ( PWY-6147 ) and the following tetrahydrofolate biosynthesis pathway ( PWY-6614 ) identified in algal metabolism includes in total eight reactions leading to the production of a necessary metabolite , a tetrahydrofolate ( THF-GLU-N ) starting from GTP . In this example ( Fig 4 ) , the need to combine approaches is illustrated by the functional characterization of the pathway after each step of the selected pipeline . Genome-annotation and orthology-based tools identified respectively 3 and 4 reactions of these pathways , including one that was idenfied by both . Combining both information is an essential step leading to a partial reconstruction of the pathways ( 7/8 reactions ) in the merged model . As mentioned on the MetaCyc website , the database groups reactions and metabolites into classes using an ontology tree structure . In our example , a 7 , 8 dihydrofolate ( DIHYDROFOLATE-GLU-N ) is a metabolite class comprising subclasses and instances . The 7 , 8-dihydrofolate monoglutamate ( DIHYDROFOLATE ) compound is one of these instances . Originally , reaction DIHYDROFOLATESYNTH-RXN produces DIHYDROFOLATE while the following reaction in the pathway consumes DIHYDROFOLATE-GLU-N . Performing gap-filling with an extended version of the MetaCyc database ( provided in the metabolic-reactions . sbml of the database ) allows to retrieve an instantiated version of the DIHYDROFOLATEREDUCT-RXN ( namely DIHYDROFOLATEREDUCT-RXN-THF/NADP//DIHYDROFOLATE/NADPH/PROTON . 37 . ) that takes DIHYDROFOLATE as a reactant . This enables to restore the producibility of the final compounds of the pathway starting from its inputs . A second reaction is added by gap-filling in PWY-6147 . Monitoring with the wiki and the various reports are helpful to keep track of this complex reconstruction process . Application of those heterogeneous methods allows the completion of the entire dihydrofolate biosynthesis pathway from GTP as described in the MetaCyc database . T . lutea is a microalga commonly referred to as T-Iso . Recently genomic and transcriptomic investigations were conducted to improve knowledge about this non-model species historically studied due to its use in aquaculture [55] . To obtain a comprehensive overview of this microalgal metabolism , the reconstruction process included metabolic models of the four previously described template organisms . The curation process included gap-filling based on an experimentally built core-network . The analysis of the final model confirmed that a functional T-Iso metabolic network had been obtained despite working with various template models and the related difficulties , especially regarding metabolite and reaction identifier mapping tasks which combined a systematic use of the MetaNetX dictionary [45] and manual curation ( Supp . Table A in S1 File ) . Data tracking , ensured by the PADMet library and format , allows biological experts to easily identify the origin of specific pathways , reactions and genes in the final metabolic network . Thus , it provides information about the complementarity among the various metabolic network drafts built using genome annotation and orthology-based reconstructions . Considering the 1164 enzymes with an EC-number reported in the network , 374 enzymes come from an annotation-based draft metabolic network only and 266 enzymes are originally associated with at least one of the 4 orthology-based draft metabolic networks ( Fig 5A ) . Contributions of all information are clearly significant . To address issues regarding integrated data origins , pathway completion and their interpretation in biological terms , the PADMet representation of the GSM was transposed into a local database in order to be investigated with semantic query languages . Indeed , considering only orthology-based draft networks , it is possible to continue investigations and to associate enzymatic reactions to their metabolic network origin . Fig 5B illustrates the query result: over the 790 enzymes associated with a reaction coming from orthology information , only 77 reactions are associated to the four metabolic network models . 388 reactions originate from E . siliculosus , reflecting reaction identifiers mapping was facilitated by the use of the same reference database ( MetaCyc ) . More generally , semantic-based queries and wiki were used to study the specificity of the T-Iso metabolic network . Indeed , the final T-Iso metabolic network led to the identification of a specific antioxidant metabolite: carnosine . In mammals , due to its antioxidant action , carnosine is an essential compound preventing brain neurodegeneration . Recent work about bioactive compounds identification in macroalgae [56] indicate the presence of carnosine in various seaweed species . Thus , this characterization in T-Iso metabolism is of interest for future biological experiments . In the T-Iso GSM , this antioxidant identification was made possible by identification of ortholog proteins between T-Iso and C . reinhardtii ( Fig 5C ) . Carnosine is a dipeptide consisting of beta-alanine and L-histidine . To complete carnosine production analysis , beta-alanine and L-histidine biosynthesis pathways were carefully examined ( Fig 5C ) . Two complete T-Iso beta-alanine biosynthesis pathways were characterized ( PWY-5155 and PWY-3981 ) . Indeed , T-Iso metabolism seems to include an aspartate decarboxylase as a first way to produce beta-alanine ( PWY-5155 ) . This enzyme was only identified by orthology ( Pantograph ) with Synechocystis sp . PCC , but based on reciprocal blasts , an ortholog could be existing in C . reinhardtii . The second beta-alanine biosynthesis pathway ( PWY-3981 ) includes two other enzymes , a diamine oxidase and an aminopropionaldehyde dehydrogenase . This pathway has been characterized in various photosynthetic organisms . So far , T-Iso L-histidine biosynthesis involves a single pathway ( HISTSYN-PWY ) composed of 10 reactions . L-histidine production pathway identification is confirmed for 8 out of 10 reactions , by genome annotations and/or Pantograph protein ortholog detection with our four template organisms . For the 2 other reactions , ATP-phosphoribosyl transferase ( EC:2 . 4 . 2 . 17 ) and imidazoleglycerolphosphate dehydratase ( EC:4 . 2 . 1 . 19 ) , putative ortholog proteins were identified a posteriori by a full ortholog protein screening ( reciprocal blasts ) . This analysis suggests that T-iso has the same capability as C . reinhardtii to produce carnosine , with two production pathways of the precursor beta-alanine . On the contrary , E . siliculosus should be able to produce beta-alanine with a single production pathway . Interestingly , the pathway involved in its synthesis from aspartate was identified in an obligatory symbiont of E . siliculosus algal wall: Candidatus Phaeomarinobacter ectocarpi [23] , whereas the PWY-3981 pathway was not evidenced in this brown alga to date [57] . Ortholog detection of carnosine synthase regarding our various template organisms , also led to the identification of a protein potentially associated to this function in E . siliculosus . This example illustrates how using several metabolic network models compensates for issues with reaction and metabolite identifiers mapping or missing gene-protein-reaction ( GPR ) information even in template GSMs . It also enables the integration of expert annotations ( i . e . T-Iso core manual network ) and information related to phylogenetically close organisms ( e . g . C . reinhardtii ) in order to understand better the specificities of a targeted organism ( T-iso ) with respect to other organisms .
Quality GSMs are true knowledge bases integrating both genomic and experimental data of studied organisms . They allow a global picture of an organism’s metabolism , predict growth phenotypes and guide their study . The added value inherent to a GSM is lost if its reconstruction cannot be reproduced or if the information it contains cannot be fully accessed . Unfortunately , this is the case for many of the GSMs that have been generated to date [58] . This problem arises in part because the reconstruction of genome-scale metabolic networks cannot be made without the use of many different tools and databases required to extract the most information possible from available data . Moreover , manual curation steps are also required to generate good quality models . Unfortunately , there is usually no standardized record or metadata related to these steps or of how and when different tools are used in the reconstruction process making it hard to track and reproduce . We introduce here AuReMe , a workspace dedicated to the generation of GSMs that offers solutions to the aforementioned concerns related to their transparency , traceability , reproducibility and exploration [17] . The main objective of AuReMe is to keep track of all metadata generated during the reconstruction of the GSM , either metadata linked to the model or its reconstruction process . Reconstruction can be done with high flexibility , both in terms of the database ( MetaCyc , BiGG , Model SEED ) and the tools/pipelines used for reconstruction . Special attention was given to potential manual curations performed in the network . Manual curations are usually the main metadata to be lost when sharing a model; thus , we simplified and traced the curations via the creation of forms to manually modify the model . AuReMe workspace relies on three levels . i ) The database representing the model is the core , as it gathers all data and metadata and is the cornerstone to every application of tool , analysis or modification to the model . ii ) The wiki for the visual exploration of the model is a new way to explore large-scale data at multiple levels . iii ) The ability to explore more deeply and query the model using RDF standards enables acute analyses to answer biological questions . Altogether , AuReMe offers solutions for GSM reconstruction made to suit user expectations in a world of data in which an emphasis is placed on sharing , tracing and exploration . The structure of metadata associated to GSM reconstruction can be viewed as a combination of the best practices of two GSM analysis platforms . First , the PADMet format can be viewed as an extension of the content of the data files produced by the Pathway Tools platform[13] . In addition to the information related to genome annotation , our approach enables a user to incorporate in the data format any additional information provided by orthology-based , functional gap-filling or curation steps . The traceability and flexibility of the reconstruction procedure is also close to the concept of narratives introduced in the Kbase platform . The AuReMe approach shows more flexibility in terms of references since a user can rely on any metabolic network knowledge repository , in contrast to the Kbase platform which exclusively relies on TheSeed database [10] . Similarly , the narratives of the Kbase platform enable the partial investigation of metabolic network attributes ( reactions , compounds ) through the availability of internet links to The Seed environment databases . In addition , some information about the methods used to assert the presence of a reaction in the network are provided in structured tables which are very close to the structure of the AuReMe wiki . The added values of the AuReMe technology are threefold . First , it enables the exploration of the full content of the metabolic network metadata either on a local computer or on a shared webserver . Second , the PADMet library makes it possible to enrich the wiki with any additional information introduced as an attribute to the PADMet format . In particular , when using the MetaCyc database , AuReMe enables the exploration of each pathway according to missing reactions , which facilitates the network curation . Thirdly , the capability of exporting the metadata information into a RDF triplestore is in the same line as the semantic analysis modules of the BioCyc database [8] . The main advantage is still increased flexibility since the triplestore can be designed according to user interests . For instance , the metabolic networks for several related species can be stored in the same triplestore in order to facilitate the comparison of their networks with ad-hoc SPARQL requests . The triplestore can also be enriched either with external linked open data ( e . g . KOG annotation for enzymes according to their EC numbers , from the KEGG database ) or with new data ( e . g . expression data in response to several stresses ) in order to identify the main pathways associated with particular biological phenotypes . The main weakness of the AuReMe workspace , though , is the command-line interface yielded by the Docker technology . Future improvements may include user-friendly workflow interfaces such as those used in Galaxy technology [15] or the Kbase platform . The main requirement of the AuReMe workspace is the need to select a reference database to ensure the compatibility of all the information introduced in the reconstruction process . This implies that used annotation based draft reconstructions as well as the metabolic networks of template organisms used for orthology-based reconstruction have to be preliminarily conciliated with the reference database . This means that all metabolite and reaction identifiers must be those from the reference database . Even though important progress has been made [45 , 59] , to date , the automatic simultaneous use of more than one database is an unresolved challenge and the correct consolidation of the information still requires significant manual effort [60] . This is because the translation of reaction and metabolite identifiers from one database to another is difficult . Metabolites in different databases are not always presented with the same name or even the same chemical formula or charge . Additionally , equivalent reactions in different databases do not always have the same stoichiometry . If this is not treated carefully , the result can be artefacts in the reconstructed metabolic networks that lead to unrealistic representations of the studied organisms . Moreover , since the universe of reactions and metabolites is large and not fully explored , available databases are not completely exhaustive and therefore many GSMs include a large number of manually annotated compounds and reactions . If they are to be exploited as templates in a reconstruction process , adapting them to fit the identifiers used in the reconstruction is unavoidable . Given this scenario , we believe that currently , the use of a previously selected and , if needed , adapted reference database throughout the reconstruction process is the best way to assure its traceability and reproducibility . Now that the era of the omics sciences is well advanced , the new needs for the reconstruction of metabolic models at the genomic level come from wild or unmodeled organisms , and even more so , the study of organisms in metagenomic samples [61] . It is interesting to note that recently metabolic models at the genomic scale for 773 members of the human intestinal microbiota were generated , showing the useful aspects of this type of reconstruction , but also shows that model reconstruction from large scale metagenomics samples can now be addressed [62] . In addition , whether in biotechnology or health sciences , there are more and more applications in which the use of wild organisms or communities of organisms becomes relevant . Examples range from the use of microorganisms consortia in biomining [63] , to address climate change [64] , to use organisms as cell factories to improve the production of certain metabolites , or to equip cells with the ability to produce new products [65] . AuReMe offers a real opportunity to manage projects where we will need to integrate a plethora of pre-constructed metabolic models , databases and bioinformatics tools to meet the new challenge of exploiting the metabolism of a metagenomic community .
We considered two extremophile bacteria , a eukaryote brown alga and a eukaryotic microalga . We deliberately selected species that are distantly related to common model organisms and that have not been studied in much detail . In particular , their genome annotations have not been given special attention and therefore they may contain genes of unknown function , which can generate uncertainties in the model reconstruction process . Ectocarpus siliculosus is a model brown alga whose GSM was previously reconstructed [41] but for which recent work provided a new annotation [66] . Enterococcus faecalis is a bacterium that plays a role in the dairy industry but is also of high interest in medicine as it may display multiple antibiotic resistances [33] . Sulfobacillus thermosulfidooxidans strain Cutipay [67] is a bacterium involved in bioleaching processes . Finally Tisochrysis lutea is a haptophyta microalga [55] of aquaculture interest in oyster farming and with a strong potential in the biotechnology field ( fatty-acids production ) . We used the concepts of seeds and targets to differentiate metabolites during the reconstruction steps . We call seed compounds to the set of metabolites that is available to initiate the metabolism , that is the growth medium . They can also be described as boundary compounds . Target compounds , on the other hand are metabolites whose production is supposed to be achieved by the metabolism of the species under study . They can be components of the biomass reactions or other metabolites that could have been identified as metabolic products in experimental studies . We developed PADMet , a Python library designed to manage metabolic networks in an attribute-based format . This format allows all the biological data to be stored relative to the metabolic network but also the metadata relative to the reconstruction workflow as shown in Supp . Fig A in S1 File . The latter also enables a user to analyse , explore and modify the metabolic network . The PADMet-utils is a suite of scripts based on the previous library to link admissible input data to the customized workflows and the various analysis tools available in the workspace . It contains four main modules for data management , connection to software , manual curation assistance and model exploration/analysis ( S1 File ) . Regarding the latter , the library proposes several tests to assess the quality of the reconstruction: Flux Balance Analysis , Flux Variability analysis ( analysis of essential and blocked reactions ) , percentage of reactions associated to a gene , determination of mass and/or charge unbalanced reactions using Cobrapy [18] . The connection module of PADMet-utils includes tools to generate RDF-triplestores from metabolic models for further SPARQL queries ( see S1 File ) . Additional details relative to the functionalities of PADMet-utils are available in S1 File . In order to analyse and curate the four networks studied in this paper , we relied on topological studies as a first step of analysis for draft models . By doing so , the GSM is considered a graph representing reaction and metabolite objects , in which the stoichiometry is not taken into account . To that end , we developed the MeneTools package ( MEtabolic NEtwork TOpological toOLS ) . This package is based on a recursive combinatorial scheme for producibility which was shown to be relevant for gap-filling [57] . The package enables the detection of unproduced target metabolites in the model ( menecheck ) ; the computation of the range of metabolites reachable from a given growth medium set of compounds ( menescope ) ; the computation of compounds that could unblock the producibility of targets when added to the model ( menecof ) and the identification of production paths from compounds of interest starting from a set of seeds ( menepath ) . It is available in the AuReMe workspace and as a standalone Python package . The tools solve combinatorial problems using Answer Set Programming . The AuReMe workspace embeds existing tools as well as ad-hoc packages developed to facilitate the interaction between tools ( PaDMet , Menetools ) . We used the Docker technology ( https://docker . com ) to encapsulate the AuReMe virtual environment as a container which can be run on any platform ( MacOS Yosemite or later , Windows 10 , Linux ) . The following software were installed in the AuReMe workspace: Blastall ( v2 . 2 . 17 ) [68] , CobraPy ( v0 . 5 . 11 ) [18] , Inparanoid ( v4 . 0 ) [21] , Meneco ( v1 . 5 . 0 ) [57] , OrthoMCL ( vmcl-02-063 ) [20] , Pantograph ( v0 . 2 ) [22] and PSAMM ( v0 . 28 ) [19] . In addition , the Docker image was developed to generate another Docker image which encapsulates MediaWiki technologies ( https://mediawiki . org ) in order to produce the representation of the metabolic model through local wiki webpages ( see S1 File for further details ) . For all reconstructed networks , The GSM reconstruction workflow was described in a configuration file , which handled the reconstruction process by running simple commands ( see details in S1 File ) . A local database is required to standardize the dataflow during the reconstruction process and to feed the gap-filling and curation steps . The databases MetaCyc 20 . 0 and BiGG 2 . 3 were used for the reconstruction of the metabolic networks . They are by default included in the current version of the AuReMe workspace , together with the Model SEED database . Notice , however , that the user can alter or import his/her own database from any SBML file . In order to make the workflow compliant with other pre-installed tools ( e . g . running FastGapfill from PSAMM instead of the topological Meneco gap-filling; or running OrthoMCL instead of Pantograph ) , a simple change in the configuration file is required . More generally , the workflow can be made compliant with any other tool by installing it in the Docker image and then adapting the configuration file to include a rule which ensures the connection between its inputs and outputs and the generic workflow .
|
Genome-scale metabolic models describe an organism’s metabolism . Building good models requires the integration of all relevant available information , obtained by exploring different data types and biological databases . This process is not straightforward and choices are made along the way , for example , which data is analyzed , with what tools . It matters that all reconstruction steps are well documented so that models can be fully exploited by the community . Having this metadata allows other researchers to reproduce , improve or reuse a model as a blueprint to create new ones . Sadly , this information is usually scattered and its proper distribution is the exception rather than the norm when using “à la carte” pipelines that combine main platforms and individual tools . We created a platform for “à la carte” metabolic model generation that responds to the need of transparency and data-connection in the field . It includes a battery of tools to exploit heterogeneous data through customizable pipelines . At each step , relevant information is stored , ensuring reproducibility and documentation of processes . Furthermore , exploration of models and metadata during the reconstruction process is facilitated through the automatic generation of local wikis . This view offers a user-friendly solution to iteratively explore genome-scale metabolic models produced with personalized pipelines and poorly interoperable tools . We highlight these benefits by building models for organisms with various input data . Among them , we show why the combination of heterogeneous information is necessary to elucidate specificities of Tisochrysis lutea , a eukaryotic microalga , for anti-oxidant production .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"metabolic",
"networks",
"enzymology",
"genomic",
"databases",
"metabolites",
"network",
"analysis",
"genome",
"analysis",
"enzyme",
"metabolism",
"plants",
"enzyme",
"chemistry",
"research",
"and",
"analysis",
"methods",
"computer",
"and",
"information",
"sciences",
"metabolic",
"pathways",
"biological",
"databases",
"algae",
"biochemistry",
"eukaryota",
"database",
"and",
"informatics",
"methods",
"genetics",
"biology",
"and",
"life",
"sciences",
"genomics",
"metabolism",
"computational",
"biology",
"organisms"
] |
2018
|
Traceability, reproducibility and wiki-exploration for “à-la-carte” reconstructions of genome-scale metabolic models
|
Chagas disease remains a serious medical and social problem in Latin America and is an emerging concern in nonendemic countries as a result of population movement , transfusion of infected blood or organs and congenital transmission . The current treatment of infected patients is unsatisfactory due to strain-specific drug resistance and the side effects of the current medications . For this reason , the discovery of safer and more effective chemotherapy is mandatory for the successful treatment and future eradication of Chagas disease . We investigated the effect of a ruthenium complex with benznidazole and nitric oxide ( RuBzNO2 ) against Trypanosoma cruzi both in vitro and in vivo . Our results demonstrated that RuBzNO2 was more effective than the same concentrations of benznidazole ( Bz ) in eliminating both the extracellular trypomastigote and the intracellular amastigote forms of the parasite , with no cytotoxic effect in mouse cells . In vivo treatment with the compound improved the survival of infected mice , inhibiting heart damage more efficiently than Bz alone . Accordingly , tissue inflammation and parasitism was significantly diminished after treatment with RuBzNO2 in a more effective manner than that with the same concentrations of Bz . The complexation of Bz with ruthenium and nitric oxide ( RuBzNO2 ) increases its effectiveness against T . cruzi and enables treatment with lower concentrations of the compound , which may reduce the side effects of Bz . Our findings provide a new potential candidate for the treatment of Chagas disease .
Chagas disease , caused by the protozoan Trypanosoma cruzi , is still a major public health problem in Latin America despite more than a century of research . Most infected individuals remain asymptomatic for several years . However , approximately 30% of these individuals progress to the cardiac or digestive forms of the disease , manifesting symptoms such as arrhythmias , heart insufficiency , megacolon and megaesophagus [1] . The disease affects more than 10 million people worldwide , and 100 million are at risk of infection . Although the control of natural transmission by insect vectors has been achieved in several countries , natural transmission persists in certain regions of Latin America [2] . Moreover , transmission of T . cruzi by other routes , e . g . , blood transfusion , organ transplantation and congenitally , have also been detected in nonendemic regions such as USA and Europe [3] , [4] , [5] . Despite the intense efforts to achieve an improved method of chemotherapy for the treatment of acute Chagas disease , nifurtimox and benznidazole ( Bz ) remain the only available drugs . These drugs have been used for more than 50 years . Treatment with Bz during the acute phase of the disease is able to cure approximately 60% of the patients . However , the cure rates do not exceed 20% when Bz is administered in the chronic phase , even in patients treated for more than 10 years [6] . Additionally , the treatment is lengthy and is relatively inefficient against certain strains of T . cruzi . Moreover , it has many side effects , from anorexia , headaches and fatigue to digestive intolerance , dermatitis , toxic hepatitis , depression of bone marrow and polyneuritis [7] . These problems encourage researchers to study new potential targets to develop better and definitive methods of chemotherapy . The trypanocidal effect of Bz has been reported to involve the reduction of a nitro group to a nitro radical anion that is able to covalently bind to several macromolecules of the parasite , such as its DNA , inhibiting their functions [8] . With the purpose of enhancing the hydrosolubility of Bz and hence increasing its activity and reducing toxicity , we previously described a ruthenium complex coordinated to Bz ( trans-[Ru ( Bz ) ( NH3 ) 4SO2] ( CF3SO3 ) 2 ) that exhibits a more active trypanocidal effect than Bz both in vitro and in vivo [9] . Additionally , we have previously shown the efficacy of ruthenium as a nitric oxide ( NO ) donor against T . cruzi . NO donors show high trypanocidal activity at very low concentrations , enhancing the survival of treated mice after T . cruzi infection [10] . In the present study , we tested the in vitro and in vivo effects of a ruthenium complex that combined the two compounds described above , Bz and NO . The representative molecular structure of cis-[Ru ( NO2 ) ( bpy ) 2 ( Bz ) ] ( PF6 ) , here denoted RuBzNO2 , is shown in Figure 1 . We found that low concentrations of RuBzNO2 had high trypanocidal activity in vitro against both trypomastigotes and amastigotes but did not show cytotoxicity in mouse cells . The compound was able to enhance the survival of treated mice after T . cruzi infection . The improvement provided by treatment was related to decreased heart damage , which was followed by reduced inflammation and parasitism in the myocardium . Importantly , the effects of RuBzNO2 were more favorable than those of the same concentrations of Bz .
The cis-[Ru ( NO2 ) ( bpy ) 2 ( NO ) ] ( PF6 ) 2 ( I ) complex was synthesized and characterized following published procedures [11] . The complex ( I ) ( 0 . 230 g , 0 . 29 mmol ) was dissolved in acetone ( 15 mL ) . NaN3 ( 0 , 02 g ) was then dissolved in methanol ( 5 mL ) and added dropwise to the above solution . After 10 min , Bz ( 0 . 260 g , 0 . 99 mmol ) previously dissolved in acetone was added to the solution and the reaction mixture was stirred at 50 . 0°C for 24 h . The resulting solution was concentrated by rotary evaporation until reduction to a third of initial volume and then NH4PF6 ( 0 . 300 g ) dissolved in water ( 1 . 0 mL ) was added . The cis-[Ru ( NO2 ) ( bpy2 ) ( Bz ) ] ( PF6 ) ( ( RuBzNO2 ) complex salt was obtained as a brown precipitate , which was collected by filtration and washed with ethanol ( 10 mL ) ( yield: 70% ) . Elemental analysis ( % ) for RuCHN Expt . ( Calc . ) C , 39 . 2 ( 38 . 8 ) , H , 3 . 1 ( 3 . 2 ) , N , 12 . 5 ( 12 . 3 ) . Trypomastigotes of Y strain of T . cruzi were obtained from infected LLC-MK2 cell culture and suspended at a concentration of 6 . 5×106 parasites/ml in RPMI 1640 medium ( Gibco-BRL LifeTechnologies , Grand Island , NY , USA ) containing 10% fetal bovine serum ( Life Technologies Inc . , Bethesda , MD , USA ) and antibiotics ( Sigma Chemical Co . , St . Louis , MO , USA ) and cultured in flat-bottom 96-well plates with various concentrations of benznidazole ( Bz ) or RuBzNO2 at 37°C for 24 h . The viability of the parasites was determined by counting the motile parasites in a Neubauer chamber as previously described [12] . The concentration of the compound corresponding to 50% of trypanocidal activity in trypomastigotes was expressed as the IC50 . To evaluate the trypanocidal activity of the compound in amastigote forms of T . cruzi , Vero cells ( ATCC ) were suspended in RPMI medium at 5×104 cells/well and were cultured in 8-well chamber slides and infected with trypomastigote forms of T . cruzi Y strain at 1×105/well for 24 h . The cells were washed to remove parasites in the supernatant and incubated with Bz or RuBzNO2 for an additional 24 h at 37°C . Slides were stained with Giemsa dye and evaluated by optical microscopy [13] . Trypanocidal activity was determined by counting the parasites/cell in at least 200 cells . Mouse spleen cells were isolated and incubated for 5 min with red blood cell lysis buffer ( one part of 0 . 17 M 700 Tris–HCl [pH 7 . 5] and nine parts of 0 . 16 M ammonium chloride ) . The cells were suspended in RPMI 1640 medium and cultured in flat-bottom 96-well plates at 5×105 cells/well with various concentrations of Bz or RuBzNO2 at 37°C . Tween 20 at 0 . 5% was used as a positive control for cell death . After 24 h of culture , cells were incubated with 10 µg/mL propidium iodide ( Sigma ) and acquired by flow cytometry using a FACSCantoII ( Becton-Dickinson Immunocytometry System Inc . , San Jose , CA , USA ) . Propidium-iodide positive cells were quantified using FlowJo software ( Ashland , Oregon , USA ) . The intracellular release of NO by RuBzNO2 was evaluated as previously described [12] . Briefly , Vero cells were cultured on slides at 1×106 cell/ml in RPMI medium for 24 h at 37°C . The NO fluorescent dye 4 , 5-diaminofluorescein diacetate ( DAF-2 DA ) ( 10 mM ) and the reducing agent phenylephrine ( 0 . 1 µM ) were added to the culture and incubated for 30 min at room temperature . In the cytoplasm , DAF-2 DA is hydrolyzed by cytosolic esterases to DAF-2 , which cannot leave the cell . In the presence of NO and oxygen , DAF-2 forms the fluorescent product DAF-2 triazole ( DAF-2T ) . Thus , RuBzNO2 was added to the culture , and the cytosolic NO concentration was assessed by fluorescence microscopy . Female Swiss mice ( 6–8 weeks old ) were intraperitoneally infected with 2000 bloodstream trypomastigotes of T . cruzi Y strain . The mice were treated orally from the first day of patent parasitemia ( day 5 of infection ) for 10 consecutive days with 4 µmol/kg or 0 . 4 µmol/kg of Bz ( 1 . 24 mg/kg and 0 . 124 mg/kg , respectively ) or RuBzNO2 ( 5 . 2 mg/kg and 0 . 52 mg/kg , respectively ) . The survival was monitored and parasitemia was evaluated in 5 µl of blood from the tail vein by counting parasites through a optical microscope [14] . All the procedures and animal protocols were conducted in accordance with the National Brazilian College of Animal Experimentation ( COBEA ) and approved by the Ethical Commission of Ethics in Animal Research of the University of São Paulo , Medical School of Ribeirão Preto ( CETEA ) - Protocol number 076/2010 . Histopathological analyses of the heart were performed by collecting tissues from mice on day 20 of infection . The tissues were fixed , dehydrated and embedded in paraffin . Slides were prepared with 5 µm sections and stained with hematoxylin and eosin ( H&E ) . Photomicrography at 400× magnification of 20 randomly chosen fields was performed with a microcamera ( Zeiss Co . , Oberkochen , Germany ) coupled with an Olympus BHS microscope ( Olympus , Miami , FL , USA ) . Inflammatory infiltrate was quantified using ImageJ software . Heart lesions were evaluated by quantifying serum creatine kinase MB ( CK-MB ) according to the manufacturer's instructions ( Labtest , Lagoa Santa , MG , Brazil ) . Tissue parasitism was evaluated by real-time PCR as previously described [15] . Briefly , DNA was extracted from tissue using a Wizard SV Genomic DNA Purification System ( Promega , Madison , WI , USA ) , and 1 µg DNA was incubated with 25 pmol of primers S35 ( 5′AAATAATGTACGGG ( T/G ) GAGATGCATGA3′ ) and S36 ( 5′GGGTTCGATTGGGGTTGGTGT3′ ) ( Sigma ) and GoTaq qPCR master Mix ( Promega ) according to the manufacturer's instructions . Samples were amplified for 40 cycles in a StepOnePlus ( Applied Biosystems , Foster City , CA , USA ) . An analysis of variance ( ANOVA ) followed by a Tukey-Kramer test was used to determine differences among the experimental groups . The results were evaluated at a statistical significance level of p<0 . 05 . A Kaplan-Meyer test was used to evaluate the survival rate .
To assess the trypanocidal activity of RuBzNO2 , trypomastigotes of T . cruzi Y strain , a strain partially resistant to Bz treatment , were incubated with serial dilutions of RuBzNO2 or Bz for 24 h , and live trypomastigotes were counted . The trypanocidal activity of RuBzNO2 was substantially higher and significantly greater than that of Bz , reaching an IC50 ( concentration able to kill 50% of the parasites ) of 7 . 28 µM , whereas the IC50 of Bz was 110 . 48 µM . In addition , at concentrations as low as 3 . 9 µM , RuBzNO2 still showed considerable activity against trypomastigotes ( Figure 2A ) . The toxicity of RuBzNO2 was specific to T . cruzi , as incubation with mouse spleen cells at the same concentrations revealed no cytotoxicity , although toxic effects were observed at higher concentrations , 250–500 µM ( Figure 2B ) . We next investigated whether RuBzNO2 is able to enter the cell and affect the amastigotes , the intracellular forms of T . cruzi . Treatment of Vero cells with RuBzNO2 increased the intracellular concentration of NO , as observed from the conversion of the NO fluorescent dye DAF-2 DA to the fluorescent product DAF-2T , which occurs in the presence of NO ( Figure 3A ) . In addition , Vero cells were infected with T . cruzi , and parasites were removed from the culture after 24 h of incubation . Cells were treated with RuBzNO2 for an additional 24 h . Through the quantification of the intracellular amastigotes , we found that RuBzNO2 was able to inhibit the replication or survival of the amastigotes more efficiently than Bz because RuBzNO2 at 50 µM decreased the percentage of infected cells to the same extent that Bz did at 200 µM ( Figure 3B ) . Overall , these data show that RuBzNO2 is able to enter the cell and is very efficient in eliminating extracellular and intracellular parasites . To assess the efficiency of RuBzNO2 in vivo , mice were infected with 2000 blood-derived Y strain trypomastigotes and orally treated with RuBzNO2 or Bz at the same concentrations for 10 days from the first day of patent parasitemia ( day 5 of infection ) . The doses used in the treatment were about 100 ( 4 µmol/Kg ) to 1000 ( 0 . 4 µmol/Kg ) times lower than the considered optimal dose of Bz ( 385 µmol/Kg ) [16] , [17] . Although neither Bz nor RuBzNO2 were able to change the peak of parasitemia , which occurred after 9 days of infection , RuBzNO2 significantly reduced the parasites in the blood before and after the peak ( 7 and 11 days post-infection ) more efficiently than the same concentrations of Bz ( Figure 4A ) . The treatment with RuBzNO2 at 4 µmol/Kg improved the survival of infected mice more efficiently than the treatment with Bz at the same concentration . Moreover , RuBzNO2 still had the same efficacy in the survival of the mice when used at a concentration that was 10 times lower ( 0 . 4 µmol/Kg ) , whereas Bz had no effect at this concentration ( Figure 4 B ) . An analysis of heart tissue performed at day 20 of infection showed that RuBzNO2 significantly reduced the inflammatory infiltrate in the tissue in a dose-dependent manner . RuBzNO2 was as efficient in reducing inflammation as Bz when used at a concentration 10 times lower ( Figure 5 A , B ) . Accordingly , the serum concentration of creatine kinase MB ( CK-MB ) , which indicates heart lesions , was decreased after treatment with RuBzNO2 more efficiently than the same concentration of Bz ( Figure 5C ) . These data strongly suggest that RuBzNO2 is able to improve the survival of mice after T . cruzi infection by decreasing tissue lesions . Tissue inflammation is induced by the presence of parasites in the tissue . We found that although the treatment with RuBzNO2 at these concentrations was not able to eliminate the parasites , it significantly reduced the parasitism of the heart , whereas treatment with Bz at the same concentrations had no effect ( Figure 6 ) . Both concentrations of RuBzNO2 had the same effect on tissue parasitism . These data indicate that low concentrations of RuBzNO2 are more efficient than Bz in decreasing the amount of parasites in the blood and , consequently , in the tissue . The lower level of heart parasitism and inflammation observed in the mice treated with RuBzNO2 may explain the increased survival of these animals .
The current chemotherapy available for the treatment of Chagas disease , Bz , has good effectiveness when administered in the acute phase of the disease; however , it has limitations , such as the side effects exhibited [18] . Accordingly , a way to enhance the effectiveness of Bz and to decrease its undesired effects is very desirable for generating potential new candidates for the treatment of a disease that has previously been incurable . With this aim , we previously synthesized a ruthenium complex with Bz and nitric oxide , RuBzNO2 . With this compound , we aimed to improve the action of Bz and to provide additional trypanocidal activity produced by NO because cis-[Ru ( NO2 ) ( bpy ) 2 ( Bz ) ] ( PF6 ) could be a NO donor agent due to high stability in physiological solution and the ability to catalyze the conversion of nitrite to nitrosyl . Substantial effort has been dedicated to the synthesis and evaluation of ruthenium complexes as antiparasitic agents . New series of ruthenium complexes have furnished improvements , as they show lower toxicity and higher hydrosolubility than the first metal derivatives synthesized [19] , [20] , [21] , [22] . In a previous study , we showed that the complexation of Bz with ruthenium improved its solubility and efficacy against T . cruzi both in vitro and in vivo when infected mice were treated for 15 consecutive days [9] . Ruthenium complexes used as a NO carrier also showed high trypanocidal activity at very low concentrations [10] , [23] . The mechanism of action of Bz involves the generation of free radicals , in particular , a nitro anion radical ( R-NO2− ) . Instead of stimulating redox cycling , these agents covalently bind to macromolecules of the parasite , modifying or inhibiting their functions [8] . In contrast , NO is a major mediator produced by infected cardiac myocytes and macrophages in response to IFN-γ and TNF-α , with pronounced trypanocidal activity via an oxidative stress-dependent mechanism [24] . In addition , NO is a negative regulator of chemokine production , decreasing the recruitment of inflammatory infiltrate that is partially responsible for the myocarditis induced by T . cruzi infection [10] , [12] , [25] . Thus , based on the possible synergistic action of two different compounds in one complex that show different mechanisms of action , we aimed to improve the effects occurring individually . RuBzNO2 was very effective in killing free trypomastigotes in culture . Moreover , as expected , it was more active than Bz because a lower concentration was necessary to kill 50% of the parasites ( lower IC50 ) . Notably , the toxicity of the compound was specific to the parasites because no toxicity was observed in mouse cells . RuBzNO2 showed an ability to enter the cell and release NO within the cytoplasm . This characteristic is very important for enhancing the effectiveness of the compound by enabling it to kill the intracellular amastigotes . In fact , RuBzNO2 was more effective than Bz in eliminating amastigote forms of T . cruzi , decreasing the frequency of infected cells . The results of the treatment of T . cruzi-infected mice with RuBzNO2 were very promising because low concentrations of the compound used in a short-term treatment were able to significantly improve the survival of the animals . Treatment with the same concentrations of Bz was not able to produce such improvement . This finding shows that the complexation of Bz with ruthenium and nitric oxide generated a compound with enhanced power against experimental Chagas disease compared to Bz alone . The treatment was effective when it was initiated after parasites were found in the blood . This treatment protocol , unlike the ones initiated when the animal is infected , is more likely to be administered in human patients with the acute form of the disease , whose symptoms appear after parasite proliferation and spread in the blood . The treatment with a concentration as low as 0 . 4 µmol/Kg of RuBzNO2 was able to decrease the parasite loads in the heart , whereas Bz did not affect parasitism of the heart when administered at the same concentration . The lower level of parasitism in RuBzNO2-treated mice was followed by reduced inflammatory cell infiltration and , consequently , reduced tissue lesions . Overall , these data suggest that Bz , when complexed with ruthenium and NO , may be used for treatment at lower concentrations to kill parasites and reduce cardiac lesions . Treatment at lower concentrations , or even for shorter times , with Bz may reduce the discontinuation of treatment by patients resulting from the side effects of the drug . Therefore , our findings provide a new candidate for the treatment of Chagas disease . Future studies are necessary to determine whether treatment with RuBzNO2 is effective in achieve the parasitological cure of infected mice . It is also worth to pay special attention to the treatment during the chronic phase of experimental Chagas disease and against other strains of T . cruzi . Moreover , additional adjustments to the dose and time of treatment may permit the treatment to achieve greater effects .
|
Chagas disease , caused by the parasite Trypanosoma cruzi , is a serious medical and social problem in Latin America and is also a worldwide concern due to widespread immigration . The current treatment with benznidazole is effective in the acute phase of the disease but has several limitations and many side effects . We showed that ruthenium complex with benznidazole and nitric oxide ( RuBzNO2 ) is very effective in killing the extracellular and intracellular forms of T . cruzi in vitro . In addition , low concentrations of this compound are able to substantially ameliorate the survival of infected mice by decreasing the amount of parasites in the heart and , consequently , reducing heart inflammation and lesions . Low concentrations of RuBzNO2 acted in a more effective manner than the same concentrations of benznidazole , indicating a synergistic effect due to NO and benznidazole . The substantial efficacy of treatment with benznidazole at a lower concentration than that used currently in clinical practice is very promising for avoiding the side effects that occur . Thus , our data provide a new potential candidate for the treatment of Chagas disease .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"chagas",
"disease",
"neglected",
"tropical",
"diseases",
"tropical",
"diseases",
"protozoan",
"infections",
"parasitic",
"diseases"
] |
2014
|
Ruthenium Complex with Benznidazole and Nitric Oxide as a New Candidate for the Treatment of Chagas Disease
|
Migration of cells within epithelial sheets is an important feature of embryogenesis and other biological processes . Previous work has demonstrated a role for inositol 1 , 4 , 5-trisphosphate ( IP3 ) -mediated calcium signalling in the rearrangement of epidermal cells ( also known as hypodermal cells ) during embryonic morphogenesis in Caenorhabditis elegans . However the mechanism by which IP3 production is stimulated is unknown . IP3 is produced by the action of phospholipase C ( PLC ) . We therefore surveyed the PLC family of C . elegans using RNAi and mutant strains , and found that depletion of PLC-1/PLC-ε produced substantial embryonic lethality . We used the epithelial cell marker ajm-1::gfp to follow the behaviour of epidermal cells and found that 96% of the arrested embryos have morphogenetic defects . These defects include defective ventral enclosure and aberrant dorsal intercalation . Using time-lapse confocal microscopy we show that the migration of the ventral epidermal cells , especially of the leading cells , is slower and often fails in plc-1 ( tm753 ) embryos . As a consequence plc-1 loss of function results in ruptured embryos with a Gex phenotype ( gut on exterior ) and lumpy larvae . Thus PLC-1 is involved in the regulation of morphogenesis . Genetic studies using gain- and loss-of-function alleles of itr-1 , the gene encoding the IP3 receptor in C . elegans , demonstrate that PLC-1 acts through ITR-1 . Using RNAi and double mutants to deplete the other PLCs in a plc-1 background , we show that PLC-3/PLC-γ and EGL-8/PLC-β can compensate for reduced PLC-1 activity . Our work places PLC-ε into a pathway controlling epidermal cell migration , thus establishing a novel role for PLC-ε .
Morphogenesis is a fundamental aspect of animal development , during which organs and tissues are formed . During morphogenesis a programmed series of migrations and fusions of epithelial sheets take place . These are finely coordinated by signalling pathways ( review by Bard [1] ) . The process of wound healing , after tissue damage , recapitulates many of the traits of epithelial morphogenesis , so that understanding morphogenesis is also relevant to this aspect of human health [2] , [3] . Dorsal closure in Drosophila [4] , and ventral enclosure in C . elegans [5]–[7] , are both paradigmatic models of morphogenesis . Due to its genetic tractability dorsal closure in Drosophila has become the best characterised example of epithelial morphogenesis and it is clear that many of its features are shared by other systems [8] . Ventral enclosure in C . elegans is another important model which is providing new insights into epithelial morphogenesis [5] , [7] . The epidermis ( also known as the hypodermis ) plays a key role during morphogenesis in Caenorhabditis elegans . Just after gastrulation the epidermis is organised in a layer of six rows of cells , located on the dorsal side of the embryo . The subsequent epidermal morphogenesis can be divided into three major events [5]–[7]: ( 1 ) dorsal intercalation , during which the two dorsal most rows of cells interdigitate to form a single row of cells; ( 2 ) ventral enclosure , during which the ventral-most rows of cells migrate to the ventral side of the embryo and establish junctional connections with symmetric opposing cells and ( 3 ) elongation , in which forces generated by circular filaments , located within the epidermal cells , drive the change from an ovoid to a worm-shaped larva . Neuroblasts and body wall muscle precursors provide the substrate for migration of epidermal cells . Many kinds of molecules are involved in the coordination of morphogenesis , including proteins of the cytoskeleton and adherens junction , and cell signalling molecules [7] . Among the later , IP3 signalling has recently been shown to play an important role in morphogenesis , by regulating the organisation of the actin cytoskeleton during epidermal cell migration [9] . IP3 signalling is a fundamental mechanism by which animal cells transduce extracellular signals into intracellular calcium signals , which in turn regulates a wide range of cellular responses . At the heart of this process is the IP3 receptor ( IP3R ) , a large channel protein located within the ER membrane , which modulates calcium release in reponse to IP3 production ( reviewed in [10]–[12] ) . Despite its importance in signal transduction little is known about either the functions of IP3 signalling during embryonic development or of the mechanisms regulating its production in developmental events ( reviewed by Whitaker [13] ) . The IP3 receptor is encoded in C . elegans by a single gene , itr-1 [14] . It has been shown that disruption of IP3 signalling in C . elegans compromises embryonic development [9] , [15] . For example , transient disruption of IP3 signalling , by means of an IP3 “sponge” , produces embryonic arrest [15] . Moreover , the cold sensitive mutant of the IP3 receptor , itr-1 ( jc5 ) , produces up to 95% dead embryos at 15°C , whilst the temperature-sensitive mutant of itr-1 ( sa73 ) produces around 20% embryonic lethality at 20°C ( a partially restrictive temperature ) [9] . Both mutants , jc5 and sa73 , produce arrested embryos due to defects during morphogenesis . Therefore IP3 signalling through ITR-1 is required during C . elegans embryonic development , and has a role in regulating morphogenesis . Despite the importance of IP3 signalling for appropriate progression of morphogenesis , little is known about the network of molecules that function in this pathway to regulate epidermal cell behaviour . IP3 is produced by hydrolysis of phosphatidylinositol 4 , 5-bisphosphate ( PIP2 ) catalysed by phospholipase C ( PLC ) . To date six isoforms of PLC have been described: PLC-β , PLC-γ , PLC-δ , PLC-ε , PLC-ζ , and PLC-η [16]–[19] . PLCs are modular proteins , which share common motifs but also contain family-specific regulatory domains , making them susceptible to different and complex modes of regulation . The recently discovered isoform , PLC-ε , is an exemplary case of complex regulation among PLCs . PLC-1/PLC-ε was isolated in C . elegans as a LET-60/Ras interacting molecule [17] . Mammalian PLC-ε proteins are both effectors and regulators of small GTPases of the Ras and Rho families [20] , and are thus able to play a pivotal role between small GTPase and IP3-mediated calcium signalling . In C . elegans there are five active PLC isozymes belonging to four of the six families: plc-2 and egl-8 ( PLC-β ) , plc-3 ( PLC-γ ) , plc-4 ( PLC-δ ) , and plc-1 ( PLC-ε ) [21] . Both PLC-1 and PLC-3 regulate ovulation [22] , [23] , and a number of other functions have been described for PLC-3 and EGL-8 [21] , [24] , [25] . Here we identify PLC-1/PLC-ε as a component of the network of molecules that regulates C . elegans morphogenesis . We show that PLC-1 is required for epidermal morphogenesis . PLC-1 depleted embryos have defects in ventral migration and also in dorsal intercalation . As a consequence , plc-1 loss of function results in ruptured embryos , with a Gex phenotype , and lumpy larvae . We show that two other PLCs , PLC-3/PLC-γ and EGL-8/PLC-β , can compensate for a lack of PLC-1 activity in morphogenesis . We demonstrate that PLC-1 acts through the IP3 receptor of C . elegans ( ITR-1 ) , a molecule known to be involved in the regulation of C . elegans morphogenesis . Therefore our results suggest that PLC-1 is a key molecule in a pathway which regulates the cytoskeleton during epidermal migration . Further , the properties of PLC-1 mean that it may be an integrator of IP3/Ca2+ and small GTPase signalling pathways .
Signalling through inositol 1 , 4 , 5-trisphosphate regulates development in C . elegans . Therefore , we hypothesised that ablation of PLC function should result in embryonic arrest due to decreased IP3 signalling . We tested the function of the five genes encoding active PLCs ( plc-1 , plc-2 , plc-3 , plc-4 and egl-8 ) using both RNAi , and mutant strains ( Figure 1 ) . Of the five PLCs , only reduction of PLC-1/PLC-ε function resulted in a clear and substantial increase in embryonic lethality . Thus , plc-1 activity is required for successful embryonic development . Depletion of egl-8 , plc-2 and plc-4 had no effect on embryonic survival ( Figure 1 ) . Complete depletion of plc-3 in null mutants , plc-3 ( tm1340 ) , or by RNAi produces sterility [24] , so embryogenesis is not readily observed in these worms . However , some of the plc-3 ( RNAi ) animals laid a small number of embryos before becoming sterile , 58% of which arrested ( N = 30 ) . plc-3 ( tm1340 ) /+ hermaphrodites do not have increased lethality ( Figure 1 , Table 1 ) , but this could result from maternal rescue . In contrast plc-3 ( tm1340 ) homozygous animals rescued with an unstable extrachromosomal array of the plc-3 gene , produced a small but significant increase in the number of dead embryos ( Table 1 ) . Such an array will be absent from a proportion of zygotes and is unlikely to be expressed in the germline ( a general property of C . elegans extrachromnosmal arrays ) . This suggests that PLC-3 may also be required during embryonic development . To test plc-1 function , we used RNAi and two alleles containing deletions: plc-1 ( tm738 ) and plc-1 ( tm753 ) . Ablation of plc-1 function results in severely reduced zygote production due to defects in ovulation [23] . plc-1 ( RNAi ) , plc-1 ( tm753 ) and plc-1 ( tm738 ) gave mean brood sizes of 5 . 8±0 . 9 , 19 . 0±2 . 9 and 40 . 4±2 . 7 ( ±SEM ) ( N = 110 , 154 and 364 ) respectively . Amongst the offspring produced , we observed substantial embryonic lethality ( Figure 1 , Table 1 ) . The embryonic lethality observed in plc-1 ( tm753 ) is rescued when the plc-1 gene is reintroduced into the plc-1 ( tm753 ) mutant ( Table 1 ) , thus the embryonic phenotype in these mutants is due to plc-1 deficiency . The brood size of plc-1 ( tm738 ) is similar to those reported for the putative null alleles plc-1 ( rx1 ) and plc-1 ( rx2 ) [23] . Molecular analysis suggests that plc-1 ( tm738 ) is a null allele ( Figure S1 ) . In contrast , both embryonic survival and brood size are less affected in plc-1 ( tm753 ) , suggesting that it may be a hypomorph . This is confirmed by molecular analysis ( Figure S1 ) . To test for a maternal requirement for plc-1 , we quantified embryonic lethality in the offspring of plc-1 ( tm738 ) /+ and plc-1 ( tm753 ) /+ heterozygotes . In both strains , the level of embryonic lethality was not significantly higher than that of wild type worms ( Table 1 ) . This suggests that , like itr-1 [9] , plc-1 has a maternal effect . We also showed that paternal plc-1 is not adequate to rescue embryonic lethality ( data not shown ) . Thus , disruption of plc-1 function results in substantial defects in embryogenesis . Ablation of plc-1 activity could result in disruption of a number of processes required for embryonic development . Perturbing IP3 signalling has been shown to result in defects in cell differentiation , gastrulation and morphogenesis , as well as low penetrance defects in early cytokinesis [9] , [15] . We therefore analysed the nature of the defect in plc-1 embryos in more detail . First we noted that , in addition to arrested embryos , we also observed arrested larvae with a lumpy appearance in both plc-1 mutants; tm738 and tm753 ( Figure 2 ) . Secondly , analysis of arrested embryos in plc-1 mutants revealed that 96% of dead embryos show clear signs of cell differentiation and organ formation ( see below ) . Thus , we did not observe any significant level of arrest prior to morphogenesis . We also observed that plc-1 mutants lay many round and misshapen embryos ( Figure 2A ) . However , this phenotype is not correlated with lethality , as only 64% and 36% of misshapen embryos , from plc-1 ( tm738 ) and plc-1 ( tm753 ) respectively , were arrested ( N = 36 and 28 respectively ) . The structure of the arrested embryos and presence of lumpy larvae suggested that plc-1 animals might have defects in morphogenesis . To define the nature of the defects in development in more detail , we used the epithelial cell marker ajm-1::gfp [26] to study both arrested ( i . e . terminally developed ) and developing plc-1 ( tm753 ) embryos . Early cell divisions were normal ( data not shown ) . All of the arrested embryos observed successfully completed gastrulation as judged by DIC microscopy ( N = 186 ) . In addition , the precursors of the gut cells showed characteristic auto-fluorescence granules ( data not shown ) indicating successful differentiation of intestinal cells , and most of the arrested embryos twitched vigorously , demonstrating that functional body wall muscle precursors were formed . In both arrested and developing embryos , AJM-1::GFP molecules accumulated in the apical junction domains of epidermal , intestinal and pharyngeal cells , suggesting that epithelial polarisation occurred normally in developing embryos ( Figure 3 ) . Although the embryos clearly show substantial cell differentiation , as discussed below , many embryos were highly disorganised . Next , we observed the structure and behaviour of the epidermal cells that occur during morphogenesis . plc-1 ( tm753 ) animals have defects in both dorsal intercalation and ventral enclosure , although the latter predominate . We observed individual embryos observed under the fluorescence microscope . Approximately 11 . 5% showed defects in dorsal intercalation ( see Figure 3A for an example ) . In other embryos , in which dorsal intercalation occurred successfully , ventral enclosure was disrupted ( Figures 3B and D ) . In more extreme cases , we observed that the epidermal cells failed to form a complete sheet exhibiting gaps along the ventral mid-line . We noted that the leading cells often failed to meet , although more posterior cells were able to contact their partners ( Figures 3D ) . The failures in ventral enclosure could reflect defects in cell migration or retraction of cells following failed junction formation . To address this we used time lapse confocal microscopy . We found that 35% of recorded embryos show defective epidermal migration ( N = 23 ) . We observed that in 50% of defective embryos the leading cells failed to migrate although more posterior cells successfully migrated and sealed ( Figure 4 and Movie S1 ) , whereas in the remaining embryos both leading and posterior cells failed to migrate . Migration of the ventral cells in plc-1 ( tm753 ) embryos was around 50% slower than wild type control animals even in those animals which successfully completed ventral enclosure . We also observed that cells often made both the proper contact with their opposing partner , and an ectopic contact with an additional opposing cell ( Figures 3B , 3D and 4 ) . Finally , we noted that those cells which failed to contact their partners prematurely accumulated AJM-1::GFP at the migrating edge ( Figure 3D ) . The defects we observed are very similar to those observed in itr-1 ( jc5 ) embryos [9] . Presumably , as a consequence of these defects , terminally developed plc-1 ( tm753 ) embryos show arrest at varying points following epidermal cell movements and present a highly disorganised morphology ( Figure 2 ) . 86% of arrested embryos have tissues that are normally internal ( gut , pharyngeal and other cells ) externally placed ( N = 186 ) . Figures 3E and 3F show developing embryos presenting extrusion of internal tissues , just before becoming highly disorganised . Of these embryos , many show epidermal rupture in the anterior part , in agreement with the observed failure of the leading cells to complete enclosure . The remaining 10% of arrested , but clearly differentiated , embryos elongated to some extent but showed aberrant morphology . Thus , the majority of embryonic lethality in plc-1 mutants is caused by defects in epidermal morphogenesis . The defects observed in plc-1 mutants are similar to the morphogenetic defects exhibited by itr-1 mutant embryos . itr-1 ( jc5 ) embryos show defects including misdirected migration and premature termination of migration of the epidermal cells . In the case of itr-1 mutants , epidermal cells have disorganised F-actin filaments and reduced filopodial protrusive activity , suggesting that ITR-1 and calcium may be regulating the cytoskeleton [9] . The epidermal defects in plc-1 mutants resemble those in itr-1 mutants , suggesting that PLC-1 may act through IP3 . To assess if the C . elegans IP3 receptor , ITR-1 , is a direct effector of PLC-1 , we tested the effect of combining plc-1 and itr-1 mutants . First , we tested the effect of an itr-1 gain-of-function allele , itr-1 ( sy290 ) , on the plc-1 ( tm753 ) allele . itr-1 ( sy290gf ) ; plc-1 ( tm753 ) double mutants have significantly decreased embryonic arrest , compared with the plc-1 ( tm753 ) control animals ( 8%±1 . 3 and 27 . 6%±3 . 4 ( ±SEM ) , respectively ) ( Figure 5A ) . This strongly suggests that PLC-1 signals through ITR-1 during C . elegans embryonic development . Attempts to make the plc-1; itr-1 loss-of-function homozygous double mutants , plc-1 ( tm753 ) ; itr-1 ( sa73ts ) and plc-1 ( tm753 ) ; itr-1 ( jc5cs ) were unsuccessful , suggesting that plc-1; itr-1 ( l . o . f ) double mutants are not viable . If PLC-1 signals upstream of ITR-1 during embryonic development , we hypothesised that increases in PLC-1 function , in an itr-1 ( jc5 ) background , should alleviate the mutant phenotype of the resulting embryos , at the restrictive temperature of 15°C . To test this hypothesis , we overexpressed plc-1 by making strains with extrachromosomal arrays carrying the plc-1 gene , jwEx302[plc-1 ( + ) ] in itr-1 ( jc5 ) . jwEx302[plc-1 ( + ) ] was able to partially rescue embryonic arrest of itr-1 ( jc5 ) mutants incubated at 15°C ( itr-1 ( jc5 ) ; jwEx302[plc-1 ( + ) ] 47 . 6%±3 vs itr-1 ( jc5 ) 77 . 9%±3 ( ±SEM ) ) ( Figure 5B ) . In addition , PLC-1 overexpression improved larval survival , as 35% ( N = 468 ) of surviving larvae developed into fertile adults , compared to only 12% ( N = 325 ) in itr-1 ( jc5 ) controls . PLC-1 may be acting within the epidermal cells or in the underlying neuroblasts to control ventral enclosure [7] . ITR-1 is expressed in the epidermal cells [9] . In situ hybridisation shows that plc-1 is widely expressed in embryos ( Y Kohara personal communication ) . To assess the potential site of action of plc-1 we expressed the plc-1 cDNA using an epidermal promoter , pelt-1 and a neuronal promoter , punc-119 . Expression of plc-1 from pelt-1 partially rescued the embryonic lethality of plc-1 ( tm753 ) animals ( 14 . 4±3 . 5% vs plc-1 ( tm753 ) 35 . 6±4 . 6% p<0 . 005 ) suggesting that plc-1 may be acting in the epidermal cells . We saw no change in lethality on expression of plc-1 from the unc-119 promoter ( 36 . 6±1 . 4 vs plc-1 ( tm753 ) 35 . 6±4 . 6% ) but this could be due to a number of reasons . These experiments strongly support the model in which PLC-1 operates through ITR-1 to signal during embryogenesis . Thus , we propose that PLC-1 is an important source of IP3 during epidermal cell migration and that this IP3 acts through ITR-1 to regulate calcium signals and the cytoskeleton , as proposed by Thomas-Virnig and co-workers [9] . We also tested whether signalling from PLC-1 by IP3 was used elsewhere in the animal . plc-1 and itr-1 are both required for ovulation [22] , [23] , [27] , [28] . However , previous attempts to test whether plc-1 acted through itr-1 in the spermatheca were inconclusive because only putative null alleles of plc-1 were available at the time [23] . We therefore measured brood size in the itr-1 ( sy290gf ) ; plc-1 ( tm753 ) animals . itr-1 ( sy290 ) is epistatic to plc-1 ( tm753 ) , increasing the average brood size in double mutants close to the level of the itr-1 ( sy290 ) animals ( brood sizes are 64 . 0±7 . 0 and 74 . 7±6 . 6 , respectively; p = 0 . 28 ) ( Table 2 ) , showing that ITR-1 works downstream of PLC-1 during both morphogenesis and strongly suggesting that this is also the case in ovulation . itr-1 ( jc5 ) has 80–95% ( [9] and Figure 5 ) embryonic arrest at its restrictive temperature . 30% of the embryos arrest due to defective morphogenesis ( 11% during ventral closure and 19% during elongation ) [9] . The remaining 65% largely arrest due to earlier defects . Thus , 86% of embryos which , reach the point at which epidermal morphogenesis begins , fail to progress as a result of defects in this process . In contrast , in plc-1 animals , we did not observe arrest prior to morphogenesis and the proportion of arrested embryos was 48% and 52% in the putative null plc-1 ( tm738 ) allele and in plc-1 ( RNAi ) animals , respectively . The penetrance of the defect in plc-1 mutants therefore appears lower than that reported for itr-1 ( jc5 ) , suggesting that other PLCs may be activating ITR-1 . We therefore tested whether ablation of any other PLC genes could enhance the effect of plc-1 deficiency . First , we performed RNAi against the PLCs in a plc-1 ( tm753 ) background . RNAi of egl-8 resulted in a substantial increase in the levels of arrest , from 33 to 56% ( Table 1 ) . Thus egl-8 may be acting to suppress the effects of plc-1 depletion . RNAi of plc-2 and plc-4 had no effect on the level of arrest . RNAi of plc-3 in plc-1 ( tm753 ) animals produced complete sterility . However , as noted previously , plc-3 ( tm1340 ) homozygotes rescued with extrachromosomal copies of the plc-3 gene do show some extra embryonic lethality ( Table 1 ) , suggesting that plc-3 could also have a role in embryonic development . To further analyse redundancy between the PLCs , we produced double mutants of plc-1 ( tm753 ) with loss-of-function alleles of egl-8 , plc-3 , plc-2 and plc-4 . Interestingly , double mutants of plc-1 with plc-3 ( in heterozygosis ) or egl-8 produced a significant increase in arrested embryos ( 52 . 3% and 62 . 9% respectively , compared to 32 . 8% of plc-1 ( tm753 ) alone ) ( Table 1 ) . We analysed the nature of the defects in the arrested embryos , from these double mutants , using DIC microscopy . We observed that 91 . 5% of plc-1 ( tm753 ) ; plc-3 ( tm1340 ) /+ and 90% of plc-1 ( tm753 ) ; egl-8 ( n488 ) arrested embryos arrest with defects in morphogenesis ( N = 129 and 120 respectively ) . We also produced a double homozygous mutant strain of plc-1 and plc-3 , rescued for fertility with an extrachromosomal array carrying plc-3 . These animals also showed increased lethality ( 43 . 5% ) compared to plc-1 alone ( Table 1 ) . Thus , reduction of plc-3 or egl-8 function enhances the phenotype of plc-1 , suggesting that PLC-3 and EGL-8 are able to function redundantly with PLC-1 . Analysis of animals carrying rescuing transgenes containing plc-3 fused to GFP revealed that plc-3 is expressed in a range of embryonic cells including the epidermal cells during morphogenesis ( data not shown ) . Thus plc-3 may contribute to successful morphogensis in normal circumstances although to dissect this would require either a plc-3 hypomorph , which is not currently available , or that the plc-3 ovulation defect is specifically rescued , allowing us to analyse embryos . egl-8 alone does not give rise to embryonic lethality , suggesting that either another PLC ( perhaps plc-1 ) is able to completely compensate for loss of egl-8 , or that egl-8 does not normally have a role in epidermal cell movements , but is able to compensate for loss of plc-1 in mutants . Compatible with the latter explanation we were unable to detect any expression of rescuing egl-8::gfp fusions during early or mid-stage embryogenesis . We also tested whether any of the other PLCs could act upstream of itr-1 and improve survival . We induced excess PLC function , in an itr-1 loss-of-function environment , by producing strains carrying extrachromosomal arrays of egl-8 ( jwEx306[egl-8 ( + ) ] ) , plc-3 ( jwEx311[plc-3 ( + ) ] ) and plc-4 ( jwEx320[plc-4 ( + ) ] ) , in itr-1 ( jc5 ) . jwEx306[egl-8 ( + ) ] gave a substantial and significant improvement in survival , whilst jwEx311 [plc-3 ( + ) ] gave a small reduction in lethality which was not statistically significant ( Figure 5 ) . These results are compatible with those obtained by testing the role of the other PLCs in plc-1 ( tm753 ) animals and again suggest that EGL-8/PLC-β is either involved in epidermal cell movements or is able to act in this pathway in certain artificial situations . We conclude that egl-8 and plc-3 may play a role in morphogenesis and/or may be able to augment plc-1 activity . No function in cell migration has , to our knowledge , been described for PLC-β . On the other hand , PLC-γ1 has been shown to play a role in epithelial cell migration in mammals where it interacts directly with villin [29] , [30] . If this relationship is conserved in C . elegans , this may provide a mechanism of action of PLC-3 in the epidermal cells during cell migration . We conclude that PLC-1 , the C . elegans orthologue of PLC-ε , has an important role in the epidermal cell movements that underlie morphogenesis in the C . elegans embryo . This is the first report of a role for PLC-ε in morphogenesis , although previous reports have suggested roles in the development of mouse heart valves [31] , and human glomeruli [32] . The results of our analyses demonstrate that the PLC-1 signal is transduced through ITR-1 . Previous work implicates itr-1 in the regulation of Ca2+ signals that in turn control cytoskeletal activity in the migrating epithelial cells [9] . The discovery of PLC-ε as a component of this process suggests a mechanism by which IP3 and calcium signalling may be linked to signalling through small GTPases of the Rho and Ras family , which are known to play important roles in these same events . Our results identify a new component of the molecular mechanism that controls epidermal cell movements . This mechanism may be important in cell movements in morphogenesis in other animals and in related processes such as wound healing .
Worms were cultured using standard techniques and media [33] . Strains used in this work and their origins are listed in Table S1 . The strain carrying the allele plc-4 ( jw1 ) is a complete deletion of the plc-4 gene , and was generated by gene targeting and homologous recombination ( unpublished results ) , following a modified version of a previously described method [34] . All strains were maintained at 20°C , unless otherwise stated . RNAi , by feeding of the PLC genes , was carried out using Escherichia coli HT115 carrying derivatives of the vector pPD129 . 36 [35] , containing ∼1 kb of the cDNA of each PLC gene [21] . As a control , we used a derivative of pPD129 . 36 with the chloramphenicol acetyl transferase ( cat ) gene from E . coli . Several L3 hermaphrodites were placed onto each feeding plate and incubated at 20°C for 24 hours and then transferred every 24 hours onto fresh separate feeding plates . Phenotypes were scored as described below . Total RNA was extracted from worms using a previously described method [14] . cDNA was produced using Superscript III ( Invitrogen ) , following the manufacturer's instructions . plc-1 cDNA was amplified using two rounds of PCR . The primers RV1157 ( CAG CAA ATA GCC TGG AGA GT ) and RV1159 ( AAC GAG CAC TGA GAA TGC CA ) were used in the first round . The second round PCR used RV1158 ( CAC AAT CTC GTG TGA TTC CA ) and RV1160 ( GGC GGA CCA GAT TGT GAC GA ) . Brood size was measured by placing L4 larvae onto individual plates , and then transferring them every 12–24 hours , at 20°C , for as long as the animals laid embryos . The number of progeny on each plate was counted ≥24 hours after removing the parent . We define brood size as the total number of embryos produced , regardless of whether these embryos hatch or not . To investigate embryonic lethality , we performed brood assays as stated above , and determined the number of arrested embryos and larvae . We used 10 to 12 parental animals per strain . We considered embryos as arrested when they failed to hatch within 24 hours after removal of the parent . In the case of strains containing the cold-sensitive allele itr-1 ( jc5 ) , when incubated at 15°C , embryos were allowed 36 hours in which to hatch . Experiments were performed at least three times and representative experiments are shown . Embryos were isolated either by dissecting from adults or by bleaching of worm populations . For routine examination embryos were mounted in embryo culture medium [36] . Embryos were then analysed as they developed or left for 18 hours at 20°C to develop for terminal phenotyping . DIC microscopy was performed using a Zeiss Axioskop 2 microscope ( Zeiss , Göttingen , Germany ) , equipped with a Q imaging , Micro Publisher 5 . 0 RTV digital camera ( Burnaby , ON , Canada ) . Fluorescence microscopy was performed using a Leica SP1 confocal microscope ( Wetzlar , Germany ) . Embryos for time lapse ( 4D ) confocal microscopy were mounted in embryo culture medium [36] . Time lapse microscopy was performed using a Leica SP5 confocal microscope , and 3D images were collected every 2–4 minutes . Standard molecular biology techniques were used to produce DNA constructs [37] . For PLC overexpression experiments , 10 ng/µl of the PLC-construct DNA was injected into worms , together with pRF4 ( a plasmid containing a dominant rol-6 marker ) , at a final total DNA concentration of 100–150 ng/µl , using previously described methods [38] . To make strains carrying arrays containing the plc-1 gene , e . g jwEx302[plc-1 ( + ) ] , the fosmid WR64BC06 was injected . WR64BC06 contains the whole plc-1 gene including the putative promoter , and was obtained from Geneservice ( Cambridge , UK ) . plc-3 arrays , e . g . jwEx311[plc-3 ( + ) ] , contain the plasmid pOB115 , which has the putative promoter ( 2 . 8 kb upstream of the ATG start site ) and plc-3 gene in frame with gfp . To obtain the plasmid pOB115 , we amplified the coding region of plc-3 ( 6186 bp ) using the following primers: OB1279 ( CGA TGG CGC GCC ATG CAA CAC GGC TCA CTT GG ) and OB1281 ( ATC GGC GGC CGC TGA TTT ACT ACT TTT TCC AAA TGA GAA C ) . This fragment was cloned in frame to YFP using the sites AscI and NotI , into the plasmid pSP002 ( S . Parker and H . Baylis , unpublished ) . Then , the putative promoter of plc-3 was amplified using OB1278 ( CGA TCT GCA GGC CAC GTG TCT CGA TAG AAT G ) and OB1280 ( ATC GGG CGC GCC GCT GAG AAA TTG AAG GAT TTA TGA AAT TGG ) and cloned before the coding region using PstI and AscI . plc-4 arrays , e . g . jwEx320[plc-4 ( + ) ] , include pRV011 which contains the complete plc-4 gene and its putative promoter ( a region spanning from 0 . 6 kb upstream of the ATG start site and 2 . 2 kb downstream of the stop codon ) . To obtain pRV011 we amplified the plc-4 loci ( 5853 bp ) using the primers RV760 ( GGA ACC GCG GCC AAT CAA TAC TTC CAT TGC C ) and HAB596 ( ATG CGG CCG CCC ATT TCT CGG TCA AAG TGA TTC C ) and cloned into pGEM-T ( Promega ) . jwEx306[egl-8 ( + ) ] contains the plasmid KP#440 ( A gift from S Nurrish ) , which consists of a minigene of egl-8 containing its promoter and first six exons fused to exons 7–20 from cDNA . To test the site of action of PLC-1 we used arrays expressing plc-1 from epidermal and neuronal promoters . jwEx325 contains pAN51 . pAN51 was constructed by placing the cDNA of plc-1 under the control of the elt-1 promoter . It also contains an “operonic” GFP downstream of the plc-1 cDNA which expresses GFP , but not fused to PLC-1 , from the same promoter . To generate this plasmid we used the Gateway technology ( Invitrogen ) . To obtain the elt-1 promoter , we used as primers AN2297 ( GGG GAC AAG TTT GTA CAA AAA AGC AGG CTG TAG ACG GTT GCC GTT TGA ATT TC ) and AN2298 ( GGG GAC AAC TTT GTA TAG AAA AGT TGG GTG ATC ATG GTC CTC GCC ACC GAC ) . The array jwEx333 contains pAN53 . pAN53 expresses the cDNA of plc-1 under the control of the unc-119 promoter , and was constructed in a similar way as pAN51 . To obtain the unc-119 promoter we used as a primers AN2142 ( GGG GAC AAG TTT GTA CAA AAA AGC AGG CTG TAA GCT TCA GTA AAA GAA GTA G ) and AN2277 ( GGG GAC AAC TTT GTA TAG AAA AGT TGG GTG ATA TAT GCT GTT GTA ) . Data are presented as means±SEM . Statistical significance was determined using Student's two-tailed t-test , using the online resources of Graphpad ( http://www . graphpad . com ) . P values are shown to indicate statistical significance .
|
Morphogenesis is a fundamental part of development which underlies the ability of animals , including humans , to define the shape of their tissues and organs and thus enable their proper function . To understand morphogenesis we need to understand the signalling networks that regulate coordinated changes in cell morphology , movement and adhesion . We know that in C . elegans intracellular signalling through the messenger inositol 1 , 4 , 5-trisphosphate ( IP3 ) is required for the proper completion of the morphogenetic processes . However the mechanism by which this signal is produced remains unclear . In this work we define the mechanism responsible for IP3 production in C . elegans . We use a combination of genetic and morphological analysis to show that phospholipase C-epsilon ( PLC-ε ) is the molecule responsible for IP3 production . In worms with disrupted PLC-ε the embryonic epidermal cells fail to migrate properly so that morphogenesis fails . PLC-ε was only discovered relatively recently and interacts directly with a wide range of signalling pathways , including others that are known to regulate important cellular properties during morphogenesis such as small GTPases . Therefore we establish a potential link between IP3 signalling and other pathways that are known to be involved in cell movements . This is an important advance in defining the network of interactions that regulate epithelial cell movements in morphogenesis .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology/morphogenesis",
"and",
"cell",
"biology",
"cell",
"biology/cell",
"signaling"
] |
2008
|
Phospholipase C-ε Regulates Epidermal Morphogenesis in Caenorhabditis elegans
|
Sclerotinia sclerotiorum is a necrotrophic ascomycete fungus with an extremely broad host range . This pathogen produces the non-specific phytotoxin and key pathogenicity factor , oxalic acid ( OA ) . Our recent work indicated that this fungus and more specifically OA , can induce apoptotic-like programmed cell death ( PCD ) in plant hosts , this induction of PCD and disease requires generation of reactive oxygen species ( ROS ) in the host , a process triggered by fungal secreted OA . Conversely , during the initial stages of infection , OA also dampens the plant oxidative burst , an early host response generally associated with plant defense . This scenario presents a challenge regarding the mechanistic details of OA function; as OA both suppresses and induces host ROS during the compatible interaction . In the present study we generated transgenic plants expressing a redox-regulated GFP reporter . Results show that initially , Sclerotinia ( via OA ) generates a reducing environment in host cells that suppress host defense responses including the oxidative burst and callose deposition , akin to compatible biotrophic pathogens . Once infection is established however , this necrotroph induces the generation of plant ROS leading to PCD of host tissue , the result of which is of direct benefit to the pathogen . In contrast , a non-pathogenic OA-deficient mutant failed to alter host redox status . The mutant produced hypersensitive response-like features following host inoculation , including ROS induction , callose formation , restricted growth and cell death . These results indicate active recognition of the mutant and further point to suppression of defenses by the wild type necrotrophic fungus . Chemical reduction of host cells with dithiothreitol ( DTT ) or potassium oxalate ( KOA ) restored the ability of this mutant to cause disease . Thus , Sclerotinia uses a novel strategy involving regulation of host redox status to establish infection . These results address a long-standing issue involving the ability of OA to both inhibit and promote ROS to achieve pathogenic success .
Sclerotinia sclerotiorum is a devastating and economically important necrotrophic fungal pathogen capable of infecting more than 400 species of dicotyledonous plants worldwide [1] , [2] causing annual crop losses exceeding $200 million in the United States alone [2] . Diseases caused by S . sclerotiorum are responsible for considerable damage , have proven difficult to control ( culturally or chemically ) , and host genetic resistance to this fungus has been inadequate ( http://www . ars . usda . gov/Research/docs . htm ? docid=20320&page=1 ) . Necrotrophic plant pathogens require dead host tissue in order to obtain nourishment . Traditionally , the resulting disease symptoms have been attributed to direct killing of host tissue via secretion of toxic metabolites by the pathogen . Recently however , emerging data from several pathosystems have suggested that necrotrophic fungi are tactically more subtle in the manner by which pathogenic success is achieved , though the mechanistic details are not known . Consistent with other necrotrophs , S . sclerotiorum produces a wide array of degradative lytic enzymes ( e . g . endo , exo-pectinase , cellulase , hemicellulase , protease ) , which are believed to facilitate colonization and host cell wall degradation [3] , [4] . We have been investigating the role of fungal secreted oxalic acid ( OA ) in pathogenicity of S . sclerotiorum [5]–[9] . OA ( dicarboxylic acid ) is remarkably multifunctional and contributes to numerous physiological processes ( e . g . reduction in pH , acidity-induced activation of enzymes , elevation of Ca2+ , guard cell regulation , vascular plugging with oxalate crystals ) that augment fungal colonization of host plants ( reviewed in [10] ) . Additionally , studies with OA-deficient mutants strongly suggest that OA is an essential pathogenicity determinant and a key factor governing the broad pathogenic success of this fungus [2] , [5] , [11] . We have shown that OA is a fungal elicitor that induces cell death in host plant tissue resulting in hallmark apoptotic-like features including cell shrinkage , DNA laddering , and TUNEL reactive cells in a time and dose dependent manner . Oxalic acid also aids Sclerotinia pathogenicity indirectly acting as a signaling molecule , via manipulation of host ROS [12] . Reactive oxygen species have long been considered detrimental to cells since they can be toxic; causing damage to proteins , lipids ( membranes ) and nucleic acids . Recent data however suggests a more subtle and versatile role for these small molecules . When present at low levels , ROS may actually be beneficial , serving as secondary messengers in intra and inter-cellular signaling pathways . Regulation of redox homeostasis is now an active area of research , particularly within pathogen/host interactions ( e . g . hypersensitive response and the oxidative burst ) and adaptation to abiotic stress ( e . g . drought , salt ) , all of which have strong correlations with ROS signaling . One of the earliest and most universal resistance responses mounted by plant tissues against an invading microbe is the oxidative burst , a controlled release of O2− and H2O2 at the point of pathogen challenge . Once triggered , the oxidative burst is believed to be required for pathogen defense and is expressed in almost all plant species [13] . Additionally , the oxidative burst also occurs during compatible interactions , but the timing and magnitude differ . Previous studies have shown that the oxidative burst can be suppressed at low pH [14] . As such , the release of oxalate could enhance fungal pathogenicity by acidifying host cells and dampening the oxidative burst . In this paper , we provide real-time evidence that this potent necrotroph modulates host-PCD pathways through secretion of OA , by a mechanism independent of acidification . Surprisingly , this process is initiated by a reducing environment generated by the fungus in host cells . As a direct consequence of this redox manipulation , the fungus subverts host defense responses , inhibits the oxidative burst , and prepares the infection court for the establishment of disease . Moreover , OA− ( non-pathogenic ) mutants are unable to suppress plant defense resulting in active recognition of the fungus by the plant that is accompanied by delimited cell death and callose formation suggestive of an HR-like response .
Previously we showed that OA , secreted by Sclerotinia , is a pathogenicity determinant and elicitor of plant programmed cell death [12] . Consistent with these observations , we noted an intriguing difference between the disease phenotypes of wild type and OA-deficient A2 Sclerotinia infected plants ( Figure 1 ) . In contrast to wild type Sclerotinia , which causes overwhelming disease and runaway cell death . Challenge with the non-pathogenic OA-deficient A2 mutant resulted in restricted growth , reminiscent of an HR-like response ( Figure 1A , 1B ) . To determine whether this response had features consistent with the HR , we examined several markers associated with the HR . Plant defense responses involving HR are typified by the oxidative burst , a universal and early response by the plant upon recognition of pathogens . Plants were challenged with the wild type and A2 mutant; 8 hrs post-inoculation leaves were stained with the ROS ( hydrogen peroxide ) indicator stain , 3 , 3′-diaminobenzidine ( DAB ) . Interestingly , ROS was virtually absent in DAB stained leaf tissue challenged with wild type Sclerotinia , even though disease progression was observed ( Figure 1C ) . However , leaves inoculated with the A2 mutant displayed strong DAB staining surrounding the infection point ( Figure 1D ) . Plants protect themselves using both physical and chemical defenses . Callose is an effective barrier induced at the site of attack during the early stages of pathogen invasion and is an established marker associated with incompatible ( HR ) responses . Aniline blue staining was used to reveal callose structures in leaf tissue following inoculation with wild type and the A2 mutant . Similarly to DAB , callose deposition was observed following inoculation with the A2 mutant but not wild type ( Figure 1E , 1F ) . Thus markers associated with plant defense were observed specifically following mutant , but not wild type inoculations; including the oxidative burst and callose deposition . It is of interest to determine whether the phenotype induced in response to the A2 mutant mechanistically resembles an HR , as there are no known proteinaceous effectors or corresponding resistance ( R ) proteins in this system . Such results would suggest active recognition of the A2 strain by the host , followed by an effective host defense response which does not occur in response to challenge with the wild type fungus . In contrast , the A2 mutant could be just physiologically compromised in pathogenicity and thus incapable of causing disease independent of plant involvement . Furthermore , these findings also suggest that the wild type fungal strain suppresses this recognition process . Existing models describing the mechanisms of recognition and response to pathogens have been , for the most part , centered on plant interactions with biotrophs . Following these observations , we theorized that Sclerotinia may alter the redox environment in the host to avoid detection . To more accurately determine whether wild type Sclerotinia suppresses the oxidative burst by modulation of the host redox environment , we used a real-time plant-based redox sensitive GFP reporter , ro-GFP [15] . In this system , the GFP chromophore has been altered such that the excitation wavelength is influenced by the oxidation state of the environment . The GFP is constitutively expressed; however , the excitation wavelength ( 410 versus 470 nm ) is reliant upon the redox potential of the environment . During reducing conditions the excitation occurs at 470 nm while under oxidizing conditions excitation occurs at 410 nm and thus these environments can be differentiated using appropriate filters . Transgenic Nicotiana benthamiana lines were generated containing a 35S driven redox sensitive GFP cassette . Initial confocal microscopy analysis supported GFP excitation at 410 nm filter but failed to detect GFP excitation and fluorescence at 474 nm ( Figure 2A and 2B ) . This suggests that in the default state reducing conditions are not detectable . To determine whether GFP fluorescence can be observed under reducing conditions , we infiltrated leaves with the established reducing agent , DTT . DTT created a reducing environment within the cell and GFP fluorescence was successfully observed with the 474 nm excitation filter ( Figure 3D ) . Since GFP fluorescence was not observed in the ro-GFP plants under the 474 nm filter ( excited only under reducing conditions ) , this filter was used to evaluate the reductive state of host cells following challenge with wild type Sclerotinia . The two strongest ro-GFP expressing lines ( 2 and 6 ) from the initial confocal microscopy analysis were chosen and challenged with the wild type and the OA-deficient A2 mutant strains . As expected , and consistent with our previous confocal analysis , GFP expression was not observed under the 474 nm filter prior to infection; leaves challenged with the wild type strain however revealed regions of strong fluorescence at this wavelength ( Figure 2C ) . Notably , the fluorescent region was distinct and located in advance of fungal growth before becoming rapidly diffuse . Unlike its wild type counterpart , but consistent with the disease phenotype and ROS staining results , the A2 mutant failed to induce GFP fluorescence ( Figure 2D ) . These results suggest that the wild type strain , but not the OA-deficient A2 mutant , is able to promote a reducing environment within the host during the initial stages of infection , possibly through the secretion of OA . The necrotrophic fungal pathogen Botrytis cinerea was also examined . B . cinerea is closely related to Sclerotinia but produces lower quantities of OA; and as shown here , reduced flouerscence/reduction ( Figure 2H ) . The role of OA in B . cinerea pathogenesis has been examined by several groups however , details are not clear . To further study the relationship between cellular reduction and oxalic acid production , we inoculated two NADPH oxidase ( Ssnox1 , Ssnox2 ) and a superoxide dismutase ( Sssod1 ) Sclerotinia mutant strains generated in our lab . These mutants are altered in redox capabilities as indicated by the observation that lower levels of OA are produced compared to wild type ( Figure S1 and S2 ) . These strains generated reducing conditions in the host to levels that were intermediate between the wild type strain and the OA-deficient A2 mutant ( Figure 2E–2H ) . The ability to reduce the host environment was related to the level of OA secreted by all of these strains . Therefore , the secretion of OA , generation of reducing conditions , and intra-/inter-cellular ROS signaling between pathogen and host appear to be integral determinants for Sclerotinia pathogenicity . To assess whether OA can modulate the host redox environment directly , we monitored the cellular redox state in OA infiltrated leaves . The acidification of cells is known to dampen the oxidative burst [14] , therefore to examine acidification as a factor we used the OA salt , KOA , buffered to pH 3 and pH 7 . We included 10 mM HCl as an additional control . Nicotiana benthamiana ro-GFP leaves from lines 2 and 6 were infiltrated with 10 mM KOA ( pH 3 and 7 ) and 10 mM HCl , ( 10–20 mM OA is commonly found in diseased plant tissue [7] ) . Consistent with previous reports showing OA induced host PCD was independent of acidification [12] , infiltration of KOA at pH 7 , but not pH 3 , was able to reduce the host cellular environment and support GFP fluorescence under the 474 nm excitation filter ( Figure 3A and 3B ) . Further confirmation that OA induced reducing conditions are independent of its ability to acidify the host cellular environment was supported by HCl treatment , which also failed to induce GFP fluorescence ( Figure 3C ) . These results strongly correlate with our previous studies [12] that show induction of ROS and PCD occur independent of the acidification ability of OA and demonstrate that OA alone is sufficient for mediating reducing conditions in plant cells . Taken together , we suggest that OA suppresses the oxidative burst , at least in part via the generation of reducing conditions and subsequently triggers ROS induced DNA fragmentation and PCD at neutral ( pH 6–7 ) but not acidic ( pH 3 ) conditions . Our results provide further evidence supporting the importance of OA as a key Sclerotinia pathogenicity factor . If the induction of a reduced state in the cell via secretion of OA is a necessary and sufficient component of Sclerotinia pathogenicity , we reasoned that pathogenicity of the OA-deficient ( A2 ) mutant could be enhanced via the induction of an “artificial” reducing environment . Exogenous application of DTT ( and KOA ) enhanced disease development of the OA-deficient A2 mutant ( Figure 4 ) . Trypan blue staining of fungal tissue verified that the A2 mutant can now grow within the DTT and KOA infiltrated areas ( Figure 4 ) . Thus , these observations are consistent with the premise that Sclerotinia induces disease by initially triggering reducing conditions in the cell during the early stages of infection . Furthermore , these data also show that the ability of the A2 mutant to cause disease is restored under these conditions . The non-pathogenic phenotype associated with OA deficiency is due , at least in part , by the inability to create a reducing environment in the host . Taken together , these data suggest that the host redox environment , specifically cellular reduction , is an integral pathogenicity component of Sclerotinia and may contribute to the broad host range displayed by this necrotrophic fungus . A conundrum of the Sclerotinia/OA system as noted by our previous experiments , is that OA suppresses the generation of host plant ROS [5] , but is also capable of inducing plant ROS during disease development culminating in PCD [12] , both of which are necessary for pathogenesis . Based on the available evidence , we hypothesize that OA triggers a rapid , but transient reduced state in the cell that is temporally followed by oxidation leading to host cell death and disease . To investigate this possibility , we infiltrated leaves from intact plants with water or KOA buffered to pH 7 , and examined GFP fluorescing regions over a 12 hour time course , sampled every three hours . Additionally , we stained the same leaves for superoxide production with nitro-blue tetrazolium ( NBT ) . Consistent with our earlier results , infiltration of KOA at pH 7 induced reducing conditions and supported GFP expression in leaf cells three hours post-infiltration ( Figure 5 ) . OA-mediated reduction and excitation of ro-GFP ( 474 nm ) was also observed six hours post-infiltration but at lower levels than those observed three hours-post infiltration ( Figure 5 ) . GFP excitation was not observed in any of the water infiltrated controls or with KOA 12 hours post-infiltration . The observation of reducing conditions as early as three hours post-infiltration of OA suggests that Sclerotinia rapidly induces strong reducing conditions during the initial stages of infection . This correlates with the dampening of the host oxidative burst , and provides further evidence that Sclerotinia “prepares” host cells for infection via the induction of a reducing environment . In contrast to GFP expression , NBT staining detected low levels of superoxide at 0 hours post-infiltration , however , this also occurred for the water control and is more likely a result of injury during the infiltration process rather than elicitation of oxidizing conditions . In comparison to the water controls , ROS levels in the KOA infiltrated samples were reduced 3 hours post-infiltration . These results are in agreement with the ro-GFP time-course that demonstrated reducing conditions in leaf cells as early as three hours post-infiltration ( Figure 5 ) . There were slight differences in the ROS levels between the water controls and KOA samples 6 hours post-inoculation . However , 12 hours post-infiltration , there was a consistent and reproducible increase in NBT staining for the KOA infiltrated sample in comparison to the water control . These observations are in accordance with our previous studies that showed strong DAB staining in leaves 24 hours post OA treatment [12] . The NBT-staining results suggest that following the initial OA induced reducing environment , oxidizing conditions prevail in the cell . We have previously shown that direct OA treatment of plant tissue induces ROS and plant cell death; both of which can be inhibited chemically [12] . Thus , OA secreted by Sclerotinia appears to have dual opposing functions . During the early stages of infection , reducing conditions are induced that may suppress the oxidative burst , host defenses , and possibly other host processes . Once infection is established however , oxidizing conditions are generated in response to the fungus and cell death occurs . In this manner Sclerotinia uses OA as a signaling molecule to control the direction of the host redox environment , plant defense responses , and cell death pathways .
Traditionally , necrotrophs were thought to directly kill host tissue via the secretion of toxins and degradative enzymes . Recent studies with Cochliobolus , Botrytis and Sclerotinia however , suggest that the infection process may be more subtle than originally believed and that certain necrotrophs do not kill the cell directly but instead commandeer plant PCD pathways for their own benefit . Although the precise mechanism by which these pathogens control host PCD is unknown , emerging evidence from Sclerotinia suggests that ROS plays a significant role . As an intermediary of PCD responses , low concentrations of ROS function as signaling molecules during pathogen development [16] and during pathogen-host interactions [17] . In this study we used a plant-based redox sensing GFP expression system , histological staining and reverse fungal genetics to demonstrate a role for oxalate in the mediation of the host redox environment and preparation of host cells for Sclerotinia infection . Cells have a limited number of molecules and combinations that can be deployed for various aspects of regulation , growth and development . The use of a single molecule to perform several seemingly unrelated tasks is a common strategy employed by organisms to increase the range of functions using a defined and limited repertoire of molecules . For example , the catabolic enzyme mannitol dehydrogenase is also a pathogenesis related ( PR ) protein that can be induced by pathogens even in mannitol non-containing plants [18] . Proline is a non-essential amino acid that functions as an osmolyte during stress . We have shown that proline is also a potent anti-oxidant and is associated with such far ranging stresses from mammalian diseases to plant drought responses as well as nutrition [19] . In this study we show that fungal oxalic acid is another such example . This “simple” organic ( dicarboxylic ) acid , is efficiently used by Sclerotinia for a range of processes that include , direct toxicity , development ( sclerotia ) , pH signaling , activation of cell wall degrading enzymes , plant guard cell regulation , chelation of calcium , vascular plugging , elicitation of programmed cell death [8] , [9] , [reviewed in 10] , [12] and in this report , directing the redox environment in the host cell for pathogenesis . Our accumulating evidence shows that OA generates reducing conditions in the cell , correlating with the inhibition of the host oxidative burst and other defense responses . This is followed by an OA mediated plant programmed cell death and eventual establishment of disease . Several lines of evidence are consistent with these conclusions: 1 ) When OA is not present in the “infection court” as is the case with the A2 strain , the plant oxidative burst is clearly evident and a resistant response ensues 2 ) Sclerotinia rapidly induces reducing conditions in host cells in advance of fungal growth , and this redox manipulation is tightly associated with the onset of disease . 3 ) The modulation of the host redox environment is subverted by OA; OA-deficient mutants were unable to induce reducing conditions and were unable to cause disease . 4 ) Temporally , this reduction precedes ROS synthesis which is necessary for cell death . 5 ) The optimum pH for OA induced reduction corresponds to the pH conditions required for OA induced ROS and subsequent PCD . 6 ) The addition of a potent reducing agent , DTT , generates transient reducing conditions during initial infection that suppress host defense and the oxidative burst to revert the OA-deficient mutant phenotype and restore pathogenicity . Therefore OA appears to have dual opposing roles in Sclerotinia pathogenesis; OA initially inhibits ROS-mediated plant defense responses , but later promotes ROS generation in the plant followed by programmed cell death . These data address a long-standing issue in this system involving the requirement for Sclerotinia/OA to both inhibit and promote ROS to achieve pathogenic success . In Monilinia fructicola , a stone fruit pathogen related to Sclerotinia , intracellular antioxidant levels in the fungus are influenced by host derived phenols , altering the fungal redox environment , though not affecting fungal growth . However pathogen gene expression and pathogen infection structure differentiation were directly affected and were related to changes in electrochemical redox potential . Monilinia also possesses a redox regulated cutinase gene , which is upregulated during oxidative stress and when overexpressed , increases virulence [20] , [21] . Thus , the redox balance in both the host and pathogen can be a key battlefield in determining the outcome following pathogen challenge . The mechanism by which OA triggers such conditions is a key question , and could involve redox molecules such as thioredoxins . Thioredoxins are ubiquitous redox proteins that act as antioxidants by facilitating the reduction of proteins involved in a variety of physiological roles within cells including the activation of plant defense pathways . For example , the redox-sensitive Arabidopsis thioredoxin-5 ( TRX5 ) mediates a conformational change in the non-expressor of PR genes ( NPR1 ) , which is necessary for the activation of plant immunity [22] . NPR1 is a key transcriptional regulator in the signaling pathways that lead to systemic acquired resistance [23] . In unchallenged plants , NPR1 is maintained as an inactive oligomer in the cytoplasm via redox-sensitive disulphide bonds . During pathogen challenge however , the redox state of the cell is altered via the plant hormone , salicylic acid . This change in cellular redox leads to the reduction of disulphide bonds and release of an NPR1 monomer that translocates to the nucleus where it functions as transcription factor for defense signaling . Although the outcome is different to that observed during Sclerotinia interactions , the overall strategy of host redox alteration during cell death regulation ( in this case plant immunity ) is maintained . In the case of Sclerotinia , the pathogen is in control of the redox environment and cell death pathways; the oxidative burst does not occur and the pathogen directly benefits . In contrast , during an immune response , the plant controls the redox environment and cell death pathways to the detriment of the pathogen . As observed following inoculation with the A2 mutant , the host was able to mount an oxidative burst , as well as callose deposition and thus effectively resist infection by mounting an HR-like response . Another potential link between thioredoxins and pathogenicity involves the necrotrophic oat pathogen , Cochliobolus victoriae that produces the host selective toxin victorin . Analogous to Sclerotinia , victorin deficient Cochliobolus strains are non-pathogenic on susceptible oat genotypes; exogenous application of victorin results in disease symptoms , including features associated with apoptotic-like programmed cell death such as DNA laddering and caspase-like protease activity [24] . Of relevance to our work , this study has shown that sensitivity to victorin also requires the activity of the plant thioredoxin , TRX5 . In this case , victorin may recruit TRX5 to alter the host cellular environment . Although the exact role for TRX5 activity during sensitivity to victorin is unknown , the observation that victorin sensitivity requires thioredoxin activity suggests that pathogen mediation of the host redox environment may also occur during C . victoriae challenge . Further evidence supporting the key role of host redox manipulation for optimal Sclerotinia infection is illustrated by work with oxalate oxidases . Oxalate oxidases ( “germin” proteins ) are members of the oxidoreductase family found in all monocots and catalyze the breakdown of OA into CO2 and H202 [25] . Knowing that OA is a pathogenicity determinant of Sclerotinia , several groups have generated dicot plants over-expressing a monocot oxalate oxidase and observed increased resistance to Sclerotinia [25]–[27] . Similarly , our studies also demonstrated high levels of plant H202 and delimited growth in response to infection with the oxalate deficient A2 mutant . Therefore , removal of OA during infection , either by expression of an oxalate oxidase in the plant or genetically , through the use of OA-deficient Sclerotinia mutant strains ( i . e . A2 ) leads to oxidizing conditions controlled by the host and the mounting of host defense responses . Additionally , Sclerotinia , while possessing an impressively broad host-range , does not infect monocots . We speculate that this inability to create reducing conditions in the host explains at least in part , why Sclerotinia diseases are essentially limited to dicotyledonous plants . In future studies we will identify monocot germin knockout lines and evaluate pathogenic behavior of Sclerotinia . The host target ( s ) of OA are not known . Recently , we have found that Arabidopsis plants lacking BIK1 , were significantly enhanced in susceptibility to A2 ( Figure S3 ) . BIK1 is an Arabidopsis cytoplasmic receptor kinase ( Botrytis induced kinase ) that mediates crosstalk between defense pathways in Arabidopsis [28] . BIK1 was originally discovered as a component in plant defense against the necrotroph Botrytis cinerea [29] and has subsequently been shown to play an important role in defense signaling to initiate MAMP triggered immunity [28] , [29] . Intriguingly , our initial studies have also shown that OA can modulate the phosphorylation status of BIK ( data not shown ) . Studies are in progress to determine whether this is a direct or indirect interaction , but regardless , OA appears to inhibit defense signaling mediated by BIK . Thus , BIK represents a potential host target for Sclerotinia . Moreover there may be shared components of MAMP defense signaling and necrotrophic fungal pathogenesis . In summary , we show in real-time using a redox sensitive GFP reporter that the earliest detectable host response to Sclerotinia challenge is the creation of a fungal ( OA ) induced reducing environment that is observed in advance of pathogenic fungal growth . In contrast , the loss of oxalate in the fungus leads to the failure of host colonization and induces strong plant defense responses , as noted with the OA-deficient A2 mutant . This strain failed to colonize the host and induced strong HR-like host defenses ( including an oxidative burst ) similar to those commonly observed during incompatible biotrophic infections . We suggest that reducing the cellular environment directly or indirectly suppresses the host-plant oxidative burst and defense mechanisms , including callose formation . The net result provides Sclerotinia with precious time for unimpeded establishment in host tissue and the hijacking of host pathways to generate ROS and induce PCD; a perfect environment for this necrotroph . Thus the initial pathogenic phase of this well established necrotroph surprisingly , displays features similar to those observed during compatible biotrophic or early stage hemi-biotrophic interactions .
S . sclerotiorum isolate 1980 and an oxalate-deficient mutant ( A-2 ) of this strain were maintained at 24°C on potato dextrose agar as previously described by [7] . NOX1 , NOX2 , and SOD strains were generated for a different study using a split-marker-deletion approach as described by [30] . Botrytis cinerea strain B05 . 10 was provided by Dr . Jan van Kan . The c-roGFP1 was kindly donated by Lewis Feldman . Wild type and transgenic Nicotiana benthamiana plants expressing the ro-GFP constructs [15] were generated as described below and maintained in tissue culture under a 16-h light period . Electro-competent Agrobacterium ( strain LBA 4404 ) were transformed with plasmids c-roGFP1 [15] by electroporation using an EC100 electroporator ( Thermo EC ) based on the method of [31] . Wild type Nicotiana benthamiana leaf discs were transformed by Agrobacterium as described by [32] . Following transformation , leaf discs were sub-cultured every two weeks on MS104 media containing timentin ( 200 mg/L ) and hygromycin ( 50 mg/L ) . After five weeks of culture , shoots of a suitable size were transferred to MSO media containing timentin ( 200 mg/L ) and hygromycin ( 50 mg/L ) . Replicates were generated by nodal cutting and culture on MSO media containing timentin ( 200 mg/L ) and hygromycin ( 50 mg/L ) . Emerging leaves of wild type and ro-GFP expressing Nicotiana benthamiana were exicised and prepared on microscope slides in distilled water . Redox GFP excitation and fluorescence were viewed using an Olympus IX81 microscope with long and short pass GFP filter sets , under a 10× magnification . Newly emerging leaves of wild type and ro-GFP expressing benthamiana were excised and inoculated with 5 mm PDA plugs containing actively growing wild type Sclerotinia isolate 1980 , OA-deficient ( A2 ) mutant , NaDPH oxidase 1 and 2 ( nox 1 and 2 ) and superoxide dismutase ( SOD ) mutant Sclerotinia . Leaves were analyzed over an eighteen hour time-course for GFP fluorescence using an Olympus SZ×10 and mGFPA long pass filter set ( exc . 460–490 nm , emm , 510 nm ) . Botrytis cinerea strain B05 . 10 was grown on Malt Agar , inoculation and GFP fluorescence analysis was performed as described above . Newly emerging cyt-roGFP leaves were infiltrated with either 10 mM KOA buffered to pH 3 or 7 , or 10 mM HCl and analyzed over an eight hour time course for GFP fluorescence using an Olympus SX-10 and the mGFPA filter set . For investigation of OA-mediated reduction , oxidation and PCD , newly emerged roGFP leaves were infiltrated with either 10 mM KOA buffered to pH 7 or water as described above . Infiltrated leaves were excised at time points of 0 , 3 , 6 and 12 hours post-treatment and analyzed for GFP expression using a Olympus SX-10 microscope and a mGFPA long pass filter set ( exc . 460–490 nm , emm , 510 nm ) , DAB staining and Evans blue staining , respectively .
|
Necrotrophic fungal pathogens need to kill plant cells to establish disease and obtain nutrition . While such pathogens are economically important , they are relatively understudied and mechanistic details important for pathogenic success are limited . Sclerotinia sclerotiorum is a necrotrophic ascomycete fungus that infects virtually all dicotyledonous ( >400 species ) plants . Our data indicate that oxalic acid production and modulation of reactive oxygen species ( ROS ) are key components for the successful interaction of this fungus with the host plant . Here , we use a GFP regulated reporter system to analyze the host redox status during infections with wild type and a non-pathogenic oxalic acid ( OA ) deficient Sclerotinia mutant . Additionally , we show that secreted OA enables Sclerotinia to hijack the host cell redox machinery , initially creating reducing conditions followed by an oxidizing environment that is necessary for pathogenesis . We also provide evidence that the OA-deficient mutants are actively recognized by the plant resulting in the elicitation of a hypersensitive-like response and resistance . Our study provides insight into how Sclerotinia , and quite possibly other necrotrophic pathogens , co-opt host redox and cell death pathways for successful colonization of the host .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biotechnology",
"biochemistry",
"pest",
"control",
"biology",
"agriculture"
] |
2011
|
Tipping the Balance: Sclerotinia sclerotiorum Secreted Oxalic Acid Suppresses Host Defenses by Manipulating the Host Redox Environment
|
A detailed understanding of the morphology of the HIV-1 envelope ( Env ) spike is key to understanding viral pathogenesis and for informed vaccine design . We have previously presented a cryoelectron microscopic tomogram ( cryoET ) of the Env spikes on SIV virions . Several structural features were noted in the gp120 head and gp41 stalk regions . Perhaps most notable was the presence of three splayed legs projecting obliquely from the base of the spike head toward the viral membrane . Subsequently , a second 3D image of SIV spikes , also obtained by cryoET , was published by another group which featured a compact vertical stalk . We now report the cryoET analysis of HIV-1 virion-associated Env spikes using enhanced analytical cryoET procedures . More than 2 , 000 Env spike volumes were initially selected , aligned , and sorted into structural classes using algorithms that compensate for the “missing wedge” and do not impose any symmetry . The results show varying morphologies between structural classes: some classes showed trimers in the head domains; nearly all showed two or three legs , though unambiguous three-fold symmetry was not observed either in the heads or the legs . Subsequently , clearer evidence of trimeric head domains and three splayed legs emerged when head and leg volumes were independently aligned and classified . These data show that HIV-1 , like SIV , also displays the tripod-like leg configuration , and , unexpectedly , shows considerable gp41 leg flexibility/heteromorphology . The tripod-like model for gp41 is consistent with , and helps explain , many of the unique biophysical and immunological features of this region .
HIV-1 and the closely related SIV envelope ( Env ) spikes are composed of a trimer of heterodimers [1]–[6] . The base of the Env spike is comprised of three gp41 subunits , each of which possesses , from N-terminal to C-terminal , a fusion peptide , N-terminal heptad repeat , disulfide loop , C-terminal heptad repeat , membrane proximal external region ( MPER ) , transmembrane domain , and cytoplasmic tail ( CT ) . The relative positions of these various elements in the mature untriggered spike are largely unknown [7] . In contrast to gp41 , the configuration of gp120 is better defined structurally . The CD4-liganded core structure consists of three subregions , the inner domain , the outer domain and the bridging sheet [8] , [9] . The atomic structure of the unliganded SIV core has recently been described [10] . For both atomic structures , some of the more flexible elements , including V loops , N and C-terminal peptides and much of the glycan shield , were either deleted from the crystallization construct or were not resolvable due to flexibility [8]–[10] . The inherent flexibility of the V loops is a well recognized characteristic of HIV gp120 and has been suggested to be an important component of the viral defense against humoral immunity . Similarly , the CD4 binding site ( CD4bs ) components display flexibility , limiting the ability of most potential anti-CD4bs Abs to effectively bind , a process known as entropic masking [11] . Electron microscopy ( EM ) is an important adjunct to atomic structural studies and has the potential to allow the placement of the atomic structures of gp120 and gp41 core fragments and peptides , as well as the unresolved flexible components , into the global structural context of the Env spikes in situ [12] . Early work by Gelderblom and others showed virions covered with varying numbers of spikes [13]–[15] . A substantial fraction of purified spikes from HIV-1 and SIV were shown to display 3-fold symmetry though other forms were observed [1] , [2] . By negative stain electron tomography , clear evidence for 3-fold symmetry was observed for a mutant form of SIV exhibiting Env with a truncated cytoplasmic tail [6] . The picture for HIV-1 is less clear in that the presumptive Env spikes appeared to display structural heterogeneity ( [6] and unpublished data ) . Biochemical evidence for structural heterogeneity has also been published [1] , [16] , [17] . Because of the potential for morphological artifacts resulting from the use of the negative staining EM technique , including the attachment to a carbon substrate , pH changes , and drying , definitive analyses of the spike architecture could not be performed by this method [6] . More recently , we have further investigated the overall configuration of the SIV Env spike using cryoEM tomography ( cryoET ) wherein samples are preserved in a frozen hydrated state , free of the potential staining and drying artifacts common to negative staining [18] . Advantage was taken of the high level of Env spike incorporation and expression on the short-tailed mutants ( SIVmac239/251 tail CEMx 174 ) ( ∼70 spikes/virion vs . ∼7–10 for wtSIV and wtHIV-1 ) thus aiding in data collection for the cryoET studies . HIV-1 variants with comparably high levels of Env spike expression are not available . The results from the SIV mutant show an Env spike in which each protomer of the presumptive trimeric gp120 head displays several morphological features and the solvent-accessible portion of gp41 forms a tripod-like set of legs . We were able to provide a tentative fit for the unliganded SIV gp120 core atomic structure [10] within the cryoET density volume and suggested that regions of unoccupied volume represented the masses of the V1/V2 and V3 loops missing from the atomic structure . In an effort to determine the degree of structural heterogeneity within the spike population , the individual spike volumes were subjected to classification analysis in which the spikes were sorted into groups according to structural similarity . The results showed that most of the spikes were similar in form [18] . Subsequently , Zanetti et al . published a cryoET study showing an SIV Env spike average which differed from Zhu et al . in several important aspects [19] . For example , each gp120 subunit consisted of a simple globular mass adequate in volume to accommodate the atomic model of the core structure [10] but not large enough for the considerable mass of the V1/V2 and V3 loops . They also reported that the gp41 ectodomain formed a compact stalk rather than the flared tripod configuration that we observed . These differences are not easily reconciled since both groups took advantage of similar SIV short-tailed mutant virus . In this report we have extend our cryoET analysis of the Env spike structure to include native ( unmutated ) Env spikes on wtHIV-1 and have now applied enhanced data collection and analysis techniques to generate 3D models . The data reveal that , as with our model of the short tailed SIV Env spike mutant , the wtHIV-1 displays tripod-like gp41 “legs” , at least in a significant percentage of the spikes . However , application of new approaches to search for structurally distinct morphological variations ( i . e . , a new classification algorithm ) within the data suggests considerable conformational variability which likely reflects a more flexible structure than previously described .
The highly purified virus ( HIV-1 BaL / SUPT1-CCR5 CL . 30 , lot p3955 ) used in this study was produced and provided by the AIDS Vaccine Program , SAIC Frederick , Inc . , NCI , Frederick , MD . The production and purification procedures were as previously described [20] . The samples were treated with 2 , 2′-dithiodipyridine ( Aldrithiol-2 , AT-2 ) , a process that eliminates viral infectivity while preserving Env structure and function [20] , [21] . Fifteen µl of AT-2-treated viruses ( ∼2 . 8 mg/ml total protein ) were added to 120 µl of PBS and pelleted at 25 psi for 15 min in an Airfuge centrifuge ( Beckman Coulter ) equipped with an A100/30 rotor . The pellets were resuspended in 10 µl of PBS of which 3 . 5 µl was placed on a 300 mesh R2/1 Quantifoil grid ( Quantifoil , Jena , Germany ) for 1 min . Excess virus and buffer was blotted with filter paper . The grid was then rapidly vitrified by plunging into liquid ethane in a liquid nitrogen bath using a homemade plunging apparatus . The EM grids were transferred to a Gatan 626 cryoholder ( Gatan , Pleasanton , CA ) and examined under low dose conditions on a Philips ( FEI , Eindhoven , Netherlands ) CM300-FEG microscope operated at 300 kV . Single axis tilt series were recorded at 43 , 200× magnification using a Tietz Tem-Cam F224 slow scan CCD camera ( 2 , 048×2 , 048 pixels , Tietz Video , Gauting , Germany ) and associated EM-MENU software . The pixel size at the specimen level is 5 . 56Å . Each tilt series consisted of 70–80 images recorded over an angular range of ±60° to ±70° at increments chosen according to the cosine rule [22] . The electron dose was estimated at 1–2 e−/Å2 per image .
Our initial selection of 2 , 874 Env spike volumes , derived from 181 wt HIV-1 virions was subsequently reduced to 2 , 070 through programmatic elimination of lower quality selected volumes during the automated phase of the classification process . Subsequently , the spikes were sorted into eight classes based on multivariate data analysis and the class members in each were aligned and averaged . The surface distribution of the selected spikes in the entire set as well as in each of the eight classes suggests that the sampling was random and that the classification scheme did not result in the biased clustering of positionally discrete subpopulations ( e . g . , top/bottom- or side-arrayed ) as might be expected with inadequate missing wedge compensation ( Figure 1 ) . Figure 3A shows cross-sections through the broadest portion of the head ( H ) , the leg region ( L ) just above the membrane ( see boxed insert for illustration ) , and a side view ( S ) of a section parallel to the axial plane with the membrane at the bottom of each of the eight classes . Although heteromorphic , each class average displayed overall dimensions similar to each other ( ∼12 nm high , ∼11 nm wide ) and to our previously published SIV spike model ( 13 . 7 nm high , 10 . 5 nm wide ) [18] , thus increasing our confidence that the selected viral surface molecules represent bona fide Env spikes . Four of the classes showed a tendency toward 3-fold symmetry ( judged visually ) , at least in the head region ( Figure 3A , H5–8 ) while the other four showed asymmetry , though none were circular or shapeless . A comparative analysis of a cross-section through the base of each spike just above the membrane showed heterogeneity in structure incompatible with the single gp41 stalk model [19] . Most classes show two ( Figure 3A , L3 , 6 , 8 ) or three densities ( Figure 3A , L1 , 2 , 5 , 7 ) , with some showing additional diffuse signals . None , however , show three truly distinct leg densities . Subvolume classification schemes can give a sense of the range of structure variation in the subvolumes selected for comparison . The real strength of the approach is that it facilitates a determination as to whether subsets ( classes ) of objects comprise the larger population . Relatively rigid , structurally distinct subpopulations , if present and significantly different from one another , should sort into distinct classes when subjected to multivariate data analysis . Even an inherently flexible structure displaying more random movement over significant distances would be expected to sort into separate classes although the result would tend to be blurred compared to the result expected in the case of structurally discrete states . Conformational variability within individual Env subunits ( i . e . , gp120 protomers ) would not be detectable at the current resolution limits of this technique . Evidence of inter-subunit conformational variability should be most easily discerned by examining the spikes down the polar axis , i . e . , screening for symmetry by viewing from above or below . Even greater modes of flexibility might be manifested as twisting and/or rocking motion of gp120 and gp41 with respect to one another ( Figure 2A ) . Because the alignment algorithms assess voxel density ( signal intensity ) , the entire mass of the spike contributes to the placement of any given spike density within the average . Because the gp120 head is of greater mass than the solvent exposed regions of gp41 , i . e . , the MPER , the head would be expected to have a proportionally greater influence on the average . As a consequence , if the gp41 legs are in a tripod-like configuration but some were twisted , for example , with respect to the gp120 heads , the gp41 signals would be blurred to yield a cone of weak density rather than discrete , dense tripod-like legs in the averaged models . Similarly , the gp41 and membrane density might also impede alignment of the gp120 heads should the head and legs be twisted or bent with respect to one another . This is , in fact , what we observed for the MPER when the various classes , representing all of the classified Env spikes , were averaged together ( data not shown ) . To test this hypothesis , we reasoned that if the gp41 leg/membrane regions and gp120 head regions were independently aligned and/or classified , the weighting effect of one region upon the other would be eliminated and structural subregion classes with more discrete structural characteristics might emerge . Figure 2B–2D depicts the various head and leg subvolume alignment and classification schemes used to address this issue . For simplicity , the membrane is not depicted in the diagram . Alignments and , independently , classifications were based on the density data within either the whole spike , head , or leg/membrane regions . Overall , the data show that subunit alignment and classification enhanced the symmetry of the targeted subregion and conversely , had the predictable effect of blurring the detail of the subregion excluded from the classification . For example , when the gp120 head was excluded , allowing the leg densities to drive the classification ( Figure 3C ) , the gp41 leg region showed 3 of 8 classes ( Figure 3C , L4 , 6 , 8 ) ( representing 38% of total spikes ) with three more-or-less distinct densities ( judged subjectively ) with a tendency toward 3-fold symmetry , a pattern that was less obvious following alignment and classification of the whole spike where only 1 of 8 classes ( 11% of total spikes ) showed this pattern ( Figure 3A , L1 ) . Surprisingly , when both alignment and classification were driven by the leg densities , one class showed clear 3-fold leg symmetry ( Figure 3E , L6 , 12% ) though all the others showed multi-leg asymmetry . In some classes where two legs are obvious , one of the legs appears extra thick ( Figure 3C , L1 , 2 , 3; 3E , L 4 , 7 ) possibly indicating a ( transient ? ) association of two of the three legs . When the gp41 leg region was excluded from classification , 5 of 8 classes ( representing 63% of total spikes ) had gp120 head regions that displayed a tendency toward 3-fold symmetry ( Figure 3B , H1 , 3 , 5 , 6 , 8 ) compared to 4 of 8 classes ( 51% of total spikes ) for whole spike alignment and classification ( Figure 3A , H5–8 ) . This trend was more pronounced when both alignment and classification were performed using just the head volumes ( Figure 3D ) wherein 6 of 8 classes ( 75% ) were trimer-like ( Figure 3D 3–8 ) . Thus , even though there was clearly a tendency toward symmetry within the spikes , it was less evident following whole spike averaging , even when whole spike classification ( into 8 classes ) was applied . Interestingly , subvolume classification appeared about as effective in enhancing applicable head or tail images irrespective of whether the alignment was based on the whole spike or the targeted subvolume of the spike ( compare Figure 3B to 3D and 3C to 3E ) . Stated another way , the use of subvolume alignment appeared less important to the final outcome than the application of subvolume classification . The reason for the less dominant effect in enhancing substructures could be attributed to the fact that selective alignment based on head or leg regions was carried out as a refinement of the already aligned whole spikes . Thus , the applied incremental changes do not appear as significant as the structural differences obtained by classification . A significant percentage of the classified spikes and spike components deviated considerably from 3-fold symmetry . The reasons for this are unclear but include bona fide segmental flexibility/heteromorphology , and “noisy” data , a general characteristic of cryoEM data where contrast is inherently low . It is worth noting that in neither SIV nor HIV-1 did we observe any evidence of conserved structural features immediately below the membrane as would have been expected if the CT of Env were rigid or associated with a geometrically arrayed submembrane matrix layer . In order to construct a single volume rendering of an “idealized” HIV spike , we selected the spike subregion classes showing the most symmetric features and averaged them together as single classes . These averaged classes were then displayed as surface renderings as illustrated in Figure 4 . For example , the head-aligned/head-classified classes represented by Figure 3D ( 3–8 ) were averaged as a single class ( Figure 4C ) and , in the final step , 3-fold symmetrized ( Figure 4D ) . The best leg-aligned/leg-classified classes ( from Figure 3E , 3 , 6 , 7 ) were similarly combined , aligned , and averaged ( Figure 4E and F ) . The optimized heads from the first set of models ( Figure 4C and 4D ) were then grafted onto the legs of the second set of models ( Figure 4E and 4F ) to yield the composite HIV-1 spikes shown in Figure 4G and 4H . To determine the correct rotational orientation of the legs with respect to the head , we averaged the most symmetric classes from the whole unmasked classification scheme and measured the rotational orientation of the legs with respect to the head ( data not shown ) . Figure 4I and 4J represent transverse digital sections through the unsymmetrized and symmetrized chimeric models in Figure 4G and 4H , respectively . The ‘a’ , ‘b’ and ‘c’ designations represent the head , midsection , and membrane-proximal leg sections , respectively . A comparison of the HIV-1 composite model ( Figure 4G and 4H ) to our previously published SIV model [18] shows protein masses comparable to the main and lateral lobes , a less well defined peak , but no mass corresponding to the proximal lobe ( Figure 4C ) . Unlike the Zanetti et al . model , we find no discernable cavity at the head-leg interface [19] . Our HIV-1 model appears to have three splayed legs though they are less well defined compared to our SIV model [18] . The radii of the legs in those HIV-1 classes where three discrete legs were visible were comparable to that previously reported for SIV ( ∼4 . 8 nm ) [18] . No legs were seen in the Zanetti et al . model [19] . Evidence for Env spike structural heterogeneity also comes from a reanalysis of the Zhu et al . SIV data [Winkler et al . , J . Struct . Bio . , in press] . In contrast to what was originally reported [18] , heteromorphology in the spike appearance and apparent flexibility are also seen in that data when subjected to the same general alignment and classification scheme reported here . Although , not subjected to independent targeted head and leg classification and reassembly , trimeric structures in the SIV head and the splayed leg conformations were also seen . Thus , the methods used here have generated similar results on two independent data sets . We now feel that both the Zanetti et al . [19] and Zhu et al . 2006 [18] models were unduly influenced by the reference that was selected in the earliest cycle and that this reference accentuated certain features at the expense of others . Consequently , the details of the respective density maps and fitting of the atomic core structures in those reports should be considered as provisional . There is a general view that one of the main reasons no suitable Env-based vaccine for the induction of effective humoral protection has been developed relates to the difficulty in engineering soluble versions that faithfully mimic the viral spike surface configuration ( reviewed in [27] , [28] ) . Early monomeric constructs largely failed due to the exposure of immunodominant epitopes on the non-neutralizing face , a region believed buried in the gp120 subunit interface in the oligomer . Consequently , numerous attempts have been made at generating trimeric soluble constructs . However , many such constructs have proven inherently unstable with unacceptably high levels of subunit dissociation and/or aggregation . Strategies to circumvent this obstacle include mutational disruption of the protease cleavage sites between gp120 and gp41 , inter-subunit disulfide bonding and other stabilization enhancing point mutations , and the addition of trimerization motifs ( reviewed in [27] , [28] ) . These approaches have had varying degrees of success at stabilizing the trimer and occluding the non-neutralizing face but have yet to faithfully mimic the antigenic profile of authentic membrane-associated trimers . Our initial observation that the MPER of Env gp41 appeared to be in an open tripod-like conformation rather than in the traditionally-depicted compact stalk configuration provided a plausible explanation for the failure of at least some engineered version of Env trimers to adopt the native configuration [18] . Specifically , the trimerization motifs used to date , which bunched the C termini of the MPER tightly together , might force the MPERs into an unnatural configuration , perhaps altering and/or weakening the already inherently unstable interactions between the rest of the tripartite subunits . The resultant constructs may thus behave more like three tethered monomers rather than true trimers . On the other hand , if , as our data indicates , the Env spike is , to a degree , polymorphic and/or displays considerable component flexibility , a fully rigidified recombinant Env spike may not mimic the structure of virion-associated Env either . However , it may well turn out that rigid constructs , even if they don't fully mimic natural virus-associated Env might nevertheless serve as more effective vaccines . The production of a crystal structure of an Env trimer in its ( near ) native configuration would significantly advance our understanding of these and other issues relating to Env , however the prospects of success are diminished if the variations in form we observe are the result of segmental flexibility . Several reasons for moving cautiously in fully embracing the tripod-legged paradigm have been put forth [12] , [26] . First , the original modeled cryoET Env spike was derived from 3D tomograms of SIV rather than HIV-1 . Although the structure of the Env spikes on the two AIDS viruses have been assumed to be structurally similar and the atomic structures of the gp120 of the unliganded HIV-1 and liganded SIV core proteins have been extensively compared [10] , [29] , [30] , true similarity at the atomic level has yet to be formally demonstrated . Indeed , our previous negative stain EM tomogram studies have found HIV-1 spikes to be less uniformly configured than those on SIV ( [6] and unreported data ) . Other data indicate that the degree of compactness and subunit accessibility to ligand binding varies significantly between SIV and HIV-1 and even between different strains of each [11] , [28] , [31]–[33] . Second , the cryoET-modeled SIV Env spike derives from a mutated version of SIV displaying a truncated CT . While this feature enhanced the expression of Env spikes on virions , thus facilitating data collection , it could be argued that the loss of a considerable segment of CT might well influence Env spike structure , especially in the most closely associated MPER [34]–[36] . This concern is somewhat ameliorated by data demonstrating that the Env spikes on these mutants are sufficiently functional so as to support efficient viral fusion and host cell infection [37] . Third , as described above , it has been argued that our previous data collection and processing schemes might have skewed the data and thus the model . Fourth and finally , Zanetti et al . [19] , analyzed a short-tailed SIV virion nearly identical to those used by us yet they generated a rather different Env spike average model . Differences were observed both in the head ( more compact in Zanetti et al . ) and the presumptive gp41 solvent exposed region ( compact vertical stalk in Zanetti et al . ) . Some of the potential reasons and technical issues relating to these differences have been discussed elsewhere [12] , [26] . The data reported here support one of the key findings of our previous spike model in that we again find evidence of tripod morphology in the MPER . More importantly , this feature is now extended to include Env spikes from non-mutated wtHIV-1 . To allay concerns about artificially enhanced symmetry , no symmetric references were utilized nor was enforcement of three-fold symmetry applied in the alignment or classification schemes used to generate the eight classes . Yet evidence pointing to a tendency toward 3-fold symmetry both in the gp120 head region and in the gp41 MPER emerged , at least in some of the class averages . Even in those classes without three leg masses , typically two masses are present as is diffuse additional density , a pattern more consistent with a three flexible leg model than a compact stalk model . Only in the final surface rendered models of averaged selected classes was symmetry enforced ( Figure 4B , 4D , 4F , 4H ) . The accumulating evidence regarding the biophysical features of both the MPER and the neutralizing MAbs that target this region are consistent with it having extensive membrane association [38]–[44] ( see [45] and [46] for a comprehensive reviews of the MPER ) . This region may also be fairly flexible . For example , the segment encompassed by the 4F10 , Z13e1 and 2F5 epitopes may transition between alpha-helical and alternative motifs to allow exposure of key residues that would otherwise be on opposite sides of the presumed alpha helical structure of this region . Such a transition would be required for effective binding of these MAbs in a membrane-associated environment [43] , [45] . Recent high resolution NMR evidence suggested that the HIV-1 4E10 targeted epitope of the MPER is initially largely buried in the lipid bilayer and may be partially extracted upon 4E10 binding [42] . The conformational change associated with this interaction is facilitated by a flexible hinge-like region within the epitope . Such inherent flexibility may well contribute to our observed heteromorphology in the leg region and is consistent with the spread tripod-like leg orientation in a significant fraction of the Env spikes . During the manuscript review process , Liu et al . 2008 [47] published a cryoET model of the HIV-1 spike with features that differed from the both the Zhu et al . [18] and Zanetti et al . [19] SIV spike as well as those reported here in several respects . Liu et al . report a compact stalk for gp41 and a Z-axis-elongated structure for gp120 in which the monomeric subunits make minimal contact with each other . Their unliganded gp120 structure could not readily accommodate the unliganded core structure of Chen et al . [10] but was fitted instead with the CD4 liganded core structure [8] , [9] . After evidence of symmetry became apparent in the early rounds of alignment , symmetry was imposed on subsequent rounds and spike densities not fitting this pattern were discarded . We suspect that this model , like those of Zhu et al [18] and Zanetti et al . [19] , may be unduly influenced by reference bias and imposed symmetry .
|
The envelope ( Env ) spikes on the surface of HIV-1 and SIV virions facilitate target cell tropism , binding , and entry , and serve as the sole targets of humoral ( antibody-mediated ) immunity . X-ray crystallography has previously revealed the atomic structures of key core domains and peptides of the gp120 and gp41 Env spike subunits , but the manner by which these components are arranged in the Env spike is still speculative . Cryoelectron tomography ( cryoET ) affords a view of the entire Env spike in the context of the intact virion . We have previously published a cryoET model of the SIV Env spike which showed a unique tripod-like leg configuration for the solvent-exposed ( external ) gp41 stalk region . This model is consistent with , and helps explain , many of the unique biophysical and immunological features of this region . Subsequently another group using similar technology and virions reported a spike model displaying a compact gp41 stalk inconsistent with our splayed-leg spike model . In this report , we apply enhanced analytical cryoET procedures to show that HIV-1 also displays the tripod-like leg configuration , and shows considerable gp41 leg flexibility/heteromorphology . These results have implications for the design of effective vaccines targeting this region and may provide new insights into Env spike function .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results/Discussion"
] |
[
"virology/virion",
"structure,",
"assembly,",
"and",
"egress",
"virology/immunodeficiency",
"viruses"
] |
2008
|
Cryoelectron Tomography of HIV-1 Envelope Spikes: Further Evidence for Tripod-Like Legs
|
Human immunodeficiency virus type 1 ( HIV-1 ) infection of the central nervous system ( CNS ) can lead to the development of HIV-1-associated dementia ( HAD ) . We examined the virological characteristics of HIV-1 in the cerebrospinal fluid ( CSF ) of HAD subjects to explore the association between independent viral replication in the CNS and the development of overt dementia . We found that genetically compartmentalized CCR5-tropic ( R5 ) T cell-tropic and macrophage-tropic HIV-1 populations were independently detected in the CSF of subjects diagnosed with HIV-1-associated dementia . Macrophage-tropic HIV-1 populations were genetically diverse , representing established CNS infections , while R5 T cell-tropic HIV-1 populations were clonally amplified and associated with pleocytosis . R5 T cell-tropic viruses required high levels of surface CD4 to enter cells , and their presence was correlated with rapid decay of virus in the CSF with therapy initiation ( similar to virus in the blood that is replicating in activated T cells ) . Macrophage-tropic viruses could enter cells with low levels of CD4 , and their presence was correlated with slow decay of virus in the CSF , demonstrating a separate long-lived cell as the source of the virus . These studies demonstrate two distinct virological states inferred from the CSF virus in subjects diagnosed with HAD . Finally , macrophage-tropic viruses were largely restricted to the CNS/CSF compartment and not the blood , and in one case we were able to identify the macrophage-tropic lineage as a minor variant nearly two years before its expansion in the CNS . These results suggest that HIV-1 variants in CSF can provide information about viral replication and evolution in the CNS , events that are likely to play an important role in HIV-associated neurocognitive disorders .
Human immunodeficiency virus type 1 ( HIV-1 ) infects CD4+ T cells in the blood and lymphoid organs . In addition , infection of the central nervous system ( CNS ) can result in mild to severe neurological disease , including HIV-1-associated dementia ( HAD ) [1] . Although the incidence of HAD and minor cognitive motor disorder have been significantly reduced following the introduction of highly active antiretroviral therapy ( HAART ) , these disorders continue to affect a substantial proportion of the HIV-1-infected population [2] , [3] . The insufficient CNS penetration of some antiretroviral drugs or viral resistance may allow HIV-1 to persist in the CNS during the course of therapy [4] , [5] , [6] , [7] . The success of HAART has led to an increased lifespan and an older demographic of HIV-infected subjects , and these subjects in particular have an increased risk of developing HAD due to their enhanced age [8] , [9] . Less severe neurological problems associated with HIV-1 infection such as minor cognitive impairments may also be increasing [10] , [11] , indicating that neurological disorders will remain a problem for HIV-1-infected subjects in the future . Finally , unequal access to HAART and the potential of CNS involvement prior to the initiation of HAART makes the question of HIV replication in the CNS relevant to many infected people . Several lines of evidence suggest that some HAD subjects can harbor macrophage-tropic HIV-1 variants [12] , [13] , [14] , [15] , [16] , [17] , a distinct phenotype associated with the ability to infect cells with low surface expression of CD4 . The initiation of antiretroviral therapy results in rapid decay of virus in the blood , which is associated with virus replicating in activated CD4+ T cells [18] , [19]; however , HIV-1 in the cerebrospinal fluid ( CSF ) can decay slowly with the initiation of therapy in some subjects with HAD , suggesting a longer-lived cell type as the origin of this virus [20] , [21] , [22] . Macrophage tropism does not appear to be a feature of the transmitted variants of HIV-1 [23] , [24] , leaving open the question of when and where macrophage-tropic variants of HIV arise and their role in HIV-1-associated pathogenesis . Previous studies have reported that HIV-1 populations in the CSF of HAD subjects have increased viral genetic compartmentalization compared to virus in the blood [25] , [26] , and genetically distinct HIV-1 variants have been detected at autopsy in the CNS of some subjects with HAD [27] , [28] , [29] , suggesting that autonomous viral replication is occurring in the CNS of subjects with more severe neurological disease . We examined HIV-1 variants in the CSF of HAD subjects to determine the viral genotypes and phenotypes associated with the development of HAD . Here we show that genetically compartmentalized CCR5-tropic ( R5 ) T cell-tropic and macrophage-tropic HIV-1 populations are independently found in the CSF of subjects diagnosed with HIV-1-associated dementia . Our results demonstrate that HIV-1 can replicate in at least two cell types within the CNS in association with the development of dementia . Macrophage-tropic viruses were poorly represented in the blood population , highlighting the restricted , tissue-specific host range of these variants .
A range of genetic compartmentalization ( 0–43% of the total CSF sequences ) was detected in the CSF HIV-1 populations of three neurologically asymptomatic subjects ( Figure 1A and Table 2 ) , which is consistent with reports from previous studies showing low but variable compartmentalization in asymptomatic subjects [26] , [32] . Viruses pseudotyped with Env proteins derived from virus in the blood plasma and CSF of these neurologically asymptomatic subjects could only infect cells with high CD4 surface expression ( Figure 1B; Table S1 ) . In addition , most Env-pseudotyped viruses did not efficiently infect MDM ( Figure 1C ) , although subject 4030 Env C23 infected about half as efficiently as the macrophage-tropic Ba-L envelope . This indicates that these viruses require the higher levels of surface CD4 found on activated CD4+ T cells for entry into target cells . While this represents a small sample size , this analysis demonstrates that T cell-tropic R5 viruses can be found in the CSF . Significant genetic compartmentalization was detected between the blood plasma and CSF HIV-1 populations of eight subjects diagnosed with HIV-1-associated neurological disease ( Figure 2 and Figure 3; Table 2 ) . We have previously shown that these subjects comprise two groups with respect to the rate of decay of compartmentalized virus in the CSF during the initiation of antiretroviral therapy: rapid decay in subjects 4033 , 4051 , 5003 , 7036; and slow decay in subjects 4013 , 4059 , 5002 , 7115 [20] . Phylogenetic analyses of the blood and CSF-derived virus in these subjects revealed significant compartmentalization and genetic distance between the blood plasma and CSF viral populations ( Table 2; Figure 2A and Figure 3 ) , indicating that sustained HIV-1 replication was likely occurring in the CNS of subjects with severe neurological disease . The detection of substantial compartmentalization in the HAD subjects was significant when compartmentalization versus an equilibrated population was compared to a model where each was equally likely ( P = 0 . 03 , Chi-squared test ) , or in comparison to the modest compartmentalization in the three asymptomatic subjects ( P = 0 . 02 , Fisher's Exact test ) . Clonal amplification of a specific viral lineage was identified for CSF variants that were separated phylogenetically from the plasma virus population in three HAD subjects ( subjects 4033 , 5003 , and 7036; Figure 2 ) who also had rapid decay of compartmentalized virus in the CSF [20] . The clonal amplification was seen as decreased env diversity in the CSF viral population , defined as having an average env pairwise distance <0 . 01 ( see Table 2 ) , and short branch lengths in the phylogenetic tree , both indicative of a recent expansion of a single variant . Clonal amplification of the CSF-compartmentalized HIV-1 population was associated with elevated CSF pleocytosis ( Table 1; P = 0 . 036 using a two-tailed Mann-Whitney test ) . We found that the clonally amplified , compartmentalized HIV-1 variants were not able to infect cells expressing a low surface density of CD4 ( Figure 2B ) and did not efficiently infect MDM ( Figure 2C ) , indicating that for these subjects the CSF-compartmentalized viruses were replicating in activated T cells . Phylogenetic analysis showed significant compartmentalization between the blood plasma and CSF HIV-1 populations , and a more genetically complex viral CSF population , for the remaining five subjects with neurological disease ( subjects 4013 , 4051 , 4059 , 5002 , and 7115; Figure 3 ) . We demonstrated in a previous study that these subjects had slow decay of compartmentalized virus in the CSF after the initiation of antiretroviral therapy , although subject 4051 had rapid decay detected in the CSF due to a complex viral population and only partial compartmentalization of CSF virus [20] . Compartmentalized HIV-1 envelopes from the CSF of these five subjects efficiently infected cells with a low CD4 surface density ( Figure 4A ) , and these Env proteins were macrophage-tropic based on their ability to infect MDM ( Figure 4B ) . In contrast , most HIV-1 Env proteins derived from the blood of these subjects were not able to mediate infection of cells with low CD4 surface expression and could only infect cells with high CD4 levels ( Figure 4 ) , indicating adaptation for replication in activated T cells in the peripheral blood . However , a macrophage-tropic lineage was detected in the blood plasma of subject 4059 ( Figure 3 and Figure 4 ) , consistent with the previous observation that it is possible to isolate macrophage-tropic viruses from the blood of some subjects in late-stage disease [12] , although this lineage was genetically distinct from that found in the CSF . The absence of the CNS macrophage-tropic virus lineage from the blood in the five subjects with macrophage-tropic virus in the CSF was statistically significant ( P = 0 . 03 , Chi-squared test ) . Finally , in the subjects with significant CSF compartmentalization there was a perfect correlation between the presence of R5 T cell-tropic virus and rapid viral load decay in the CSF , and with macrophage-tropic virus and slow viral decay in the CSF ( P = 0 . 03 , Fisher's Exact test ) . We further examined macrophage-tropic and R5 T cell-tropic viral population dynamics in the CNS by conducting longitudinal env genotypic and phenotypic analyses for two subjects who progressed to HAD ( Figure 5 and Figure S1 ) . We detected a clonally amplified , compartmentalized R5 T cell-tropic population in the CSF of subject 7036 at the time of HAD diagnosis , but this population was not present in the CSF prior to the diagnosis of dementia ( Figure S1 ) , suggesting rapid expansion of a discrete viral population around the time of diagnosis . In addition , CSF viral populations at sampling time points prior to HAD diagnosis ( 10/31/2002 and 4/28/2003 ) were less compartmentalized , similar to CSF populations detected in neurologically asymptomatic subjects ( Table 2 ) . Clinical assessment also revealed a dramatic increase in CSF viral load and CSF neopterin , a pteridine associated with intrathecal immunoactivation [33] , [34] , over the study period ( Table 1 and Figure S2A ) , which correlated with both the decline in neurological function from nearly asymptomatic to HAD ( see QNPZ-4 scores in Table 1 ) , and with the rapid expansion of a compartmentalized R5 T cell-tropic HIV-1 population in the CSF at this time-point . The other subject ( 7115 ) had a compartmentalized macrophage-tropic HIV-1 population present in the CSF at the time of dementia diagnosis . We identified a macrophage-tropic lineage in the CSF of this subject ( Figure 5A ) spanning a period of approximately two years prior to the diagnosis of severe dementia [see clones C17 ( −21 ) and C17 ( −16 ) ] . All of the HIV-1 env genes tested from this lineage encoded Env proteins that were macrophage-tropic based on the ability to infect cells with a low CD4 surface expression ( Figure 5B ) , and the ability to infect MDM ( Figure 5C ) . HIV-1 env genes from the blood populations at each time-point encoded proteins that were T cell-tropic . The viral sequences in the CSF from the macrophage-tropic lineage increased as a fraction of the population over time , especially between the time of HAD diagnosis and one month later . Neurological assessment of subject 7115 several years prior to HAD diagnosis revealed slightly impaired neurological performance ( see QNPZ-4 scores in Table 1 ) , and CSF viral load and neopterin were fairly stable over the two-year course of the study ( Table 1 and Figure S2B ) , perhaps indicating earlier onset of mild neurological disease , although this subject had other confounding conditions including drug use and psychiatric disease . These confounding factors precluded an AIDS dementia complex ( ADC ) stage determination at earlier sampling time-points ( 7/8/2002 and 12/3/2002 ) , although this subject was considered ADC stage 0 if this could be applied . Neurological performance markedly declined at the time of dementia diagnosis ( QNPZ-4 of −6 . 2 ) and continued to decline thereafter . Taken together , our data indicate that in this subject macrophage-tropic HIV-1 variants existed as minor variants within a specific evolutionary lineage of the CSF/CNS viral population and eventually became the dominant CSF population , suggesting that confounding factors may have obscured an understanding of the potential earlier contribution of HIV-related CNS dysfunction . Neurocognitive impairment can result from multiple factors , and knowledge about viral replication in the CNS , as viewed through virus in the CSF , can provide insight about the potential contribution of HIV-1 to the neurological status of the patient .
We have demonstrated that genetically compartmentalized R5 T cell-tropic and macrophage-tropic HIV-1 populations are independently found in the CSF of subjects diagnosed with HIV-1-associated dementia . Our study was limited to a cohort of eight subjects with neurological disease who were receiving lumbar punctures at the time of HAD diagnosis and while initiating therapy , with samples available prior to therapy in two subjects followed longitudinally . In spite of a relatively small sample size we were able to link several features that distinguish two different virological states associated with severe neurological dysfunction . Compartmentalized HIV-1 populations in the CSF with R5 T cell-tropic entry phenotypes were separated phylogenetically from plasma virus populations , and were associated with clonal amplification of the CSF viral population . Replication in CD4+ T cells is consistent with both the rapid decay of compartmentalized virus in the CSF after the initiation of therapy in these subjects , and the migration of immune cells into the CNS/CSF as indicated by the presence of elevated CSF pleocytosis . Compartmentalized macrophage-tropic HIV-1 populations were associated with more genetically diverse viral populations in the CSF , and the slow decay of virus in the CSF after the initiation of therapy , indicative of viral replication in a long-lived cell . However , given the small sample size we cannot provide an accurate estimate of the relative frequency of each type of virologic state other than to note that they appeared with similar frequency in this cohort of eight subjects ( three with compartmentalized R5 T cell-tropic virus , five with compartmentalized macrophage-tropic virus ) . Overall we detected a significantly compartmentalized CSF population in seven of these eight subjects suggesting that virus outgrowth in the CNS , whether macrophage-tropic or R5 T cell-tropic , will be a feature in a majority of HAD cases . HIV-1 replication in the CNS is thought to occur in perivascular macrophages and/or microglia within the brain parenchyma [13] , [35] , [36] . We found that both R5 T cell-tropic and macrophage-tropic HIV-1 populations are independently associated with clinical dementia . This indicates a more complex interaction between HIV-1 and the CNS since the genetically compartmentalized R5 T-cell tropic viruses are unlikely to be replicating in macrophages or microglia given their requirement for high levels of CD4 to enter target cells . During simian immunodeficiency virus ( SIV ) infection of macaques , CNS infection is associated with the presence of infiltrating SIV-specific CD8+ T cells in the brain , but infiltrating CD4+ T cells have not been detected [37] . Trafficking of CD4+ T cells has been reported in the CNS during infection of other neurotropic viruses [38] , [39] . We propose that the presence of viral antigen , especially during periods of increased HIV-1 replication in the CNS/CSF compartment , could drive the migration of both CD8+ and CD4+ T cells into the CNS/CSF ( resulting in elevated CSF pleocytosis ) and lead to persistence of compartmentalized virus through replication in the CD4+ T cells , and thus the apparent loss of this cell type . Consistent with the loss of these cells is the rapid decay of virus in the CSF during the initiation of therapy , which is considered a marker of viral replication in activated T cells [18] , [19] . The pathological determinants of HAD are poorly understood . Some subjects with dementia exhibit HIV-1 encephalitis ( HIVE ) characterized by the presence of multinucleated giant cells of the macrophage/microglia origin and immunohistochemical evidence of viral replication [40] , [41] . Although the incidence of HIVE has decreased during the HAART era , neuropathological changes in brain tissue , including glial activation and monocyte/macrophage infiltration [3] , [42] , [43] , are still common . Future studies examining HIV-1 populations in paired blood , CSF , and brain tissue from HAD subjects with and without neuropathological findings will help determine whether there are physiological differences in brain pathology between subjects with R5 T cell-tropic versus macrophage-tropic HIV-1 variants as the predominant CSF population . Also , the appearance of macrophage-tropic viruses largely restricted to the CNS/CSF compartment is most consistent with the appearance of these viruses late in the infection time course of HIV-1 , representing an expanded host range of the virus that is initially replicating in activated T cells . Although severe neurological disease associated with HIV-1 infection has declined in the HAART era , milder forms of neurological disease are increasing . In this study we detected a significantly compartmentalized macrophage-tropic HIV-1 population in the CSF of one subject with more mild neurological dysfunction ( subject 4013; Table 1 ) , illustrating the potential importance of understanding the correlates of HIV-1-associated neurological dysfunction with CNS/CSF viral population phenotypes . HIV replication in the CNS can contribute to neurocognitive decline , so the ability to detect features of the CSF viral population associated with viral replication in the CNS may provide new opportunities to guide interventions prior to the development of overt neurological disease . In our study , one subject with longitudinal sampling ( subject 7115 ) had macrophage-tropic variants present as a minor CSF population prior to the diagnosis of severe dementia ( Figure 5 ) . The application of sequencing technologies with greater capacity to sample a large number of viral genomes would allow the identification of minor CSF population variants , but this approach would rely on genotypic determinants of macrophage tropism rather than the phenotypic determinants used in our study . Several sequence determinants in env have been reported to be associated with macrophage tropism [27] , [44] , [45]; however , none of these determinants distinguishes the CSF-derived macrophage-tropic viruses from the paired blood-derived T cell-tropic viruses for the subjects in our study . Thus , the evolution of macrophage-tropic virus likely occurs through multiple pathways that will require a larger catalog of env sequences to allow reliable genotypic identification . It remains a possibility that the clonal amplification of R5 T cell-tropic viruses we detected in three HAD subjects is obscuring a smaller population of macrophage-tropic CNS virus , a question that could be addressed using more sensitive sampling methods . Developing a more complete understanding of the virological markers of CNS replication , and utilizing deep sequencing technologies to find minor populations , will provide opportunities to examine the use of CSF for information about viral replication in the CNS as a potential predictor of neurological involvement in the pathogenic process .
This study was conducted according to the principles expressed in the Declaration of Helsinki . The studies were approved by the Committee for Human Research at the University of California at San Francisco , and written informed consent was obtained for the collection of samples from all subjects or their health care surrogates when informed consent was not considered possible . All subjects included in this study were HIV-1-infected individuals that eventually initiated highly-active antiretroviral therapy . The subject samples used for viral genetic compartmentalization and Env protein phenotypic analyses were collected during previous studies carried out at the University of California at San Francisco [46] . Serial blood plasma and cerebrospinal fluid ( CSF ) samples were collected from subjects at baseline prior to the start of therapy and during the initiation of antiretroviral therapy , and samples were collected longitudinally from subjects 7036 and 7115 for several years prior to the initiation of therapy . Plasma and CSF HIV-1 RNA concentrations were determined using the Amplicor HIV Monitor kit ( Roche ) . Blood CD4 and CSF white blood cell ( WBC ) counts were performed by the San Francisco General Hospital Clinical Laboratory using routine methods . Subjects all underwent standardized neurological testing , including clinical criteria for diagnosis and staging of ADC . They also underwent brief quantitative neurological testing using four tests to derive a normalized score , the QNPZ-4 [47] . HIV-1 RNA was isolated from blood plasma and CSF samples as previously described [20] . Viral RNA was reverse transcribed using Superscript III Reverse Transcriptase ( Invitrogen ) with oligo ( dT ) as the primer per the manufacturer's instructions . Single genome amplification of the full-length HIV-1 env gene through the 3′ U3 region was conducted as previously described [30] . Briefly , cDNA was diluted to endpoint , and nested PCR was conducted using the Platinum Taq High Fidelity polymerase ( Invitrogen ) . The primers B5853 UP0 and LTR DN1 , and B5957 UP1 and LTR DN1 , were used for the first and second rounds of PCR , respectively [48] . The SGA amplicons were sequenced from the start of V1 through the ectodomain of gp41 [Hxb2 numbering of positions 6600–8000] . Nucleotide sequences of the env genes were aligned using Clustal W [49] , [50] or MAFFT software [51] . Sequences from each subject were codon aligned using GeneCutter ( Los Alamos HIV-1 database; http://www . hiv . lanl . gov/content/sequence/GENE_CUTTER/cutter . html ) . Maximum likelihood phylogenetic trees were generated using PhyML [52] with the following parameters: HKY85 nucleotide substitution model , four substitution rate categories , estimation of the transition/transversion rate ratio , estimation of the proportion of invariant sites , and estimation of the gamma distribution parameter [53] . Compartmentalization of CSF viral populations by sequence was determined using the Slatkin-Maddison test for compartmentalization [54] by HyPhy software [55] using 10 , 000 permutations . Pairwise distance was calculated for HIV-1 env sequences in the CSF-compartmentalized population using MEGA 4 . 1 software [56] , [57] , [58] . The SGA amplicons used in the cloning procedure were selected based on each subjects' phylogenetic tree structure and sequenced from the start of gp120 to the end of gp41 . An additional PCR was conducted to amplify only the full-length HIV-1 env gene using the Phusion hot start high-fidelity DNA polymerase ( Finnzymes ) and the primers B5957F-TOPO ( 5′-CACCTTAGGCATCTCCTATGGCAGGAAGAAG-3′ ) and B8904R-TOPO ( 5′-GTCTCGAGATACTGCTCCCACCC-3′ ) following the manufacturer's instructions . HIV-1 env amplicons were then gel purified using the QIAquick gel extraction kit ( Qiagen ) . The purified HIV-1 env genes were cloned into the pcDNA 3 . 1D/V5-His-TOPO expression vector ( Invitrogen ) using the pcDNA 3 . 1 directional TOPO expression kit ( Invitrogen ) and MAX Efficiency Stbl2 competent cells ( Invitrogen ) as per the manufacturer's instructions . 293T and TZM-bl cells were cultured in Dulbecco's modified Eagle medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) and 100 µg/ml of penicillin and streptomycin . 293-Affinofile cells [31] were maintained in DMEM supplemented with 10% dialyzed FBS ( 12–14 kD dialyzed; Atlanta biologicals ) and 50 µg/ml blasticidin ( D10F/B ) . The Affinofile cell line was generously provided by Dr . Ben-Hur Lee . Monocyte-derived macrophages ( MDM ) were isolated from Ficoll-purified PBMCs ( Biological Specialty Corporation , Colmar , PA ) using the human monocyte enrichment kit without CD16 depletion ( Stemcell Technologies ) as per the manufacturer's instructions . Following isolation , MDM were seeded in 48-well tissue culture plates and cultured for 6 days . MDM were cultured in RPMI 1640 medium supplemented with 10% FBS , 100 µg/ml of penicillin and streptomycin , and 10 ng/ml recombinant human macrophage colony stimulating factor ( M-CSF; Gibco ) . Env-pseudotyped luciferase reporter viruses were generated by co-transfection of 293T cells with 3 µg HIV-1 env expression vector and 3 µg of the pNL4-3 . LucR-E- plasmid ( obtained from the NIH AIDS Research and Reference Reagent Program , Division of AIDS , NIAID , NIH ) [59] , [60] using the FuGENE 6 transfection reagent and protocol ( Roche ) . Five hours post-transfection the medium was changed and the cells were incubated at 37°C for an additional 48 hours , and viral supernatants were harvested . Two hours prior to infection the coreceptor inhibitors TAK-779 [61] , [62] and bicyclam JM-2987 ( hydrobromide salt of AMD-3100 ) [63] , [64] , [65] ( both obtained from the NIH AIDS Research and Reference Reagent Program , Division of AIDS , NIAID , NIH ) were added to TZM-bl cells at concentrations of 2 . 5 µM and 5 µM using the following conditions for each virus: no drug , TAK-779 only , AMD-3100 only , and both drugs . Cells were infected in the presence of drug using 50 µl of viral supernatant per well and spinoculated ( 2 , 000 rpm ) for 2 hours at 37°C . Infections were incubated for 48 hours at 37°C , and then the cells were harvested and luciferase activity was assayed using the Luciferase assay system ( Promega ) . All infections and conditions were conducted in triplicate . 293-Affinofile cell [31] CD4 and CCR5 receptor expression was induced with tetracycline and ponasterone A ( ponA; Invitrogen ) , respectively . Cells were induced for 18 hours at 37°C in a matrix format for a total of 24 induction levels with varying amounts of tetracycline ( 0–0 . 1 µg/ml ) and ponA ( 0–2 µM/ml ) . Receptor expression was measured using quantitative fluorescence-activated cytometry ( qFACS ) . Cells were stained with either phycoerythin ( PE ) -conjugated anti-human CD4 antibody ( clone Q4120 , BD Biosciences ) or PE-conjugated mouse anti-human CCR5 antibody ( clone 2D7 , BD Biosciences ) . CD4 and CCR5 receptor levels were quantified using QuantiBRITE beads ( BD Biosciences ) . Env-pseudotyped luciferase reporter viruses were initially titered on 293-Affinofile cells expressing the highest induction levels for CD4 ( 0 . 1 µg/ml tetracycline ) and CCR5 ( 2 µM ponA ) surface expression . The amount of pseudotyped virus used in the single-cycle Affinofile infection assay was normalized to 1×106 relative light units for infection at the highest drug levels tested . All pseudotyped viruses were used within the linear range of the assay , and all infection conditions were assayed in quadruplicate . Two days prior to infection , 96-well black tissue culture plates were coated with 10% poly-lysine in PBS and seeded with 293-Affinofile cells ( 2 . 5×104 cells/well ) . Expression of CD4 and CCR5 was induced the following day by adding varying concentrations of tetracycline and ponasterone A as described above . Eighteen hours later , the induction medium was removed and fresh culture medium containing Env-pseudotyped virus was gently added to the cells . The infection plates were spinoculated [66] at 2 , 000 rpm for 2 hours at 37°C , and incubated for an additional 48 hours at 37°C . Infection medium was then removed and the cells harvested , and luciferase activity was assayed using the Luciferase assay system ( Promega ) . Env-pseudotyped luciferase reporter viruses were initially titered on 293-Affinofile cells expressing the highest induction levels for CD4 ( 0 . 1 µg/ml tetracycline ) and CCR5 ( 2 µM ponA ) surface expression . The amount of pseudotyped virus used in the single-cycle MDM infection assay was normalized to 1×107 relative light units for infection at the highest 293-Affinofile drug levels tested . All pseudotyped viruses were used within the linear range of the assay , and all infection conditions for MDM were assayed in duplicate wells . Env-pseudotyped virus was gently added to MDM in culture and the infection plates were spinoculated at 1 , 200×g for 2 hours at 37°C [66] . Unattached virus was removed from the MDM cultures by removing the medium gently without disturbing the cells . The MDM were then washed once with warm PBS supplemented with 1% fetal bovine serum to remove any residual virus , and once with warm RPMI 1640 medium supplemented with 10% fetal bovine serum and 100 µg/ml of penicillin and streptomycin to remove any remaining PBS/FBS . After the second wash , RPMI 1640 medium supplemented with 10% FBS , 100 µg/ml of penicillin and streptomycin , and 10 ng/ml M-CSF was added , and the cells were cultured for 5 days at 37°C . The culture medium was then removed and the cells were harvested and luciferase activity was assayed using the Luciferase assay system ( Promega ) . The HIV-1 env nucleotide sequences determined in this study have been deposited in GenBank under accession numbers JN562755-JN563605 .
|
Human immunodeficiency virus type 1 ( HIV-1 ) infection of the central nervous system ( CNS ) can lead to the development of a severe neurological disease termed HIV-1-associated dementia ( HAD ) . Individuals diagnosed with HAD commonly have genetically distinct HIV-1 variants in their cerebrospinal fluid ( CSF ) that are not detected in the blood virus population , suggesting that independent viral replication is occurring in the CNS of HIV-1-infected subjects with severe neurological disease . We examined HIV-1 variants in the blood plasma and CSF of HAD subjects to determine the viral characteristics associated with the development of dementia during HIV-1 infection . We found that genetically distinct HIV-1 variants in the CSF of HAD subjects were either R5 T cell-tropic or macrophage-tropic . The R5 T cell-tropic viruses required high levels of the cellular surface receptor CD4 to enter cells , while macrophage-tropic viruses could enter cells with low levels of CD4 , suggesting that HIV-1 can replicate in at least two cell types within the CNS during the course of dementia . Finally , macrophage-tropic viruses were detected in the CSF but poorly represented in the blood virus population . Our results suggest that HIV-1 variants in the CSF can provide information about independent viral replication in the CNS during the course of HIV-1 infection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunodeficiency",
"viruses",
"viral",
"evolution",
"viral",
"entry",
"viral",
"transmission",
"and",
"infection",
"virology",
"neurovirulence",
"biology",
"microbiology"
] |
2011
|
HIV-1 Replication in the Central Nervous System Occurs in Two Distinct Cell Types
|
Clinical relevance of nontuberculous mycobacteria ( NTM ) is increasing worldwide including in Saudi Arabia . A high species diversity of NTM’s has been noticed in a recent study . However , the identification in diagnostic laboratories is mostly limited to common species . The impact of NTM species diversity on clinical outcome is so far neglected in most of the clinical settings . During April 2014 to September 2015 , a nationwide collection of suspected NTM clinical isolates with clinical and demographical data were carried out . Primary identification was performed by commercial line probe assays . Isolates identified up to Mycobacterium species level by line probe assays only were included and subjected to sequencing of 16S rRNA , rpoB , hsp65 and 16S-23S ITS region genes . The sequence data were subjected to BLAST analysis in GenBank and Ez-Taxon databases . Male Saudi nationals were dominated in the study population and falling majorly into the 46–59 years age group . Pulmonary cases were 59 . 3% with a surprising clinical relevance of 75% based on American Thoracic Society guidelines . Among the 40 . 7% extra-pulmonary cases , 50% of them were skin infections . The identification revealed 16 species and all of them are reporting for the first time in Saudi Arabia . The major species obtained were Mycobacterium monascence ( 18 . 5% ) , M . cosmeticum ( 11 . 1% ) , M . kubicae ( 11 . 1% ) , M . duvalli ( 7 . 4% ) , M . terrae ( 7 . 4% ) and M . triplex ( 7 . 4% ) . This is the first report on clinical relevance of M . kubicae , M . tusciae , M . yongonense , M . arupense and M . iranicum causing pulmonary disease and M . monascence , M . duvalli , M . perigrinum , M . insubricum , M . holsaticum and M . kyorinense causing various extra-pulmonary diseases in Saudi Arabia . Ascites caused by M . monascence and cecum infection by M . holsaticum were the rarest incidents . To the first time in the country , clinical significance of various rare NTM’s are well explored and the finding warrants a new threat to the Saudi Arabian clinical settings .
In the last decade , the prevalence of pulmonary and extra-pulmonary diseases caused by nontuberculous mycobacteria ( NTM ) has been increased [1–7] . This elevation in case rates , whether it is a real emergence or due to the development of advanced diagnostic tools is still unclear . On the other hand , the elevation in immunosuppressive conditions including infectious or non-infectious diseases and therapies contribute considerably in this phenomenon . To date , more than 140 species of NTM’s have been described from different sources with varying pathogenicity and almost 50 species were identified in the last 8 years alone [8] . However , in literature only a small number of reports are available about the new species as their role in clinical microbiology is largely undetermined . Mostly , the species defined as “rare” will remain unrecognized or misidentified due to the lack of proper resources , lack of knowledge or ignorance [8] . The clinical characteristics of diseases caused by the rare or new NTM’s are still not fully established . The advancement in technologies such as genome sequencing , line probe assays , high performance liquid chromatography ( HPLC ) and matrix assisted laser desorption ionization time-of-flight ( MALDI-TOF ) to identify the NTM species increased the detection of rare and new NTM species . However , accessibility to such tools in resource poor settings is a major concern for timely identification . Thus , the species level identification is mostly neglected regardless of its importance in clinical outcome . Following the global trend of NTM prevalence , Saudi Arabia also reports with an increasing numbers of NTM diseases [7 , 9] . In 2013 , Varghese et al . reported in the first national level study a highly diverse population of clinically relevant NTM’s with the potential of causing pulmonary and extra-pulmonary diseases [9] . Interestingly , a new species of pathogenic mycobacteria also has been identified from the country named M . riyadhense [10] . However , the diagnosis of NTM’s is mostly limited to the common species only in majority of the laboratories , because of the lack of infrastructure . Therefore , there is no data available on the existence of rare NTM species in the country so far . To explore the diversity of rare NTM species with clinical relevance in the Saudi Arabian clinical setting , a prospective analysis on a nationwide isolate collection has been designed . Sequencing of 16S rRNA , rpoB , hsp65 and 16S-23S ITS region genes were carried out to identify the species . Clinical significance of pulmonary isolates in the study was determined by applying the criteria based on American Thoracic Society ( ATS ) guidelines for NTM pulmonary diseases [11] . The species diversity and clinical significance of each isolates have been evaluated .
This study has been conducted as part of the first national NTM surveillance survey of Saudi Arabia . The duration of collection was 18 months , from April 2014 to September 2015 . All the suspected NTM isolates from different mycobacterial diagnostic laboratories were collected and transferred to the Mycobacteriology Research Section of King Faisal Specialist Hospital and Research Centre , Riyadh . The demographical and clinical data were collected by using standard data collection forms without keeping any patients identifiers . Pulmonary cases were defined as clinically relevant based on ATS guidelines [11] . Briefly , a minimum of two positive cultures from separate sputum samples or at least one positive culture from bronchial wash , lavage or one positive culture from trans-bronchial or other lung biopsy showing mycobacterial- histopathological features were considered as clinically relevant to define NTM pulmonary disease . Isolates were maintained on Lowenstein Jensen slants and modified Middle Brook 7H9 medium ( Becton Dickinson , USA ) . The genomic DNA was extracted by using the QIAamp DNA Mini kit ( Qiagen , Germany ) . The primary screening to identify the NTM’s was carried out by commercially available line probe assay kit- Genotype MTBC ( Hain Life science , Germany ) . The non-MTBC isolates were initially identified with Genotype Mycobacterium CM kit ( Hain Life science , Nehren , Germany ) and unidentified isolates were further tested with Genotype Mycobacterium AS kit ( Hain Lifescience , Nehren Germany ) . Isolates which were detected by the Genotype Mycobacterium AS assay up to genus level ( Mycobacterium species ) only were included in the study as “unidentified” species . This study has been reviewed and approved by the Office of Research Affairs in King Faisal Specialist Hospital and Research Centre , Riyadh , Saudi Arabia . Sequencing assay was carried out by using the BigDye Terminator cycle sequencing chemistry kits ( Applied Biosystems , CA , USA ) in DNA analyzer 3730 ( Applied Biosystems , CA , USA ) . The first attempt of identification was based on a 645-655bp hyper variable region of the 16S rRNA and a 342bp region of rpoB genes based on previously standardized protocol [12 , 13] . Isolates which could not be identified by 16S rRNA and rpoB gene sequencing were subjected to further sequencing of highly conservative regions of two more genes hsp65 ( 439bp ) and 16S-23S ITS region ( 480bp ) according to previously validated primers [14–16] . The line probe assay test strips were scanned with Genoscan ( Hain Lifescience , Germany ) and the results were interpreted with the Blotrix software ( Hain Lifescience , Germany ) . The sequence base calling and assembly were carried out by using Sequence Analysis software v5 . 3 . 1 ( Applied Biosystems , USA ) and Lasergene core suite 12 ( DNA STAR , WI , USA ) respectively . Assembled sequences were subjected to BLAST analysis in NCBI GenBank and EzTaxon ( http://www . ezbiocloud . net/identify; 16S rRNA Based Database ) online data bases [17] . A stringent similarity index of ≥99–100% was kept with the type strain in GenBank and EzTaxon . Statistical data analysis was carried out by using SPSS V20 . 0 software package ( IBM , USA ) .
During the study period , 510 suspected NTM clinical isolates were collected and subjected to line probe assay identification . Of the total , 27 isolates met the inclusion criteria and enrolled for further analysis . Demographical summary of the study subjects showed 22 ( 81 . 5% ) of the enrolled cases were Saudi nationals with a male ( 77 . 8% ) gender domination . Interestingly , the age group of the study subjects showed a predominance of 46–59 years ( 48 . 2% ) . Seven cases out of 27 had a previous history of tuberculosis and 3 were reactive to HIV antigens . The major comorbidities noticed among the study subjects were rheumatoid arthritis ( 18 . 5% ) , malignancies ( 18 . 5% ) and diabetes ( 14 . 8% ) . Considerable percentage of Chronic Obstructive Pulmonary Disease ( COPD ) , asthma and bronchiectasis also were observed ( Table 1 ) . Analysis of 16S rRNA , rpoB , hsp65 and 16-23S ITS region genes , showed an extreme diversity of NTM species distributed to 16 species . The majorly detected species were M . monascence ( 18 . 5% ) , M . cosmeticum ( 11 . 1% ) , M . kubicae ( 11 . 1% ) , M . duvalii ( 7 . 4% ) , M . triplex ( 7 . 4% ) and M . terrae ( 7 . 4% ) . Rest of the 10 species were reported with one case each ( 3 . 7% ) ( Fig 1 ) . The major site of infection observed in the study was pulmonary ( 59 . 3% ) . Clinical relevance of pulmonary isolates based on the ATS guidelines was dominating ( 75% ) . Of the 16 pulmonary cases , four cases did not qualify the clinical relevance guidelines and thus considered as colonization . Clinically relevant diseases were caused by M . arupense , M . cosmeticum , M . iranicum , M . kubicae , M . monascence , M . novocatrense , M . tusciae and M . yongonense ( Table 1 ) . On the other hand , extra-pulmonary involvement was found with 11 ( 40 . 7% ) cases . Among extra-pulmonary cases , skin ( 45 . 5% ) was the most affected site of infection followed by lymphnode ( 27 . 3% ) . M . insubricum , M . perigrinum and M . marinum were found exclusively causing granuloma or sepsis . Interestingly , all these five skin infections were observed among non-Saudi patients . Cecum infection by M . holsaticum and ascites caused by M . monascence were the rarest incidents in this study ( Table 1 ) .
For the first time in Saudi Arabia , the existence of rare NTM species has been explored on a nationwide collection of clinical isolates . The findings showed a strong presence of clinically relevant NTM rare species in the Saudi Arabian clinical settings . The species diversity of rare NTM’s causing both pulmonary and extra-pulmonary diseases was huge ( 16 species ) . To date , all of these 16 species with clinical relevance are reporting for the first time in the country . Demographical analysis showed predominance of male Saudi nationals and mainly the age group 46–59 years and a similar domination had been noticed in a recent nationwide study of NTM prevalence [9] . Clinical data showed various comorbidities existed among the study group . Rheumatoid arthritis and various malignancies were the major problems followed by diabetes . There were no studies so far analyzed the reasons behind the predominance of male Saudi nationals towards the NTM disease . Perhaps , there were some speculations like higher rate of consanguinity existed in the community which leads to several genetic susceptibility diseases , increasing rate of immunosuppressive therapies and various malignancies in the geographical region [18–20] . Indeed , the confounding factors for this predominance need further detailed scientific exploration . In this study , pulmonary diseases caused by rare NTM species were predominant with higher clinical relevance ( 75% ) . Of the 16 identified species , 11 species except M . insubricum , M . kyorinense , M . holsaticum , M . perigrinum and M . marinum were isolated from respiratory samples . Of the 11 species , except M . duvalii and M . terrae all the others reported with clinically relevant diseases . Moreover , most of these species are isolated very rarely from clinical specimens with relevance so far around the world [21] . Interestingly , to date , M . tusciae , M . yongonense , M . novocastrens and M . monascence pulmonary diseases were reported in hardly 2–3 publications . Therefore , these findings are important as it shows and confirms the growing problems with rare NTM species in Saudi Arabia as well for the first time in the Gulf Cooperation Council ( GCC ) states also [22] [21 , 23 , 24] . The higher clinical relevance established after consulting the ATS guidelines shows the increasing potential of NTM respiratory diseases in the country . Supportively , NTM respiratory diseases caused by various species have been observed in a recent nationwide study in the country [9] . In the current study majority of the isolation was from sputum samples , except six bronchial washes . The frequency of isolation from sputum was peaked up to 7 occasions for a case of M . cosmeticum . The higher frequency shows the increasing potential of NTM’s as en establishing pathogen in the clinical settings . Most of the NTM species isolated in the current study causing pulmonary diseases are not only rare in Saudi Arabia but also around the globe . In concordance to the current findings , previous global studies showed an increasing prevalence of NTM’s and particularly the domination of pulmonary isolates . The existence of numerous rare or new species was observed in most of the large level studies [5–7 , 21] . The increased clinical relevance is a warranting key to be vigilant on the pathogenicity and potential of NTM’s to cause confirmed diseases rather than colonization . In the current study , 11 cases of extra-pulmonary diseases were observed with a predominance of skin infections ( 50% ) caused by M . cosmeticum , M . insubricum , M . perigrinum , M . marinum and M . terrae . The M . perigrinum and M . marinum were isolated from two immigrant patients from the Western coast of the country , where the major fishing harbors located with considerable number of foreign fishermen . The skin infection caused by M . marinum and M . perigrinum among people who work in fisheries or swimming pool maintenance is generally reported elsewhere [25 , 26] . The rarest cases in the study were the ascites caused by M . monascence and cecum infection caused by M . holsaticum . To our knowledge , this might be the first cases of the same reporting globally . On the other hand , lymphadenitis caused by M . kyorinense and M . triplex are rare manifestations which have been reported in only two or three cases so far globally and the current study also found a case of each [27 , 28] . The infections caused by M . duvalii and M . monascence in patients undergoing peritoneal dialysis are reported for the first time in Saudi Arabia and in GCC nations . Such cases are rarely reported around the world [8] .
The following 16S rRNA gene sequences have been deposited in GenBank/DDBJ/EMBL data bases . M . monacense ( KY287007 ) , M . iranicum ( KY287008 ) , M . kubicae ( KY287009 ) , M . cosmeticum ( KY287010 ) , M . duvalii ( KY287011 ) , M . terrae ( KY287012 ) , M . arupense ( KY287013 ) and M . novocastrense ( KY287014 ) .
|
Nontuberculous mycobacteria ( NTM ) are ubiquitous in nature and they are opportunistic pathogens . In the last decade , infections caused by NTM’s increased—around the world in immune-suppressed and immune-competent individuals and Saudi Arabia is not an exception . Developments in diagnostic technologies increased the identification of several new or rare species of NTM’s . Indeed , the species diversity of NTM has a direct impact on clinical outcome and therapies . Saudi Arabian clinics so far report only the common species of NTM’s and rare species are mostly neglected due to the lack of proper infrastructure or ignorance . To the first time in the country , an exploration on the existence of clinically relevant rare NTM species was conducted on a nationwide level . The findings showed a huge diversity of rare NTM species causing both pulmonary and extra-pulmonary diseases . Clinical relevance of pulmonary infection based on American Thoracic Society guidelines was confirmed as an aggressive 75% , which is really alarming . Interestingly , 16 species of NTM’s were isolated in the study , and all of them are reporting for the first time in country . Overall finding shows Saudi Arabia faces serious threat from rare NTM species with high clinical significance and warrants the immediate need for more advanced infrastructure .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion",
"Conclusion"
] |
[
"dermatology",
"ecology",
"and",
"environmental",
"sciences",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"geographical",
"locations",
"microbiology",
"pulmonology",
"chronic",
"obstructive",
"pulmonary",
"disease",
"saudi",
"arabia",
"skin",
"infections",
"cellular",
"structures",
"and",
"organelles",
"bacteria",
"bacterial",
"pathogens",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"ecological",
"metrics",
"infectious",
"diseases",
"mycobacteria",
"bioinformatics",
"medical",
"microbiology",
"microbial",
"pathogens",
"biological",
"databases",
"actinobacteria",
"species",
"diversity",
"ribosomes",
"people",
"and",
"places",
"biochemistry",
"rna",
"nontuberculous",
"mycobacteria",
"sequence",
"databases",
"ribosomal",
"rna",
"asia",
"nucleic",
"acids",
"cell",
"biology",
"ecology",
"database",
"and",
"informatics",
"methods",
"biology",
"and",
"life",
"sciences",
"non-coding",
"rna",
"organisms"
] |
2017
|
Emergence of Rare Species of Nontuberculous Mycobacteria as Potential Pathogens in Saudi Arabian Clinical Setting
|
Escherichia coli serves as an excellent model for the study of fundamental cellular processes such as metabolism , signalling and gene expression . Understanding the function and organization of proteins within these processes is an important step towards a ‘systems’ view of E . coli . Integrating experimental and computational interaction data , we present a reliable network of 3 , 989 functional interactions between 1 , 941 E . coli proteins ( ∼45% of its proteome ) . These were combined with a recently generated set of 3 , 888 high-quality physical interactions between 918 proteins and clustered to reveal 316 discrete modules . In addition to known protein complexes ( e . g . , RNA and DNA polymerases ) , we identified modules that represent biochemical pathways ( e . g . , nitrate regulation and cell wall biosynthesis ) as well as batteries of functionally and evolutionarily related processes . To aid the interpretation of modular relationships , several case examples are presented , including both well characterized and novel biochemical systems . Together these data provide a global view of the modular organization of the E . coli proteome and yield unique insights into structural and evolutionary relationships in bacterial networks .
Escherichia coli is the leading model bacterium . Due to its ease of culture and genetic manipulation , it has proven extremely useful for the study of basic biological processes including signalling , metabolism and gene expression [1] , [2] . Furthermore , E . coli serves as a major model for the study of bacterial pathogenesis [3] . In consequence , a considerable body of knowledge has been collated for E . coli . First sequenced in 1996 , over half of its genes have now been experimentally characterized [2] , [4] . In addition , through decades of painstaking biochemical studies a variety of metabolic , signalling and regulatory pathways have been assembled [2] , [5] , [6] . However , despite the impressive nature of the available data , details of the organization and co-ordination of proteins within and across cellular processes in E . coli is still far from complete , precluding a global ‘systems’ view of the E . coli proteome . To date a variety of methods have been developed and systematically applied to derive large scale networks of protein-protein interactions ( PPIs ) for a variety of organisms . These range from the experimental: e . g . co-immunoprecipitation ( co-IP ) , yeast-two-hybrid ( Y2H ) screens and tandem affinity purification ( TAP ) coupled with mass spectrometry [7]–[12]; to the theoretical: e . g . genome context methods and co-expression data [13]–[15] . Exploiting the topological properties of these networks , clustering algorithms have subsequently allowed proteins to be organized into functional modules such as protein complexes or signalling pathways [11] , [16] . Integration of additional datasets such as comparative and functional genomics data are further providing insights into how these modules and their components are co-ordinated or how they may have evolved [11] , [17] . For example , clustering of phylogenetic profiles in the context of metabolic networks have identified evolutionary conserved functional entities [18] . While a number of genome scale protein-protein interaction ( PPI ) datasets have been generated for yeast [7] , [8] , [10] , [11] , [19] , [20] , similar datasets for E . coli are more modest . These include two datasets of physical interactions generated through TAP [9] , [21] and several datasets of functional interactions derived through genome context methods , gene co-expression analyses and literature surveys [13]–[15] , [22] . Note that throughout , we use the term functional interactions , to represent proteins that may be involved in a common biological process but do not necessarily physically interact . A recurring challenge in the analysis of PPI datasets has been the discrimination of physiologically meaningful interactions ( true positives ) from those that arise as methodological artefacts ( false positives ) [23] , [24] . To address this challenge integrative methods , such as the use of Bayesian classifiers , have been applied to identify those interactions which are more reliable [20] , [25] , [26] . More recently three large scale PPI datasets have been generated for E . coli based mainly on genome context methods [15] , [27] , [28] . While these datasets provide extensive coverage , such coverage may compromise the quality of interactions . Here we build on these previous studies by integrating several experimental and computational interaction datasets to reconstruct an extensive network of functional interactions for E coli with an equivalent accuracy to that obtained for small scale ( e . g . co-IP ) experiments . We combine this set of functional interactions with a recently generated set of physical interactions generated through reciprocal TAP [27] to yield a single global network of over 7 , 600 high quality protein interactions representing over half of the proteins in E . coli . Through the application of a graph clustering algorithm we systematically organize these data into discrete functional modules to provide , to the best of our knowledge , the first large scale view of the modular organization of a bacterial ( as opposed to eukaryotic ) proteome . Due to the fundamental role of E . coli in basic and biomedical research , the findings presented in this study are expected to find significant and wide scale impact .
Adopting a Bayesian framework , we constructed a high quality network of protein interactions for E . coli through the integration of interaction data from seven sets of computational predictions and three sets of experimentally verified interactions that include both large scale pull down and small scale assays ( Fig . 1A and Table S1 ) . Each dataset was assigned a log likelihood score ( LLS ) calculated from its performance relative to a gold standard set of functional annotations ( see methods ) . Here we used EcoCyc [5] functional categories . Datasets with higher frequencies of interacting proteins that share a common functional category are assigned higher LLS's ( indicating a higher confidence dataset ) . Other gold standard sets of functional annotations based on Clusters of Orthologous Genes ( COGs ) [29]; the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) [30]; and Gene Ontology ( GO ) [31] terms were found to give comparable results ( data not shown ) . Based on an analysis of dataset overlap ( see Text S1 - Generation of the functional network and Fig . S1 ) , we merged two highly redundant datasets and adopted a weighted sum scheme [32] to avoid potential biases due to data dependencies ( Fig . S1E ) . Integration of the datasets resulted in the scoring of 58 , 844 non-redundant functional linkages involving 4 , 149 ( ∼97% ) E . coli proteins ( Fig . 1B ) . Small-scale assays represent the most accurate datasets and were used to define a score cut-off for including interactions within our final dataset . The final high confidence network ( hereafter referred to as the ‘functional’ network ) contains 3 , 989 non-redundant linkages for 1 , 941 E . coli genes ( ∼45% of the E . coli proteome – Table S2 ) . To assess the performance of the network reconstruction , we adopted a five-fold cross-validation scheme to predict membership of COG functional categories , using a label propagation method [33] . Comparisons of these predictions with previously assigned COG functional annotations revealed relatively high values of precision - TP/ ( TP+FP ) - and recall – TP/ ( TP+FN ) - ( Fig . 1C ) . Of 19 functional categories , 15 had a precision in excess of 0 . 5 and 11 had a recall in excess of 0 . 5 . Interactions involving proteins involved in cell motility ( COG category N ) demonstrated the best performance in terms of precision and recall ( 0 . 97 and 0 . 96 respectively ) . While interactions involving proteins involved in transcription ( COG categories K ) had among the lowest values ( 0 . 36 and 0 . 28 respectively ) reflecting the tendency of these proteins to interact with and mediate a diverse range of cellular functions . Consistent with similar studies [27] , [28] , [32] , we make the assumption that links between proteins from the same functional group are correct , while those that occur between different functional groups are incorrect . This assumption is supported by the large frequency of interactions derived from small scale assays , involving proteins annotated with the same COG or EcoCyc functional categories ( Fig . S1F ) . Applying the same cross-validation approach , we found that our functional network significantly out performs three previously published networks of E . coli functional interactions [15] , [27] , [28] ( Fig . S2 ) . Compared to these other datasets , our functional network demonstrated improved recall across all COG categories . Furthermore , the functional network provides the highest values of precision for eight of 19 COG categories and provides the next best value of precision for an additional eight categories . Finally , based on area under the receiver operating curve AUROC values , our functional network out performs the other datasets in 10 of 19 COG categories . For more discussion of how this network improves over previous analyses see Text S1 - Comparisons with other datasets . Recently a large scale PPI network consisting of 3 , 888 interactions derived for 918 proteins was generated for E . coli based on TAP [27] ( Table S2 ) . Since genome context methods were used to validate these interactions , it was not appropriate to include them as an additional dataset in our integration exercise . Instead , due to the reported high quality of these data we simply merged the ‘Hu et al . TAP’ dataset with our functional network to create a single ‘combined’ network of 7 , 613 interactions between 2 , 283 proteins . Graph analyses of all three networks ( functional , Hu et al . TAP and combined ) reveal the typical scale free properties associated with biological networks ( Fig . S3 ) . Comparisons of global topological metrics show how the significance cut-off impacts network node degree and shortest path lengths in the E . coli functional network . However , even accounting for differences in node degree , functional networks display higher eccentricities and betweenness values than their experimental counterparts , derived through the use of TAP ( Fig . 1D ) . This is likely related to the tendency of TAP to identify interactions between common members of complexes that may not directly interact . While the Hu et al . TAP and functional datasets share 557 proteins , only 241 ( 6% ) of their interactions were common . When we consider indirect interactions within the functional network , we find that the overlap increases to 502 ( 13% ) and 966 ( 25% ) for path distances of two and three respectively – significantly greater than for randomly generated networks ( Fig . 1E ) . This increase in overlap between the datasets arises as a consequence of the TAP approach identifying proteins through indirect interactions . Indeed , when we take into account these indirect interactions , we note that the overlap between the functional and Hu et al . TAP datasets is relatively high compared to previous analyses comparing the overlap between different interaction datasets [34] , [35] . The availability of a large network of well annotated genes facilitates the study of the topological properties both within and between different COG functional categories ( Fig . S4 ) . For example , proteins from COG category J ( translation , ribosomal structure and biogenesis ) and L ( replication , recombination and repair ) tend to be highly connected ( high node degree ) , perhaps reflecting their tendency to occur in complexes , and central to the global network ( high betweenness values ) indicating their fundamental role to E . coli . On the other hand , while proteins from COG category N ( cell motility ) tend to be highly connected , they have low betweenness values but strikingly , high node and mutual clustering coefficients . This suggests that these proteins form highly integrated systems that operate in relative isolation to the rest of the network ( e . g . flagella see below ) . Analysis of topological relationships between different COG categories ( Figs . 1F & S5 ) are similarly revealing of functional relationships . For example , proteins from in COG categories D , M , O and U ( Cell cycle control , cell division , chromosome partitioning; Cell wall/membrane/envelope/biogenesis; posttranslational modification , protein turnover and chaperones; and intracellular trafficking , secretion and vesicular transport respectively ) , all share high numbers of connections . This may reflect the need to tightly coordinate these processes for purposes of cell growth and division . Conversely , proteins from COG categories E , G and P ( amino acid , carbohydrate and ion transport and metabolism respectively ) are not highly connected and are also more distant ( high shortest path lengths ) to other COG categories , suggesting that these processes operate as functionally distinct modules within the global network . An emerging paradigm from the analysis of protein interaction networks is the tendency for protein activity to be coordinated through distinct functional modules . Applying the Markov cluster algorithm ( MCL ) [36] to the combined network , we identified 316 modules composed of three or more proteins ( together with 243 two component clusters and 33 singletons – Fig . 2A and Table S3 ) . 209 ( 66% ) of the predicted modules ( containing three or more proteins ) possessed a high proportion ( > = 50% ) of common COG functional annotations ( Fig . 2B and Table S3 ) and hence likely correspond with known functional modules such as protein complexes and biochemical pathways ( see next section ) . Conversely we identified three modules that could be defined as novel . Finally 16 modules were composed of proteins with non-overlapping COG categories ( ignoring the uninformative COG categories R , S or - ) . The heterogeneous nature of these modules , suggest that they may represent novel linking modules interconnecting different functional processes . Compared to the functional network , modules derived from the Hu et al . TAP network were more functionally heterogeneous , with only 39% of the predicted modules ( containing three or more proteins ) possessed a high proportion ( > = 50% ) of common COG functional annotations ( Figs . 2B , S6 & S7 and Table S3 ) . These differences are further exemplified by the higher proportion of inter-module∶intra-module interactions observed in the Hu et al . TAP network ( 2 , 329∶845 for the Hu et al . TAP network; 1 , 247∶2 , 107 for the functional network ) . This may reflect the tendency for TAP derived PPI data to include indirect interactions . From Fig . 2A we note the presence of a highly interconnected core of modules comprised predominantly of proteins derived from the Hu et al . TAP dataset . For the most part these are also linked through experimentally derived interactions . Modules derived mainly from the functional network are either isolated or tend to group into smaller discrete clusters of functionally related modules . Noteworthy , the networks presented here were functionally more homogeneous than a set of modules previously predicted from a network of functional interactions generated by Hu and colleagues [27] ( Fig . 2B ) . The heterogeneous nature of this latter dataset reflects the high proportion of inter-module∶intra-module interactions ( 36 , 640∶19 , 043 ) that likely impact the resolution of the modules ( see Text S1 - Comparisons with other datasets ) . Within the E . coli proteome , 1293 ( ∼30% ) proteins have either not been assigned a COG functional category or assigned an uninformative category ( S - ‘function unknown’ or R - ‘general function prediction only’ ) . The organization of proteins into functional modules provides a valuable resource for further studies aimed at elucidating the functions of these poorly characterized proteins . For example from Fig . 3C below , we might infer that the uncharacterized protein yehB ( annotated as a putative outer membrane protein ) is involved in pili assembly . Interestingly , initial studies inferring functional annotations on the basis of common annotations within defined modules was found to be more accurate than one based solely on direct neighbour interactions ( Fig . S6C and Text S1 - Prediction of functional annotations for unknown genes ) . While modules generated solely from physical interaction data are known to represent protein complexes [11] , [19] , those derived from functional interaction data may have other biological interpretations . Within the E . coli interactome , as well as known protein complexes ( e . g . the 30S and 50S ribosomal subunits , RNA and DNA polymerases ) , we identified modules that represented biochemical pathways ( e . g . nitrate regulation and cell wall biosynthesis ) as well as batteries of functionally and evolutionary related processes . To illustrate the types of relationships that are associated with modules generated from mainly functional interaction data , we present several case examples of modules representing both well characterized and novel biochemical systems ( Figs . 3 & 4 ) . In these detailed views , interactions with different levels of confidence are presented . In general we find that proteins with interactions of lowest confidence scores are indicative of a general functional association ( i . e . the protein forms part of the complex/pathway but its precise role is ambiguous ) . On the other hand , interactions with higher confidence scores may reflect closer functional relationships that can serve as a focus for more detailed investigation . The availability of a large scale interaction map for E . coli provides a valuable resource for exploring the evolution of protein interaction networks in bacteria . Consistent with previous studies in yeast and a smaller network derived for E . coli [9] , [11] , essential and/or highly conserved proteins are more highly connected and occupy more central roles within the combined network compared to non-essential and poorly conserved proteins ( Fig . S8 ) . Proteins from large gene families ( >10 members ) were also more highly connected and centric to the network ( Fig . S9A ) . However , it should be noted that the number of connections associated with members of large gene families may be inflated due to the fact that they often possess similar phylogenetic profiles ( one of the features used for generating the functional network ) . Nonetheless we also note that for each network , from 25–35% of genes from the same gene family have a shortest path length of two , indicating a common interactor . Together these findings highlight the role of conserved and essential proteins in coordinating cellular processes and support a model of preferential attachment in which duplicated proteins tend to interact with the partner of their paralog [54]–[57] . A unique facet of bacterial evolution is their ability to readily acquire new genes through lateral gene transfer events ( LGT ) . Of 359 previously identified LGT genes [58] , only 130 ( 36% ) were identified within the combined network . On the whole proteins derived from these genes were poorly connected and found at the periphery of the network ( Fig . S9B ) . For example , of the 130 LGT derived proteins in the network , 63 ( 48% ) had a betweenness value of 0 , i . e . they are connected to only one other protein , compared to 673 out of 2 , 173 ( 31% ) for non-LGT proteins ( χ2 = 17 . 26 , p<0 . 0001 , Chi-Square test ) . Similar results were obtained for both the Hu et al . TAP and the functional networks . These results suggests that LGT derived proteins largely contribute to PPI network evolution through the addition of peripheral functions , perhaps in response to changed environments [59] . For example , the peripheral ( betweenness = 0 ) protein gadX is a regulator of two isoforms of glutamate decarboxylase which operate in several amino acid metabolic pathways . The predicted origin of gadX in E . coli through LGT suggests a recently acquired role in the network linking pH sensing with differential expression of these decarboxylases which are known to play a major role in acid resistance [60] . It is worth noting that the finding that LGT genes tend to occupy the periphery of networks , highlights a novel property that could be exploited for improving LGT detection methods . Comparing shortest path lengths , we found a subset of LGT proteins that associate with other LGT proteins , although this appears to be a property of the functionally derived interactions rather than the Hu et al . TAP derived interactions ( Fig . S9B ) . We identified a series of seven LGT specific subnetworks consisting of three or more interconnected LGT proteins with an additional nine other pairs of interacting LGT proteins ( Fig . 5 ) . In many cases , LGT genes associated with the same subnetwork were found in close genomic proximity , possessed similar phylogenetic profiles and were also organized within the same functional module ( Figs . 4B and 5A ) , suggesting a mode of lateral evolution in which functional units may be co-inherited through discrete transfer events . For example , fourteen genes involved in phosphonate uptake and transport are organized into a single operon [61] and grouped into four functional modules , including module 62 which contains phnGHIJM . To examine the role of LGT in modular organization , we present two detailed examples involving hydrogenase and iron transport systems respectively . The E . coli genome encodes four hydrogenases ( Hyd1-4 ) : Hyd1 and Hyd2 are isoenzymes involved in hydrogen uptake , while Hyd3 and Hyd4 perform the reverse reaction , although the physiological role of Hyd4 is not clear [62] . Hyd3 is encoded by hycBCDEFGHI , of which hycBCDEG are predicted to derive through LGT . Only three proteins associated with Hyd3 - hycEFG - are present within the combined network ( Fig . 5C ) . Together these are organized in a single operon and form part of module 31 along with components of Hyd4 and NADH∶ubiquinone oxidoreductase , reflecting common sequence similarity relationships between the three systems [63] . Linking Hyd3 subunits to components of Hyd1 and 2 , are a variety of proteins required for the maturation of the active hydrogenase enzymes , including hypBCDEF and slyD [62] . The emerging picture suggests that the putative acquisition of many components of Hyd3 via LGT and their integration into the network as a functional entity was facilitated by the presence of existing maturation proteins which were originally associated with Hyd1 and 2 . Enterobactin is a siderophore , produced by E . coli which is secreted by E coli and used to sequester and import iron and has been implicated in host invasion [64] . Evolution of metabolic pathways often involves the use of pre-existing metabolic precursors ( note for example the links between enzymes from other amino acid pathways with those involved in tryptophan biosynthesis Fig . 5D ) . The synthesis of enterobactin requires chorismate produced by the enzyme trpD as a precursor and involves five enzymes: entABCEF . Of these , the first four are putative LGT genes organized in a single operon along with three of the four subunits ( fepB , fepD , fepG ) of the ABC transporter used to import ferric-enterobactin in a tonB-dependent process . Given a source of chorismate , the acquisition of these genes as a discrete functional module , provides the host bacterium with the ability to synthesise , secrete and import enterobactin . As an interesting aside , related genes in the pathway: entD , entF , fepA , fepC and fes , are also located in the same genomic proximity but were not predicted to have derived from LGTs . Finally it is worth noting the presence of two additional ABC-based iron transport systems within this subnetwork: fhuABCDE and fecABCD , responsible for the uptake of iron via hydroxamate and dicitrate respectively . While the fhu-based transporter appears native to E . coli , the fec-based system is another LGT acquired system . The interactions between the permease subunits fecCD and fepGD reflects their close evolutionary relationships and highlights the need for most bacteria to evolve and maintain a diverse battery of iron uptake systems as they attempt to compete with other microbial organisms for this relatively limited resource [65] . Here we have combined a novel functional network with a recently generated experimental network to provide a global view of the modular organization of proteins in E . coli . The identification of functionally coherent modules , their interactions and the emergence of ‘neighbourhoods’ of interconnected modules represent a major step towards a deeper understanding of how biological processes are organized and operate . In an attempt to understand how the network may have arisen , we examined the role of gene family expansions and lateral gene transfer events on the generation of the network . From these analyses , we propose an amended model of network evolution ( Fig . 6 ) based on preferential attachment as previously suggested [55] . In this new model , we suggest that the bacterial network gains interactions either through the duplication of existing genes , or through the acquisition of novel genes from LGT events . From the preferential attachment model and consistent with our analysis of gene family relationships we note that gene duplication events result in preferential growth at the core of the network . On the other hand , perhaps due to their potential to disrupt essential interactions that are enriched in the core of the network , the acquisition of new interactions through LGT events occurs mainly at the network periphery . Instead , the evolution of the network through LGT events at the network periphery might be associated with contingency genes allowing the bacteria to adapt to new ecological niches . It should be noted that the LGT derived proteins used in this study were detected mainly by their composition properties and may therefore be biased towards more recent transfers [58] . It cannot therefore be discounted that proteins derived through older LGT events that are less easily recognized , may have become integrated into the network , potentially developing into core components of the network . Previous studies of PPI networks , have shown that many functional modules tend to be conserved over evolution [66] , [67] . More recently , studies of protein complexes evolution suggest that protein complexes form early in evolution and evolve as coherent units [68] and that duplication of self-interacting proteins play a key role in their formation [69] . Here we expand on these ideas by suggesting that at least in bacteria , LGT events resulting in the simultaneous acquisition of several functionally related genes may also contribute to the formation of a modular network structure . To our knowledge , this network represents the most comprehensive and accurate gene network reconstruction in E . coli that not only provide insights into the evolution and organization of bacterial protein interaction networks , but may be usefully exploited to help understand the molecular basis of pathogenesis . Furthermore , the identification of groups of proteins organized into discrete functional modules will assist the design and construction of artificial biological systems and hence provide a valuable contribution to the emerging field of synthetic biology [70] , [71] . To allow researchers to freely download explore these datasets a publicly available web tool has been developed - http://www . compsysbio . org/bacteriome/ [72] . Finally , it is important to note that the network presented here represents only 45% of the E . coli proteome . While the coverage of the network will improve as additional datasets become available , we would nonetheless encourage researchers , interested in genes not contained within this dataset , to explore the other previously published datasets outlined in this paper . Links to these datasets are also provided on our project website .
Datasets to derive the functional network included seven computational ( C ) and three experimental ( E ) datasets: Phylogenetic profiles ( C ) [14]; Rosetta stone ( C ) [14]; Gene neighbourhood ( C ) [14]; Gene clusters ( C ) [14]; Literature curated ( C ) [22]; Interologs of H . pylori ( C ) [12] , [73]; Conserved coexpression ( C ) [13]; Large scale TAP [9] ( E ) and Small scale assays ( E ) from DIP [74] , [75]; and Large scale pull down ( E ) [21] . Numbers of proteins and interactions associated with each dataset are presented in Table S1 , along with a breakdown of the experimental methods used to derive the small scale assay dataset . Each dataset assumed a bait∶prey ( “spoke” ) model of interaction ( as opposed to a “matrix” model , in which each component of a complex is assumed interact with all other components of the complex ) . Due to the high level of overlap between the Gene neighbourhood and Gene clusters datasets , they were combined into a single set of interactions termed Gene proximity . In order to test the correct assignment of functional linkages we used four different benchmark sets: EcoCyC [5] , the Clusters of Orthologous Group ( COG ) [29] , the Kyoto-based KEGG [76] , and the Gene Ontology ( GO ) annotation database [31] . Our analyses also incorporated a recently generated large scale TAP-derived network ( designated Hu et al . TAP ) containing 3 , 888 interactions between 918 proteins [77] . This dataset also assumes a spoke model of interaction . Due to the high quality and coverage of this data set which has also been subjected to validation through a similar data integration process , it was not included in the generation of the functional network . Instead the two networks ( Hu et al . TAP and functional ) were merged into a single combined network featuring 7 , 613 interactions between 2 , 283 proteins . Lists of essential and non-essential proteins were derived from Zhang and co-workers [78] . For further details on these datasets see Text S1 - Methods . To derive a high quality dataset of functional interactions , information from the seven computational and three experimentally derived datasets were integrated within a Bayesian framework . The scoring scheme used in this study derives from Bayesian statistics and is similar to that used by Lee and co-workers [32] , in which each input data set , either experimentally or computationally derived , adds some evidence that a pair of genes are functionally linked . Each experimental and computational data set is evaluated for its ability to reconstruct known pathways by measuring log likelihood scores ( LLS ) representing the likelihood that a pair of genes are functionally linked . P ( L|E ) /∼P ( L|E ) represents the posterior odds ratio , where P ( L|E ) represents the frequency of interactions ( L ) in a dataset ( E ) between proteins participating in the same functional category ( as defined by EcoCyc ) ; ∼P ( L|E ) represents the frequency of L in E participating in different functional categories . P ( L ) /∼P ( L ) represents the prior odds ratio , where P ( L ) represents the frequency of interactions between all E . coli proteins participating in the same functional category; and ∼P ( L ) represents the frequency of interactions between all E . coli proteins participating in different functional categories . Higher values of LLS indicate more confident interactions associated with the dataset . To derive a score associated with a functional interaction , we integrate the LLS's from each dataset in which that interaction is found . To examine potential biases that may arise from data dependencies we applied a weighted sum scoring scheme [32] to derive a final score S associated with each interaction:where LLSi represents the LLS for the functional interaction from dataset i ( ordered by descending magnitude of the n log likelihood scores for the given interaction ) ; D is a free parameter representing the relative degree of dependency between various datasets; and n is the number of datasets containing the interaction . Here we examined values of D from 1 to ∞ and found that D = 1 gave the best performance in terms of accuracy ( LLS ) and coverage , suggesting that the datasets were independent ( Figure S1E ) . Hence in this study , the final score for a functional interaction was simply derived from the sum of LLS's of all datasets in which the interaction was found . Finally a cut-off based on the LLS derived from the small scale experimental dataset was used to define a high confidence set of functional interactions ( Fig . 1B ) . To assess functional interactions we used a previously published label propagation method using a threshold cut-off > = 0 . 5 [33] with five-fold cross-validation based on COG category assignments . We derived values for precision ( true positives/ ( true positives+false positives ) ) , recall ( true positives/ ( true positives+false negatives ) ) and area under receiver operator characteristic curve ( AUROC ) from 100 replicate samplings . Network statistics were derived using in house perl scripts and the two software packages: Pajek ( http://vlado . fmf . uni-lj . si/pub/networks/pajek/ ) and tYNA ( http://tyna . gersteinlab . org/ ) . The global network view was generated using an in-house Java based applet . Other network views were generated using Cytoscape ( http://www . cytoscape . org/ ) . The genome ideogram was generated using the Circos software ( http://mkweb . bcgsc . ca/circos/ ? home ) . Functional modules were predicted using the Markov clustering algorithm [36] , testing several inflation parameters and using values that provided the best overlap of the computed clusters with COG functional categories ( Fig . S6 ) . Note the average % overlap in COG categories across modules derived from the combined network was found to only vary between ∼56–59% . Hence , even without optimization , there is a high proportion of clusters with common COG terms . For each E . coli sequence , a BLASTP [79] search was performed against each of 199 different organism genome data sets derived from the COGENT database [80] ( Table S5 ) . Homologs for each protein were determined based on a raw bit score threshold of 50 . These homologs were used to generate the phylogenetic profiles presented in Fig . 5A . For additional conservation analyses , two sets of conservation were defined . The first consists of three categories: conserved ( homologs in more than 100 genomes - 932 proteins in the combined network ) ; medium conserved ( homologs in at least 25 genomes – 539 proteins ) ; and non conserved ( homologs in less than 25 genomes – 553 proteins ) . The second set consists of eight categories: E . coli specific ( no detectable homologs outside E . coli– 21 proteins in the combined network ) ; gammaproteobacterial specific ( no detectable homologs outside gammaproteobacteria – 121 proteins ) ; proteobacterial specific ( no detectable homologs outside proteobacteria – 129 proteins ) ; and proteins with detectable homologs to 1–5 different groups of prokaryotes ( Cyanobacteria , Spirochaetes , Firmicutes/Actinobacteria , ‘Other bacterial groups’ and Archaea – Table S5 ) as defined by the NCBI taxonomy resource ( http://www . ncbi . nlm . nih . gov/Taxonomy/taxonomyhome . html/ ) – 110 , 186 , 249 , 433 and 822 proteins associated with 1 , 2 , 3 , 4 and 5 groups respectively . We considered a functional interaction to be preserved in a genome if both interacting proteins have detectable homologues . Expanded descriptions of network generation and analyses are provided in the supplementary files - Text S1 , Figures S1 , S2 , S3 , S4 , S5 , S6 , S7 , S8 , and S9 , and Tables S1 , S2 , S3 , S4 , S5 , and S6 .
|
Genes and their protein products do not operate in isolation , but form components of highly interconnected biological systems . Identifying the connections between components is therefore critical to understanding how these processes are organized and operate . E . coli is the leading model bacterium; however despite its importance in biological and medical discovery , an accurate atlas of these interactions is still lacking . On the other hand , several computational and experimental procedures have been applied on a high-throughput basis to provide collections of interaction data of varying quality and coverage . Using a sophisticated mathematical framework , we have combined and benchmarked these data to create a single , highly reliable set of interactions that encompasses almost 50% of the E . coli proteome . Organizing these data on the basis of their interactions , we identify groups of proteins representing functionally coordinated modules such as molecular machines ( e . g . , the flagellum ) and biochemical pathways . Finally through examining the organization of E . coli interactions in the context of evolution , we propose a new model of bacterial network evolution that accounts for the integration of foreign genes acquired through horizontal gene transfer mechanisms .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Methods"
] |
[
"microbiology/microbial",
"evolution",
"and",
"genomics",
"computational",
"biology/systems",
"biology",
"computational",
"biology/genomics"
] |
2009
|
The Modular Organization of Protein Interactions in Escherichia coli
|
ChIP sequencing ( ChIP-seq ) is a new method for genomewide mapping of protein binding sites on DNA . It has generated much excitement in functional genomics . To score data and determine adequate sequencing depth , both the genomic background and the binding sites must be properly modeled . To develop a computational foundation to tackle these issues , we first performed a study to characterize the observed statistical nature of this new type of high-throughput data . By linking sequence tags into clusters , we show that there are two components to the distribution of tag counts observed in a number of recent experiments: an initial power-law distribution and a subsequent long right tail . Then we develop in silico ChIP-seq , a computational method to simulate the experimental outcome by placing tags onto the genome according to particular assumed distributions for the actual binding sites and for the background genomic sequence . In contrast to current assumptions , our results show that both the background and the binding sites need to have a markedly nonuniform distribution in order to correctly model the observed ChIP-seq data , with , for instance , the background tag counts modeled by a gamma distribution . On the basis of these results , we extend an existing scoring approach by using a more realistic genomic-background model . This enables us to identify transcription-factor binding sites in ChIP-seq data in a statistically rigorous fashion .
Gene expression is carefully regulated in all living cells . Only a fraction of the genes in a genome are expressed to various degrees under a given condition or in a particular cell type . The main control of such regulation occurs at the transcription level: the RNA polymerases transcribe genes following binding of trans-acting transcription factors to cis-acting regulatory DNA sequences within genes or in their vicinities . To determine the biological functions of transcription factors , it is imperative to identify their binding sites and target genes in the genome . Currently the most commonly used high-throughput method for identifying transcription factor binding sites ( TFBSs ) is chromatin immunoprecipitation followed by microarray hybridization ( ChIP-chip ) [1]–[3] . In this method , the transcription factors are cross-linked to DNA under the test condition . After the genomic DNA is isolated and fragmented by sonication , an antibody specific to the transcription factor of interest is used to isolate the transcription factor and the DNA fragments which it binds . Following chromatin immunoprecipitation , the protein–DNA crosslink is reversed and the DNA fragments are hybridized to a tiling microarray . After the signal quantification , the DNA fragments enriched by the binding of the transcription factor are identified—in terms of both genomic sequence and location—by the oligonucleotide tiles that give significantly high relative signals on the microarray [4] . Instead of using microarrays to identify the sequences of the immunoprecipitated DNA fragments , new methods have recently been developed to take advantage of the fast-maturing next-generation massively parallel sequencing technologies . In one such method , ChIP-PET [5] , paired-end ditags ( PETs ) derived from both ends of the immunoprecipitated DNA fragments are sequenced and mapped to the genome . In a newer method , ChIP-seq [6] , [7] , immunoprecipitated DNA fragments are directly sequenced at one end for ∼30 bp , and the short sequence reads are then mapped to the reference genome . The apt combination of ChIP and next-generation sequencing technology has generated much excitement in the field of functional genomics . Comparing with ChIP-chip , whose usability for large mammalian genomes is limited by serious cross-hybridization at high genomic resolution , these sequencing-based methods offer not only direct whole-genome coverage but also low analytical complexity , high signal-to-noise ratio , and sensitivity that increases with sequencing depth . The current trend in high-throughput molecular biology laboratories is to migrate from ChIP-chip to ChIP sequencing to identify transcription factor binding sites in vivo . Proper computational modeling of ChIP-seq process is needed for both data scoring and determination of adequate sequencing depth , as it provides the computational foundation for analyzing ChIP-seq data . Here we show the characteristics of ChIP-seq data and present in silico ChIP sequencing , a computational method to simulate the experimental outcome . Our simulation results reveal that both the genomic background and the binding sites are not uniform . Such nonuniformity in the background will have important implications in ChIP-seq data analysis and binding sites identification .
ChIP-seq data are generated in a straight-forward manner , by high-throughput sequencing and subsequent sequence alignment . Because Illumina/Solexa 1G Genome Analyzer generates a very large number of short sequence reads , ChIP sequencing is currently done mainly with this sequencing platform . This could change in the future , however , as other high-throughput sequencing technologies may become better suited . Here we briefly describe the procedure of ChIP sequencing with the Solexa platform . The immunoprecipitated DNA fragments are sequenced from one end for approximately 30 bp . These short sequence reads are aligned to the human reference genome , and only uniquely mapped reads ( typically 60–80% of all sequence reads ) are retained for the downstream analysis . Based on size selection after gel electrophoresis prior to sequencing , the retained reads are elongated into longer tags by directional extension to the mean length of the size selected DNA fragments and then transformed into profiles of the number of overlapped DNA fragments at each nucleotide in the reference genome [6] . For our analysis , we link overlapping tags into tag clusters ( Figure 1 ) , each of which is characterized by y , the number of tags it contains , and indexed by a and b , its start and end genomic locations . Thus , by definition a tag cluster is a genomic site continuously covered by one or more sequence tags can be characterized in two different ways . One type of characterization is to set a and b to the boundaries of the cluster and y to the number of all tags in it , while the other is to identify the peak of the overlap in the cluster first and then to set a and b to the start and the end positions of the peak and y to the height of the cluster . We term tag clusters characterized by these two methods as ‘outer clusters’ and ‘inner clusters’ respectively and use ‘outer clusters’ in our analysis . Suppose there are M tag clusters , ChIP-seq data after preprocessing are defined by the matrix T , whose row m , ( am , bm , ym ) , characterizes tag cluster m ( m = 1 , … , M ) . The main goal of our ChIP-seq data analysis is to identify tag clusters that are transcription factor binding sites by determining a threshold on the tag count to separate the DNA-binding signals from the background noise . To identify transcription factor binding sites in ChIP-seq data , we assess the statistical significance of each tag cluster found in the actual data by assigning it a P-value as the result of the test of the null hypothesis that its tag count is generated by a null distribution , which is the distribution of the tag count on the genomic background alone . This null distribution is generated by placement of sequence reads onto the genomic background in the absence of binding sites . It is critical to simulate the correct background , as the null distribution generated from it is used to assign P-values to all actual tag clusters . The simulation starts with the removal of sequence gaps and repeats from the genomic region—the entire genome or a part of it—under consideration . It is followed by the random placement of n sequence tags , corresponding to the same number of uniquely-mapped sequence reads from the experiment , onto the genomic background , whose distribution of the sampling weight on the nucleotide level could be either uniform or non-uniform . After the tag placement , suppose that N tag clusters are identified in the simulated data and the largest one contains C tags , thus the null distribution of the cluster tag count is given by the number of tag clusters on each tag count level , 1 , 2 , … , C: . Given this null distribution , for tag cluster m ( m = 1 , 2 , … , M ) identified in the experimental data we calculate its associated P-value , Pm , for the test of the null hypothesis that it is part of the background asin which ym is the tag count of tag cluster m from the experimental data and kc is the number of tag clusters on tag count level c in the simulated data . In essence this is a permutation test and Pm can be calculated to arbitrary accuracy as the number of simulation increases . To control the type I error in this set of M hypothesis tests , we first adjust the P-values so that they directly reflect the controlled false discovery rates [8] , and then choose the lowest tag count that gives a low FDR ( e . g . , less then 0 . 05 ) as the threshold . Tag clusters with at least this tag count are identified as the binding sites . For our simulation of ChIP sequencing ( Figure 2 ) , we use the lengths of human chromosomes as specified in the NCBI v36/hg18 human genome assembly . We first remove all sequence gaps as defined in the UCSC genome browser annotation database . Because only uniquely mapped sequence reads are used in ChIP-seq data analysis , we also remove positions covered by repetitive sequences identified by RepeatMaster , and then randomly place without overlap a chosen number of transcription factor binding sites , each of which was assumed 500 bp long , onto the genome . After the placement of binding sites , the genome ( excluding removed sequence gaps and repeats ) is effectively partitioned into the floating fixed foreground ( binding sites ) and the background . The process of the chromosomal immunoprecipitation and the subsequent unique mapping and extension of sequence reads can be simulated by randomly placing uniquely mapped sequence tags onto the chromosome , according to certain sampling weight at each nucleotide position . Such weights are generated first for the background nucleotide positions and then for those in the binding sites . For a uniform background , every nucleotide position in the background is given one as its sampling weight . For a varying background , if we assign each nucleotide position a different weight , given the large size of the human genome it becomes computationally prohibitive to sample the background many times as the simulation requires . Instead , we partition the background into adjacent blocks of nucleotide positions . After testing different block sizes ranging from 500 bp to 5 kb , we find they all give practically identical simulation results . In the end , we choose 1 kb as the block size . Every adjacent 1-kb block in the background is given a random weight drawn from a pre-specified underlying distribution and all nucleotide positions in a block are assigned the same weight . For the background variation , we assume that most of the background has a low sampling weight as most of the background is not enriched in the immunoprecipitation ( the working principle of ChIP ) but a few places of it have relatively high weights , comparable to some binding sites . Based on this assumption , we use a gamma distribution , Gamma ( s , c ) , which skews to the right , for the distribution of sampling weight on the background . To specify the sampling weights in the binding sites , we first calculate w̅b , the average sampling weight at each nucleotide position in the background , and multiply it by the enrichment coefficient t , to obtain w̅f = t⋅w̅b , the average sampling weight at each nucleotide position in the binding sites . The ChIP enrichment at different binding sites is , however , different and can be estimated by the fold increase of tags placed in the foreground over those placed in the background in the simulation . Given w̅f and the number of nucleotides in the binding sites , we calculate Wf , the total amount of sampling weight in the binding sites , and then distribute it to each binding site either evenly or varyingly according to a certain distribution . For the intersite variation , we use a power-law distribution generated by a “preferential attachment” procedure . If a tag is placed in a binding site , the current sampling weight of this site , wk , is updated by a linear function as wk = w+r·k·w , in which w is its initial sampling weight , k is the number of tags placed at this site , and r is the weight increase coefficient . For each binding site , we also distribute the amount of its sampling weight to each nucleotide position according to a symmetric binomial or an equilateral triangular profile . We test various combinations of values for s , c , and r , the two free parameters in our simulation method , and find s = 1 , c = 20 , and r = 1 . 5 produce simulated data that give overall best fit to the actual data . We implemented our ChIP-seq simulation method in R and wrote several auxiliary programs for text processing in Perl . The whole software package with source code and documentation is available for download at http://www . gersteinlab . org/proj/chip-seq-simu .
For our analysis and simulation of ChIP-seq data , we used the dataset generated from STAT1 DNA binding under IFN-γ stimulation by Robertson et al . [6] . Of the initial 2 , 915 , 382 sequence reads obtained in their experiment , 2 , 025 , 931 ( 69 . 5% ) could be uniquely mapped to the unmasked NCBI v36/hg18 human reference genome . After the genomic mapping , we extended the length of mapped sequence reads from 27 to 174 bp , the estimated average length of the size selected DNA fragments [6] , and identified 1 , 264 , 752 STAT1 tag clusters on the whole genome level . While the majority ( 1 , 149 , 405 , >90% ) of these tag clusters comprise only one or two tags , a relatively small number ( 661 ) of them contain large numbers of tags ( 50 and more , the outer-overlapping count ) and consequently show high stacking peaks ( the inner-overlapping count ) in their profiles ( Figure 3A ) . For example , the most prominent STAT1 tag cluster appears immediately upstream to the centromere of chromosome 1 . With a peak height of 472 tags , it comprises 1 , 733 tags in its ∼1 . 6 Kb genomic footprint . Indeed , a closer examination revealed that the tag count c follows a power-law distribution:where the degree exponent γ = 2 . 97 ( R2 = 0 . 9955 , P-value<2×10−16 ) for the outer count and 3 . 44 ( R2 = 0 . 9976 , P-value<2×10−16 ) for the inner count , respectively ( Figure 3A and 3B ) . We also examined tag counts on individual human chromosomes separately to check for possible discrepancies in their distributions on different chromosomes . The plots in Figure 3C and 3D show that over all the tag count on individual chromosomes and on the genome as a whole follows the same power-law distribution , and there is considerable variation among different chromosomes in the distribution at high counts . In our simulation of the ChIP-seq process , we use either uniform or varying sampling weights on the genomic background and among the binding sites for the tag placement . The four simulated datasets generated from the resultant combinations of the background and the inter-site distributions fit the actual data in very distinct ways ( Figure 4 ) . The goodness of fit is assessed by the fit of the simulated distribution to the actual one in the range of small to high tag counts . The four combinations of the background and the inter-site distributions can be seen as a gradual increment in the overall simulation complexity: from a simple model that assumes uniformity in both the background and the binding sites to one that assumes variation in either of them and to the most complex one that assumes variation in both of them . The simplest model assumes that the tag placement is identical everywhere on the background and also identical among the binding sites . Data generated from this model give a distribution of tag counts that is a very poor fit to the actual one: not only is there a depletion of tag clusters with small to medium tag counts due to an excess of single tags being placed onto the genome , but also clusters with large tag counts are completely absent ( Figure 4A and see the Table S1 for the quantification of the goodness of fit ) . The slightly more complicated second model assumes identical binding sites but a varying background for tag placement . The simulated data fit the actual distribution well at small to medium ( 1 to ∼5 ) tag counts but there is still a complete absence of clusters with large tag counts ( Figure 4B ) . Contrary to the second model , the third model assumes a uniform background but varying binding sites for tag placement instead . Using this model we see an inversion in the simulation result: tag clusters with small to medium tag counts are depleted in the simulated data while clusters with large tag counts are generated ( Figure 4C ) . Finally , we use a model that assumes variation both in background and among binding sites for tag placement . It generates data that give the best fit to the actual distribution of the tag count in its whole range ( Figure 4D ) . To identify STAT1 binding sites , we can assess the statistical significance of each tag cluster found in the actual data using a null distribution of tag counts derived from a background model . For the initial assessment , we used a simple background model that assumes equal probabilities for random tag placement at every available nucleotide position in the genome and combined 500 independent replicates of background simulation to generate such a null distribution . After assigning P-values and adjusting them for multiple testing to control the false discovery rate , we set five and above , which corresponds to an FDR<0 . 05 , as the threshold on the tag count and identify 32 , 763 STAT1 binding sites . In light of the simulation results , we can reassess the statistical significance of each tag cluster found in the actual data by using the varying-background model and combining 500 independent replicates of background simulation to generate the null distribution of the tag count . As before , we assign P-values to tag clusters found in the actual data by using this null distribution and adjust them for multiple testing to control the false discovery rate . At the same FDR level ( <0 . 05 ) , we set thirteen and above as the threshold on the tag count and identified 5 , 858 STAT1 binding sites from the initial ∼3-million sequence reads . Using the full sets of reads , we identified 28 , 434 and 5 , 307 STAT1 binding sites with and without IFN-γ stimulation respectively ( Table S2 ) . In their study , Robertson et al found 41 , 582 and 11 , 004 sites in these two datasets . The reduction in both of our numbers reflects a more stringent threshold for peak calling , which was set by the more realistic varying-background model . Moreover , the proportionally greater decrease in the number of sites without stimulation reflects the limitation of STAT1 as a transcription factor without IFN-γ stimulation . To demonstrate the validity of the threshold change , we performed a STAT1 motif analysis in the peaks that are between the thresholds set by the uniform background and the varying background models . Using Meta-MEME [9] with blocksize = 128 , 205 characters , background = peaks . bg ( nucleotide frequencies estimated from the input peak sequences ) , and E-value<1 , we are able to identify significant STAT1 motifs ( as defined in TRANSFAC [10] and JASPAR [11] ) in 6 . 1% of those peaks . This result suggests that the threshold increase greatly boosts the specificity at a very small expense of the sensitivity . Four distributions of tag counts are plotted in Figure 5: two actual distributions generated by experiments with and without IFN-γ stimulation and two null distributions derived from the uniform- and the varying-background models . Compared with either null , there is a significant increase of the number of tag clusters with high tag counts in the observed stimulated distribution . For example , there are 661 tag clusters with 50 or more tag counts in the actual data but none in the simulated data generated with either background model . While the number of tag clusters strictly decreases monotonously as the tag counts increases in the null distribution , there is a long tail on the right of the actual distribution given by the enrichment of clusters with high tag counts . Moreover , we also observe significant differences between the simulated datasets generated with two background models alone . First , comparing with 903 , 832 tag singletons in the actual data , there is an enrichment of tag singletons in all simulated background datasets . However , this increase is much more pronounced in the datasets generated with the uniform-background model ( ∼150% ) than with the varying-background model ( ∼115% ) . Second , on average there are only three tag clusters with nine or more tag counts in the data simulated with the uniform-background model but over 2 , 000 with the varying-background model . To check how closely the varying-background model models the background in the actual experimental data , we compared the distribution generated under this model with the actual one without the IFN-γ stimulation . In response to the stimulation , STAT1 binds to numerous promoter elements to upregulate interferon stimulated genes . Without the stimulation , the role of STAT1 as a transcription factor is limited . Given such a difference in the DNA binding of STAT1 in the presence or absence of IFN-γ , we expect the distribution of tag counts from the experiment without stimulation should be a distribution dominated by a significant background with a small right tail from its limited DNA binding . This is exactly what we see in Figure 5 , where the good fit between the distribution simulated under the varying-background model and the actual unstimulated one is striking and shows the validity of the varying-background model . Considering these observations ( Figure 5 ) in the light of the full simulation results presented in the previous subsection ( Figure 4 ) , we conclude that as the genomic background is varying it is better captured by the varying model than the uniform one .
We generate synthetic ChIP-seq datasets under simulation models with various assumptions for the binding sites and the genomic background . By comparing the simulated dataset with the actual one , we assess the goodness of the assumptions made in each simulation and thus can gain insight into the actual ChIP-seq data generating process: the closer the simulated dataset is to the actual one , the closer the assumptions are to the real process . We use the uniform and the varying models for both the background and the binding sites in our simulation . In Figure 5 , marginal comparisons show that the model with a varying ( non-uniform ) weight distribution for either the background or the binding sites generates substantially better simulated data . When the background and the binding sites are considered together , the simulated datasets generated with various combinations of the background and the binding-site models show striking differences in their quality . The data simulated with the uniform-weight models used for both the background and the binding sites show practically no fitting to the actual data except for the general trend ( Figure 5A ) . When the varying-weight model is used for either the background or the binding sites , there are substantial improvements to the fit in different ranges of the tag count ( Figure 5B and 5C ) . However , when the varying-weight models are used for both the background and the binding sites , not only is the fit the best but also there is a general agreement between the simulated and the actual data ( Figure 5D ) . These simulation results clearly show that neither the binding sites nor the background is uniformly presented in ChIP-seq data . Due to the inherent random noise in the experiment , binding sites are unlikely to contain the same number of mapped sequence tags . Not all the variance in the number of sequence tags mapped to binding sites could be explained by random noise , which should be counted by the uniform-site model as the simulation itself is intrinsically a stochastic process . Because DNA segments containing the binding sites are enriched by immunoprecipitation , the variance should also reflect the different DNA-binding affinity that a transcription factor has for its binding sites . Such variation could be the result of differences in either the nucleotide sequences of the binding sites [12] or the local chromotin modification status [13] . Perhaps more importantly , our simulation results also reveal that there is a substantial variation in the tag placement on the genomic background . Obviously , such background variation cannot be explained by the uniformity of background currently assumed in ChIP sequencing . Instead , our results suggest a varying background that is mildly fluctuating and contains some “hot” spots with relatively high ChIP enrichment comparable to some binding sites . The presence of such background ‘hot’ spots in the ChIP-seq data may be caused by preferential sequencing particular to the sequencing protocol/platform used in the experiment . Their enrichment through immunoprecipitation is precluded , however , as the background DNA segments are not bound by the transcription factor . Our inference of a varying genomic background not only raises questions about both biology and technology involved in ChIP sequencing but also has important practical implications to the analysis of ChIP-seq data as it provides a better background model ( see next subsection for explanation ) . To examine our simulation results more closely , we plot in Figure 6 the actual tag count distribution and the simulated ones generated under different background and site models with the enrichment coefficient t = 10 only ( the blue lines in Figure 5A–D ) because as seen in Figure 5D at this enrichment level the simulated data give the best fit to the actual ones . Based on the fitting of different simulated distributions to the actual one , the range of the tag count in the actual data can be divided into four sections with low , medium , high , and ultrahigh tag counts respectively . As marked by the dashed circles and lines in Figure 6 , the three section boundaries are defined by the divergence of the simulated distribution based on the varying-background and uniform-site model from the actual distribution ( the green and the black lines ) , the convergence of the simulated distribution based on the uniform-background and varying-site model from the actual distribution ( the purple and the black lines ) , and the divergence of the simulated distribution based on the varying-background and varying-site model from the actual distribution ( the orange and the black lines ) . Based on the models used to generate these simulated distributions , we can also infer the genomic identities of tag clusters found in the actual data . Tag clusters with low and high ( including ultrahigh ) tag counts are almost certain to be background and binding sites , respectively . Because there is a mixture of signals , the true identities of the clusters with medium tag counts are much less certain , and thus some form of thresholding is necessary . Figure 6 also shows that the part of the tag count that has a power-law distribution is supported by the background or the binding sites or both at low , high , and medium counts respectively . The right tail , diverged from the power-law distribution ( Figure 4 ) , occupies the ultra-high count section . Reported in two recent studies [6] , [7] , ChIP sequencing is a newly-developed high-throughput method for genome-wide mapping of in vivo protein–DNA association . In these two studies , two different analytical methods were used to identify transcription factor binding sites . In the first study [7] , a list of sites ‘known’ to be bound ( the positives ) and unbound ( the negatives ) by the transcription factor being studied is first compiled . Given this ‘gold standard’ , the sensitivity and the specificity of the experiment at each threshold on the sequence read per region are then calculated . And finally a threshold is chosen to give both high sensitivity and high specificity . In the second study [6] , a background model is first used to simulate the sequence read placement unto the genome in the absence of binding sites . The false discovery rate , defined as the ratio of the number of peaks at and above a peak height threshold in the simulated data to that at and above the same threshold in the actual data , is then calculated at each peak height as the threshold . And finally a threshold on the peak height is chosen to give a stringent FDR . For easy reference in our later discussion , we name the former the “known-sites” method and the latter the “background-simulation” method . The known-sites method has the advantage in giving the sensitivity and the specificity of a particular ChIP-seq experiment at a chosen threshold . Its applicability is , however , problematic since it requires a “gold standard , ” a list of true positives and true negatives . Conceptually , the validity of such a ‘gold standard’ is questionable given the dynamic nature of protein–DNA association—i . e . , under different conditions a transcription factor has different DNA-binding profiles . Operationally , this method is also difficult to use . The prerequisite functional “gold standard” is rarely available , let alone a good one . Moreover , the “known” positives are biased towards binding sites with high enrichment of sequence tags , and as the majority of the genome is not bound by a transcription factor ever , it is an open question how many “true negatives” should be included in the calculation . That is , given the huge preponderance of negatives , it is very difficult to build a correctly balanced gold standard , which is essential for training an effective classifier [14] . Instead of using a “gold standard” to identify binding sites in ChIP-seq data , the background-simulation method uses a background model to simulate how sequence reads are distributed in a genome in the absence of binding sites . Since this method does not assume any prior knowledge about the binding sites of the transcription factor under investigation , it avoids major difficulties encountered by the known-sites method . In their study , Robertson et al used a background model that implicitly assumes uniform tag placement everywhere on the background . However , our simulation results show that the data generated by this uniform-background model agree poorly with the actual experimental data . Based on our further analysis , we can generate a better null distribution by using a more realistic , varying-background model that assumes most of the background is not enriched but at a few places it has a high enrichment level on a par with some binding sites . In our analysis we estimated the background and the foreground together from the ChIP-seq sample data alone . However , if the negative control data from the experiments without immunoprecipitation are available , the estimation of the background becomes simpler as such experiments give a direct empirical estimate of the ChIP-seq background . Because our method can simulate the background alone , the negative control data can thus be easily accommodated . First the control data are used to estimate the parameters of the varying background model . The fitted model is then used to generate the null distribution of the tag count . And finally this null distribution is used to score the ChIP-seq data . We also make improvement to the usage of the null distribution in the background-simulation method . In the study of Robertson et al , the false discovery rate is defined as the ratio of the number of peaks at and above a threshold in the simulated data to that at and above the same threshold in the actual data . The implicit assumption behind this definition is that the peaks identified in the simulated data are false positives and the number of them is equal to the number of false positives in the actual data . The first half of this assumption is reasonable , but the second half is unwarranted . For direct comparability , the same number of uniquely mapped sequence tags as contained in the actual data is used to simulate the null distribution on the background . Due to the finiteness of this number and the presence of binding sites ( the true positives ) in the actual data , the number of the peaks identified in the simulated data will be greater than the number of false positives in the actual data at any threshold . This discrepancy is more pronounced at lower thresholds . In fact , at low thresholds there could be more peaks in the simulated data than in the actual data . When this happens , the false discovery rate exceeds one , which is nonsensical . Instead of using the null distribution in such an ad hoc manner , we use it to assign each tag cluster found in the actual data a P-value to assess its statistical significance . We then adjust the P-values of the multiple-hypothesis tests to control the false discovery rate .
|
ChIP-seq is an apt combination of chromosome immunoprecipitation and next-generation sequencing to identify transcription factor binding sites in vivo on the whole-genome scale . Since its advent , this new method has generated much excitement in the field of functional genomics . Proper computational modeling of the ChIP-seq process is needed for both data scoring and determination of adequate sequencing depth , as it provides the computational foundation for analyzing ChIP-seq data . In our study , we show the characteristics of ChIP-seq data and present in silico ChIP sequencing , a computational method to simulate the experimental outcome . On the basis of our data characterization , we observed transcription factor binding sites with excessive enrichment of sequence tags . Our simulation results reveal that both the genomic background and the binding sites are not uniform . On the basis of our simulation results , we propose a statistical procedure using the more realistic genomic background model to identify binding sites in ChIP-seq data .
|
[
"Abstract",
"Introduction",
"Model",
"Results",
"Discussion"
] |
[
"computational",
"biology/genomics"
] |
2008
|
Modeling ChIP Sequencing In Silico with Applications
|
Biofilms are surface-adhered bacterial communities encased in an extracellular matrix composed of DNA , bacterial polysaccharides and proteins , which are up to 1000-fold more antibiotic resistant than planktonic cultures . To date , extracellular DNA has been shown to function as a structural support to maintain Pseudomonas aeruginosa biofilm architecture . Here we show that DNA is a multifaceted component of P . aeruginosa biofilms . At physiologically relevant concentrations , extracellular DNA has antimicrobial activity , causing cell lysis by chelating cations that stabilize lipopolysaccharide ( LPS ) and the outer membrane ( OM ) . DNA-mediated killing occurred within minutes , as a result of perturbation of both the outer and inner membrane ( IM ) and the release of cytoplasmic contents , including genomic DNA . Sub-inhibitory concentrations of DNA created a cation-limited environment that resulted in induction of the PhoPQ- and PmrAB-regulated cationic antimicrobial peptide resistance operon PA3552–PA3559 in P . aeruginosa . Furthermore , DNA-induced expression of this operon resulted in up to 2560-fold increased resistance to cationic antimicrobial peptides and 640-fold increased resistance to aminoglycosides , but had no effect on β-lactam and fluoroquinolone resistance . Thus , the presence of extracellular DNA in the biofilm matrix contributes to cation gradients , genomic DNA release and inducible antibiotic resistance . DNA-rich environments , including biofilms and other infection sites like the CF lung , are likely the in vivo environments where extracellular pathogens such as P . aeruginosa encounter cation limitation .
Pseudomonas aeruginosa is an opportunistic pathogen capable of causing both acute and chronic infections . It is the third-leading cause of nosocomial infections and is the predominant pathogen associated with morbidity and mortality of CF patients [1] , [2] . The biofilm-forming ability of P . aeruginosa , and indeed other bacteria , is thought to contribute to their ability to thrive in hostile host environments and result in chronic infection [3] , [4] . Biofilms are multicellular surface-associated microbial communities encased in an extracellular matrix which display a characteristic structure and increased resistance to antimicrobial compounds and environmental stresses . P . aeruginosa biofilms are up to 1000-fold more antibiotic tolerant than planktonic cells , to single and combination antibiotics [5]–[7] . As acute CF exacerbations caused by P . aeruginosa are often treated with combination antibiotic therapy [8]–[10] , the increased resistance of biofilms to combination antibiotics is of direct clinical relevance . Eighty five percent of P . aeruginosa strains isolated from the lungs of CF patients with advanced stages of disease have a distinctive mucoid colony morphology [11] . This mucoid phenotype is a result of overproduction of the alginate exopolysaccharide ( EPS ) [1] , [12] . Alginate production has been shown to inhibit phagocytic killing of Pseudomonas , to protect from antibiotic exposure [13] , [14] , and is associated with poor prognosis for the infected patients [15] , [16] . The direct observation of P . aeruginosa microcolonies encased in an alginate matrix in microscopy studies of CF bronchial samples [17] , along with a large body of additional in vitro and in vivo data [7] , [18]–[21] suggests that P . aeruginosa forms biofilms in the lungs of CF patients . The mechanisms of biofilm-associated antibiotic resistance are distinct from the well studied intrinsic resistance mechanisms such as drug efflux , drug inactivation , membrane permeability and target site alterations . Although the basis of biofilm-associated antibiotic resistance is not fully understood , it is likely that multiple mechanisms operate simultaneously in biofilms to contribute to antibiotic resistance . Cells in a biofilm may be protected from antibiotic exposure due to the restricted penetration of antibiotics through the biofilm matrix [19] . However , while the biofilm matrix may limit diffusion initially for certain antibiotics such as β-lactams and aminoglycosides [14] , [22] , the penetration of fluoroquinolones occurs immediately and without delay [23]–[25] . The rate of diffusion through the matrix is presumably dependent on binding of the antibiotic molecules to the EPS matrix . Once the matrix becomes saturated , diffusion and antimicrobial activity of the drug will resume [26] . It is the general consensus that reduced diffusion through the biofilm matrix only provides a short-term protective effect and does not play a significant role during long-term antibiotic exposure [26] . Other resistance mechanisms include the presence of subpopulations of multidrug tolerant persister cells [27]–[29] , drug indifference of slow-growing , nutrient-limited cells [30] , and unique resistance mechanisms specifically associated with biofilms [31] , [32] . Despite the fact that biofilms are recognized as the predominant mode of bacterial growth in nature and are responsible for the majority of refractory bacterial infections [19] , little is known regarding the mechanisms of biofilm-specific antibiotic resistance . Furthering our understanding of the mechanisms underlying biofilm-associated antibiotic resistance will significantly improve the treatment options available to patients with chronic bacterial infections . Signal transduction systems have been documented to be involved in the regulation of biofilm formation in multiple bacterial species including P . aeruginosa , S . aureus , E . coli and V . fischeri [33]–[38] . These two component systems ( TCS ) are comprised of an membrane-anchored histidine kinase sensor and a cytoplasmic response regulator . After detecting specific environmental signals , a signal transduction cascade is initiated that results in phosphorylation of the response regulator , which activates or represses the necessary target genes . A number of regulatory systems that influence biofilm formation have been described . These include , but are not limited to , the global virulence factor regulator GacA , mutation of which results in a 10-fold decrease in biofilm formation and failure to form microcolony structures [33] . Additionally , the hybrid sensor kinases , LadS and RetS appear to work upstream of GacA to possibly control the switch to a biofilm lifestyle [34] , [35] . Mutations in algR , a response regulator protein required for synthesis of alginate , which is a major component of the matrix of biofilms in the cystic fibrosis lung [1] results in a P . aeruginosa strain that has decreased type IV pili-dependent motility and biofilm formation [39] . The three-component system SadARS which regulates the formation of mature microcolonies [40] and PvrR , a response regulator involved in the switch from planktonic to antibiotic-resistant biofilm cells in P . aeruginosa are additional examples of regulators of biofilm formation [41] . During the course of an infection , one of the first lines of defense encountered by colonizing bacteria is the production of cationic antimicrobial peptides ( CAPs ) by a variety of host cells including neutrophils , platelets and epithelia . CAPs are short , amphipathic peptides that bind to and disrupt both the outer and cytoplasmic membranes resulting in cell death . The broad-spectrum antimicrobial activity of CAPs against Gram-negative and Gram-positive bacteria accounts for their role as an essential component of the innate immune response of humans , animals and insects . Cationic peptides , which have antimicrobial and immunomodulatory activities , are being developed as a promising new class of therapeutically relevant drugs [42] . In P . aeruginosa , resistance to CAPs is inducible by the PhoPQ and PmrAB TCSs , both of which are activated independently in response to limiting Mg2+ [43]–[46] . Under conditions of limiting magnesium , PhoP and PmrA bind to the promoter of the CAP resistance operon PA3552–PA3559 ( arnBCADTEF-ugd ) and induce its expression [45]–[47] . These genes encode an LPS modification pathway required for the addition of aminoarabinose to lipid A , which reduces the OM permeability to CAPs [48] . The PhoPQ and PmrAB regulatory systems are well studied in planktonic cultures and have been shown to induce modest resistance to CAPs ( 8-fold ) under low Mg2+ conditions [45] . However , while the PA3552–PA3559 operon has been reported to be expressed in biofilms cultivated in flowcells , and is required for survival in response to colistin treatment [49] , little else is known regarding these systems and the role they may play in biofilm-associated antibiotic resistance . The extracellular matrix of P . aeruginosa biofilms includes extracellular DNA [50] , [51] , multiple bacterial exopolysaccharides and host proteins [4] , [52] . Extracellular DNA , which is a matrix component of both Gram-positive and Gram-negative bacterial biofilms [51] , [53] , functions to maintain the 3D biofilm architecture by acting as a cell-cell interconnecting compound [50] . Genomic DNA has been shown to localize to the biofilm surface , surrounding the mushroom-shaped microcolonies [51] . DNA in the biofilm matrix is likely released by dead bacteria or immune cells . It has been reported that prophage-mediated cell death is an important mechanism in the differentiation and dispersal of biofilms [54] , [55] . Additional sources of DNA in biofilms may include the quorum sensing regulated release of DNA [51] and/or DNA contained within outer membrane vesicles ( OMV ) that bleb and are released from the OM of living P . aeruginosa cells [56] , [57] . Furthermore , while a specific mechanism of DNA release has not been reported for P . aeruginosa it is possible that such a method may exist , similar to the autolysin-mediated DNA release observed in Staphylococcus epidermidis biofilms [53] . In this study we sought to examine if the presence of DNA in biofilms may contribute to biofilm-specific antibiotic resistance . Here we identify a novel cation chelating property of DNA , which has several important consequences for biofilm physiology and antibiotic resistance in biofilms .
To study the role of the matrix component DNA on biofilm formation and biofilm-associated antibiotic resistance , we first examined the influence of extracellular DNA on P . aeruginosa growth in rich and defined media , LB and BM2 , respectively . Addition of 0 . 5% ( w/v ) ( 5 mg/ml ) or greater extracellular DNA to LB or 1% ( w/v ) or greater DNA to BM2 media inhibited growth of P . aeruginosa ( Fig 1A and 1B ) . The kinetics of killing by extracellular DNA was determined by measuring the loss of luminescence from a chromosomally-tagged luminescent P . aeruginosa strain , PAO1::p16Slux . DNA-mediated killing was fast , within minutes , as measured by the rapid loss of luminescence upon exposure to 1 . 25% ( w/v ) DNA , or greater ( Fig 1C ) . Killing was dose-dependent , with faster killing observed as the DNA concentration increased ( Fig 1C ) . The rapid decrease in luminescence corresponded with a loss of bacterial viability , as determined by plating ( Fig 1D ) . One percent ( w/v ) extracellular DNA in LB also inhibited the growth of Escherichia coli , Staphylococcus aureus and Burkholderia cenocepacia ( data not shown ) , suggesting that the antimicrobial activity of DNA is not unique to P . aeruginosa . DNA is a highly anionic polymer due to the phosphates in the deoxyribose backbone . This property , in combination with the fast-killing observed in response to extracellular DNA led us to hypothesize that addition of exogenous DNA resulted in the loss of membrane integrity through cation chelation , in a manner similar to that observed with the known cation chelator EDTA [58] . The OM of P . aeruginosa contains a 20∶1 ratio of Mg2+∶Ca2+ cations [59] , which bind to and stabilize LPS in the outer leaflet of the OM [58] . EDTA treatment of cells resulted in chelation and removal of divalent cations from the OM , leading to disruption of the OM [58] . To determine the effect of DNA on membrane integrity , microscopic analysis in response to lethal concentrations of DNA and relevant controls was performed . Lipoproteins are lipid-modified proteins anchored in the outer leaflet of the IM or the inner leaflet of the OM . P . aeruginosa cells producing mCherry fluorescent membrane-anchored lipoproteins ( lipoChFP ) that are localized to either the OM or IM [60] , [61] were used as markers of OM and IM integrity . LipoChFP-labelled P . aeruginosa cells showed dramatic membrane perturbations when exposed to 2% ( w/v ) DNA , but showed uniform membrane staining patterns in untreated cells ( Fig 2A ) . The OM perturbations in DNA-exposed cells included regions of patchy fluorescence and the release of OMVs , while the IM perturbations were visualized simply as patchy and irregular regions of membrane fluorescence ( Fig 2A ) . EDTA , the known cation chelator caused comparable IM and OM perturbations as those observed in cells exposed to extracellular DNA . Propidium iodide ( PI ) stains extracellular DNA and DNA in dead cells . PI staining was observed in cells exposed to DNA and EDTA , confirming that this treatment was lethal ( Fig 2B ) . PI staining also revealed the presence of long strands of genomic DNA , presumably as a consequence of the loss of membrane integrity , cell lysis and release of cytoplasmic contents , including DNA ( Fig 2B ) . The DNA released by lysed cells formed a mesh-like coating surrounding and connecting individual bacterial cells ( Fig 2B ) . Degradation of these strands by DNAse treatment of lysed cells confirmed that these fibres were composed of DNA ( Fig S1 ) . Pseudomonas specific semi-quantitative PCR ( qPCR ) was also performed to confirm that the DNA released following DNA or EDTA treated cells was in fact genomic DNA from P . aeruginosa ( Fig 2C ) . Buffer treated control cells showed intense green staining with syto9 ( indicating viability ) and a lack of PI staining ( indicating no dead/dying cells or DNA release ) ( Fig S1 ) . The observation that DNA disrupted the integrity of the cell envelope causing cell lysis suggested that DNA was acting as a cation chelator . To confirm that DNA-mediated killing was a result of cation chelation , excess Mg2+ , Ca2+ , Mn2+ , and Zn2+ were added to P . aeruginosa cultures . The rapidity of DNA-induced cell death ruled out the possibility that death , or lack of growth , was simply due to cation starvation . Addition of excess cations to planktonic cultures inhibited the fast-acting antimicrobial effects of DNA ( Fig 3A ) . Protection was measured in response to a range of cation concentrations , where the highest concentration tested was that which remained soluble in the presence of DNA ( 3 . 125–25 mM ) . The concentration at which maximal protection was obtained for each cation is represented in Fig 3A ( 25 mM Mg2+; 6 . 25 mM Ca2+; 6 . 25 Mn2+; 3 . 125 mM Zn2+ ) . Kill curve assays indicated that the addition of Mg2+ , Ca2+ or Mn2+ provided protection from DNA-induced lysis , however , the addition of Zn2+ did not protect from DNA-mediated killing ( Fig 3A ) . In a similar manner , the addition of excess Mg2+ , Ca2+ and Mn2+ restored growth of P . aeruginosa in BM2 media . Only partial restoration of growth was observed in the presence of Zn2+ ( Fig 3B ) . The increased protection observed following addition of Mg2+ and Ca2+ is likely due to their importance in membrane integrity where they function to stabilize the OM by crosslinking adjacent LPS molecules [58] . Cations play diverse physiologically important roles within a cell; from detoxification of reactive oxygen species and co-factors for enzymes to the stabilization of macromolecules within the cell [62] . Since Mg2+ limitation has been shown to be associated with CAP resistance in P . aeruginosa [44] , [45] , [47] , we sought to determine if Mg2+ chelation by DNA may account , at least in part , for the increased antibiotic resistance observed in biofilms . In P . aeruginosa , the PhoPQ and PmrAB-controlled response to magnesium limitation includes the induction of the PA3552 and its neighbouring genes . The genes PA3552–PA3559 are co-regulated under low Mg2+ conditions and are thought to be organized as an operon [45]–[47] . These genes encode an LPS modification pathway required for the addition of aminoarabinose to lipid A , which reduces the OM permeability to CAPs , thus conferring resistance [48] . To determine if extracellular DNA imposes Mg2+ limitation , we measured the gene expression of a chromosomally encoded transcriptional lux ( bioluminescence ) fusion to PA3553 , as a measure of the CAP resistance operon expression in planktonic cultures . PA3553::lux expression was strongly induced ( up to 10-fold ) by sub-inhibitory concentrations of low molecular weight salmon sperm DNA ( Fig 4A ) . Induction of the CAP resistance operon was dose-dependent with increasing DNA concentrations up to 0 . 5% ( w/v ) DNA , at which growth is inhibited ( Fig 4A ) . Addition of excess Mg2+ ( 5 mM ) to the growth medium completely repressed the expression of PA3553 in cultures supplemented with DNA , except at the highest DNA concentration tested ( 0 . 5% ( w/v ) ) ( Fig 4B ) . A similar induction profile of PA3553::lux was observed following exposure to high molecular weight P . aeruginosa genomic DNA ( not shown ) or P . aeruginosa genomic DNA that was mechanically sheared by sonication ( Fig 4C ) . P . aeruginosa genomic DNA inhibited growth at similar concentrations as salmon sperm DNA . Thus , the ability of extracellular DNA to chelate magnesium is independent of origin and molecular weight , indicating that chelation is a general property of this negatively charged polymer . To ensure that induction of PA3553 expression was specific to the ability of DNA to chelate cations , DNAse treated DNA was assessed for its ability to induce PA3553 gene expression ( Fig 4D ) . DNAse treated DNA failed to induce PA3553 gene expression . However the addition of DNAse buffer to cells in our buffer control experiment also abolished induction of PA3553 . This is due to the addition of excess Mg2+ ions as part of the DNAse buffer , which is required for DNAse activity . Thus , it is impossible to determine conclusively if DNAse treatment of DNA abolished PA3553 gene expression . To determine the influence of extracellular DNA on PA3553 gene expression in biofilms , DNA-enriched biofilms were cultivated on the surface of polystyrene pegs . Consistent with previous reports that DNA is a component of biofilms [50] , [51] , we observed DNA in 24 h old peg-adhered biofilms ( Fig 5A and 5B ) . Double staining of P . aeruginosa with syto9 ( stains viable cells green ) and the extracellular DNA stain DDAO ( red ) [51] was used to visualize DNA as a loose lattice in biofilms formed on polystyrene pegs after 24 h ( Fig 5A ) . DNA was also visualized ( PI stained ) as a mesh-like DNA matrix in 1 day-old peg-adhered biofilm monolayers ( Fig 5B ) , which resembled the thread-like projections of genomic DNA observed in DNA or EDTA-lysed cells ( Fig 2B ) . These localization patterns of extracellular DNA are suggestive of DNA gradients within biofilms . Biofilm formation was inhibited at extracellular DNA concentrations ≥0 . 5% ( w/v ) ( Fig 6A ) . This is consistent with the observed growth inhibition of planktonic cells at similar DNA concentrations ( Fig 1A ) . One-day old PA3553::lux biofilms were washed to remove non-adhered cells and gene expression was measured from the cells adhered to the polystyrene peg surface . PA3553 gene expression was strongly induced , up to 20-fold , in peg-adhered biofilms , with the highest induction at 0 . 5% ( w/v ) extracellular DNA ( Fig 6B ) . Although gene expression was measured in a mutant background , both PAO1 and PA3553::lux had similar biofilm phenotypes in each condition tested ( Fig 6A ) . In biofilms cultivated in the presence of extracellular DNA supplemented with excess Mg2+ ( 5 mM ) , PA3553 gene expression was completely repressed ( data not shown ) . At lethal concentrations , extracellular DNA induced cell lysis by chelating cations from the OM . This antimicrobial activity can be prevented if DNA is pre-loaded with Mg2+ , Ca2+ or Mn2+ , but not Zn2+ , prior to treatment of cells ( Fig 3A and 3B ) . To determine the specificity of cation chelation , flame atomic absorption spectroscopy was employed to quantitate DNA-dependent removal of cations from buffer containing known concentrations of Mg2+ , Ca2+ , Mn2+ or Zn2+ and a combination of all four cations . DNA was capable of binding all four cations at similar levels ( 80–88% ) , whether alone ( Fig 7A ) or in combination ( data not shown ) . To ensure binding was specific to DNA a negative control was included . The concentration of Mg2+ that bound to the column in the absence of DNA is indicated . At sub-lethal concentrations , extracellular DNA imposes a cation limitation that leads to induction of PA3553 ( Fig 4A ) , which can be repressed by excess Mg2+ ( Fig 4B ) , indicating that P . aeruginosa senses Mg2+ . The P . aeruginosa PhoQ sensor kinase protein has been shown to bind to and be repressed by Mg2+ and Ca2+ cations [63] , [64] . Under limiting Mg2+ conditions , the addition of excess Mg2+ , Ca2+ or Mn2+ , but not Zn2+ , repressed PA3553 expression ( Fig 7B ) . Taken together , these data indicate that P . aeruginosa can sense the presence of Mg2+ , Ca2+ or Mn2+ and that chelation of these same cations by DNA results in induction of the PA3552–PA3559 LPS modification operon . To determine if DNA-induced expression of PA3552–PA3559 resulted in increased resistance to antimicrobials , antibiotic susceptibility testing of P . aeruginosa biofilms grown in the presence and absence of extracellular DNA was performed . Biofilms were cultivated on 96-well format , polystyrene pegs submerged in BM2 defined media , with or without sub-inhibitory concentrations of extracellular DNA ( 0 . 75% ( w/v ) ) , and challenged with antibiotics . After antibiotic challenge , this assay allows for determination of both the minimum inhibitory concentration ( MIC ) of planktonic cultures and the minimum biofilm eradication concentration ( MBEC ) . Consistent with previous results reporting on the antibiotic resistance phenotype of bacterial biofilms [6] , [19] , the MBEC values of biofilms cultivated in magnesium-replete conditions and treated with CAPs ( polymyxin B , colistin ) or aminoglycosides ( gentamycin , tobramycin ) were up to 64-fold higher than the MIC values of planktonic cultures ( Table 1 ) . The MBEC values of biofilms supplemented with extracellular DNA were 8 and 64-fold more CAP and aminoglycoside resistant than biofilms without exogenous DNA , respectively ( Table 1 ) . DNA-enriched biofilms were dramatically more resistant than planktonic cultures , up to 256-fold , and this resistance phenotype to CAPs and aminoglycosides was also observed in planktonic cultures supplemented with DNA . The simple addition of sub-inhibitory DNA amounts to planktonic cultures closely simulated the resistance-inducing effects of DNA in a biofilm ( Table 1 ) . The MIC values for polymyxin B and gentamicin are equal to 1 µg/ml and 2 µg/ml , respectively , using the standard microbroth dilution method for antimicrobial susceptibility testing ( National Committee on Clinical Laboratory Standards ( NCCLS ) protocol ) ( data not shown ) . Thus , depending on the method used to determine the MIC ( CBD or NCCLS protocol ) , DNA-enriched biofilms can be up to 2560-fold more polymyxin B resistant and up to 640-fold more aminoglycoside resistant than planktonic cultures . DNA-enriched biofilms did not show an increased tolerance to ceftazidime ( β-lactam ) or ciprofloxacin ( fluoroquinolone ) ( data not shown ) . Since extracellular DNA is a natural matrix component of PAO1 biofilms ( Fig 5A and 5B ) , DNA-induced antibiotic resistance is likely to be a phenomenon unique to biofilms or other DNA rich environments . The presence of DNA in peg-cultivated biofilms ( Fig 5A ) , grown in the absence of exogenous DNA , likely contributes to the increased antibiotic resistance generally observed in biofilms ( Table 1 ) . We have shown previously that the PA3552–PA3559 operon is required for resistance to cationic antimicrobial peptides in planktonic cultures grown in limiting magnesium conditions [47] . To determine if DNA-induced resistance requires these genes in biofilms , the resistance phenotype of the PA3553::lux mutant was determined . PA3553::lux had no significant DNA-induced CAP resistance in biofilm or planktonic cultures , confirming that these genes are essential for CAP resistance in the presence of extracellular DNA ( Table 1 ) . The PA3553 mutant also displayed decreased DNA-induced resistance to aminoglycosides compared to PAO1 . The differences observed between CAP and aminoglycoside resistance in PA3553::lux suggests that DNA-induced resistance to aminoglycosides is not limited to PA3553 induction . The biofilms formed by the PA3553::lux mutant were unaltered compared to PAO1 biofilms under these conditions , ensuring that the difference observed was not due to an altered biofilm phenotype ( Fig 6B ) . The CAP resistance phenotype of biofilms grown in limiting magnesium ( 20 µM ) was similar to biofilms grown in DNA , confirming that DNA imposes a magnesium limitation stress ( Table 2 ) . Biofilms that were exposed to DNA during either the cultivation or challenge stages only , showed similar resistance profiles to biofilms grown and challenged in magnesium-replete conditions ( Tables 1–2 ) . Therefore , the DNA-induced resistance of biofilms requires both the cultivation and challenge under cation-limiting conditions . These latter two observations rule out the possibility that negatively charged DNA simply interacts with cationic antimicrobial peptides and prevents their access to bacterial cells .
Infections caused by P . aeruginosa continue to be a leading cause of mortality among immunocompromised patients . The ability of P . aeruginosa to form biofilms promotes survival of the bacteria in the presence of antimicrobials and host defense mechanisms and is thought to contribute significantly to its ability to survive long-term within the hostile environment of chronically-infected patients . Understanding the mechanisms underlying antibiotic resistance and especially biofilm-specific antimicrobial resistance is of significant importance in the development of new treatment options and/or strategies . We have identified a novel mechanism of biofilm-associated antibiotic resistance in which the presence of DNA in the extracellular matrix of biofilms creates a localized cation-limited environment that is detected by P . aeruginosa leading to the induction of LPS modification genes and resistance to antimicrobials . Magnesium limitation has long been known as an in vitro signal that induces resistance to CAPs in P . aeruginosa [59] . As an intracellular pathogen , the PhoPQ system of Salmonella typhimurium is activated by limiting magnesium in vitro and phoP-regulated genes are also induced after invasion of macrophages and epithelial cells [65] . These observations suggested that Mg2+ is limiting within host cells , but it was recently shown that vacuole acidification and low pH is the crucial environmental trigger of PhoPQ activation [66] . Many extracellular pathogens possess homologs of the cation-sensing PhoPQ TCS that responds to magnesium limitation and induces genes necessary for surviving this environmental challenge [65] . However , to date the identification of a relevant in vivo environment for P . aeruginosa which is cation limited has remained elusive . We have demonstrated that DNA-rich environments , such as biofilms , are cation limited . While Mg2+ limitation has been identified as a signal involved in induced resistance to aminoglycosides in P . aeruginosa [59] , the contribution of the PhoPQ-regulated LPS modifications has not been clearly determined . PhoQ mutants , which constitutively express phoP and are constitutively resistant to cationic antimicrobial peptides , are also more resistant to aminoglycosides [43] . In S . typhimurium , PhoPQ regulates multiple LPS modifications that decrease the OM permeability to membrane cationic dyes , bile salts and antibiotics , including gentamicin [67] . We report here that DNA-induces aminoglycoside resistance in P . aeruginosa biofilms , and this resistance is partially dependent on the LPS modification operon PA3552–PA3559 . The aminoarabinose modification likely blocks the self-promoted uptake of aminoglycosides , which normally bind and displace cations that crosslink adjacent LPS molecules [68] . Previous reports have documented the involvement of P . aeruginosa PmrAB [49] and the E . coli PmrAB homologs BasRS [69] in regulating the formation of an antimicrobial peptide-tolerant subpopulation within biofilms . In pure culture P . aeruginosa biofilms , genomic DNA localizes throughout the biofilm surface monolayer and surrounds the mushroom-shaped microcolonies [51] . This coincides with the localization of a CAP-tolerant subpopulation of bacteria that expresses the PA3552–PA3559 operon along the surface of mushroom-structured P . aeruginosa biofilms [49] . To date , it was thought unlikely that a biofilm environment may be cation limited . However , our data indicates that the presence of DNA in biofilms does indeed result in a cation-limited environment , resulting in the induction of the LPS modification operon PA3552–PA3559 . To our knowledge this is the first report to identify the antimicrobial properties of DNA . Above certain concentrations ( ∼0 . 5% ( w/v ) ) extracellular DNA inhibited planktonic growth and biofilm formation . Recently , a novel host defense mechanism was discovered whereby stimulated neutrophils ejected a mesh-like net of intracellular DNA and proteins that functions to trap and kill pathogens [70] . The antimicrobial property of neutrophil nets was attributed to DNA-associated histones and other antimicrobial peptides [70] . However , our results demonstrate that above certain concentrations , the DNA itself is antimicrobial due to cation chelation . In principle , cation chelation by DNA is similar to another recently identified host defense mechanism , where the Mn2+ and Zn2+ metal chelation properties of the host innate-immune protein calprotectin was shown to limit Staphylococcus aureus growth in tissue abscesses [71] . Staining of peg-adhered biofilms indicated that DNA was present throughout the biofilm . ( Fig 5B ) . This data supports the hypothesis that the release of genomic DNA by lysed cells following exposure to inhibitory concentrations of extracellular DNA may result in a continual release of DNA by dying cells and a DNA gradient within the biofilm . Our observation that DNA imposes a cation gradient in biofilm is also consistent with previous reports of oxygen and nutrient gradients within biofilms , which result in diverse physiological cellular states within a biofilm community [72] . Although DNA is toxic at high concentrations , it functions as a double-edged sword whereby sub-inhibitory DNA concentrations serve to protect bacteria from antibiotic exposure , either from the host immune response or from antimicrobial treatment . It has previously been reported that Mg2+ concentrations within the airway surface fluid are high ( 2 . 2 mM ) [73] , [74] . However , sputum samples from the lungs of CF patients have very high concentrations of DNA , up to 20 mg/ml ( 2% ( w/v ) ) [75] , [76] . It is likely that within the CF lung , localized cation limited environments exist within DNA-rich microcolonies . It is also known that CF airway fluid contains high levels of neutrophil defensins [77] and that sub-lethal doses of CAPs induce PA3553 gene expression , although independently of PhoPQ and PmrAB [45] . Therefore , it appears that there are multiple environmental signals in the CF lung that can induce the expression the PA3552–PA3559 operon , which may explain why many P . aeruginosa CF isolates show LPS modifications such as aminoarabinose addition to lipid A [78] . As many P . aeruginosa strains isolated from the CF lung overproduce the negatively charged EPS alginate , we hypothesized that alginate may also be a relevant in vivo signal inducing expression of the PA3552–PA3559 operon . However , induction of PA3553 gene expression does not occur in the presence of alginate ( data not shown ) . The observation that DNA is present in the lungs of CF patients has prompted the use of DNAseI as a therapeutic agent to reduce the sputum viscosity and improve lung function [75] , [76] . However , our data suggests that the success of DNAseI therapy may , in part , be attributed to the degradation of DNA and subsequent disarming of the PhoPQ/PmrAB response and antibiotic resistance mechanisms . While previous studies have shown the biofilm matrix to function as a diffusion barrier to antibiotics , these results demonstrate a novel function of the biofilm matrix component DNA , where the cation chelating properties of DNA in biofilms induces resistance to host-derived or therapeutic antimicrobials . Furthermore , these findings indicate that DNA-rich environments , such as bacterial biofilms or the CF lung , may represent the natural setting where bacterial growth is cation limited , and highlight the importance of the PhoPQ/PmrAB controlled response and LPS modifications in antibiotic resistance in biofilms .
Pseudomonas aeruginosa PAO1 and lux-tagged PAO1::p16Slux [79] were used as wild-type strains . The mini-Tn5-lux transposon mutant in the CAP resistance gene PA3553::lux ( arnC ) was previously constructed [47] . For all experiments involving DNA , DNA was isolated in the absence of EDTA and resuspended in the buffer or media in which each experiment was carried out . Growth kinetics of P . aeruginosa was carried out in LB or BM2 media [47] supplemented with low molecular weight salmon sperm ( Fluka ) or P . aeruginosa genomic DNA , with and without the addition of various cations in excess ( 25 , 12 . 5 , 6 . 25 , and 3 . 125 mM ) . Cation sources were MgCl2 , CaCl2 , MnCl2 and ZnCl2 . For Mg2+ supplementation , no difference was observed when MgCl2 was substituted with MgSO4 . Growth assays were carried out in 100 µl volumes in transparent 96-well plates ( Nunc ) . Fifty µl of sterile mineral oil was added to each well to prevent evaporation during the assay . Microplate planktonic cultures were incubated at 37°C in a Wallac Victor3 luminescence plate reader ( Perkin-Elmer ) and optical density ( growth , OD600 ) readings were taken every 20 minutes throughout growth . Killing assays were carried out as previously described [80] . Briefly , overnight cultures of PAO1::p16Slux were washed and diluted in 25 mM sodium phosphate or 50 mM Hepes buffer , pH 7 . 4 , as indicated in the figure legends . 5×107 cfu were exposed to varying concentrations of salmon sperm DNA , in the presence or absence of excess cations , and CPS monitored over time , as a measure of viability . Each growth or killing experiment was performed at least five times and representative curves are shown . For microscopy analysis of peg-adhered biofilms , PAO1 was cultivated on pegs ( NUNC-TSP ) , washed as described below and stained with 1 µM 7-hydroxy-9H- ( 1 , 3-dichloro-9 , 9-dimethylacridin-2-one ) ( DDAO ) ( Molecular Probes ) or 10 µM propidium iodide ( PI ) for 10 mins . Individual pegs were removed and placed on a drop of 0 . 9% saline on a glass slide prior to visualization . Images were captured with a Leica DMIREB2 inverted , epifluorescence microscope . For DNA lysis experiments , overnight cultures of PAO1 producing mCherry fluorescent lipoproteins with sorting signals for either the OM ( lipoCSFP-ChFP ) or IM ( lipoCKVE-ChFP ) were subcultured 1/100 and grown for 3 h to mid-log phase ( OD 0 . 5 ) [60] , [61] . Overnight cultures were diluted 1 in 100 and grown to mid-log phase . 1 . 5×108 cells were spun , washed in sodium phosphate buffer ( 25 mM , pH 7 . 4 ) and resuspended in 50 µl of 1 mM EDTA , 2% ( w/v ) salmon sperm DNA or buffer alone ( negative control ) . Cells were lysed for 10 mins , pelleted ( 8000 rpm , 5 mins ) and 1 µl of supernatent used as a template for semi-quantitative PCR ( 25 cycles ) . PCR was carried out on lysates obtained from 2% ( w/v ) DNA , 1 mM EDTA and untreated control cells . For P . aeruginosa specific PCR studies , 1 µl of the lysate was used as a template for semi-quantitative PCR using P . aeruginosa specific primers ( F-5′ gaggatcccgccgggttttttgtgtctg-3′ , R-5′gaggatcccaggagtgatattagcgattc-3′ ) . These primers amplify a 216 bp product corresponding to the promoter region of the rsmZ gene in P . aeruginosa . PCR controls included 2% ( w/v ) salmon sperm DNA alone to ensure the specificity of the primers for P . aeruginosa DNA , a negative PCR control with no template and a positive PCR control containing P . aeruginosa genomic DNA as template . For microscopy , cells were washed , concentrated 2 . 5 fold in sodium phosphate buffer ( 25 mM , pH 7 . 4 ) and stained with 10 µM PI or left unstained . Cells were visualized on agarose beds with a Leica DMIREB2 inverted microscope equipped with an ORCA-ER digital camera and Openlab software ( Improvision ) . For DNAse treatment of DNA/EDTA lysed cells or relevant controls , cells were treated with DNAseI ( 500 µg/ml ) for 45 mins at 37°C prior to the addition of fluorescent dyes and microscopic analysis . To determine the specificity of cation chelation by DNA , 1 . 25% ( w/v ) salmon sperm DNA was resuspended in 50 mM Hepes buffer , pH 7 . 4 , and incubated individually with 2 . 5 mM Mg2+ , Ca2+ , Zn2+ or Mn2+ or a cocktail of 0 . 625 mM of each cation . After 3 h incubation at room temperature with constant rotation , samples were centrifuged in a Amicon ultra column ( Millipore ) with a 10 kDa cutoff ( 3200 g for 30 mins ) . All unbound cations passed through the filter but DNA was retained . The filtrate was sent for flame atomic absorption spectroscopy analysis to determine the percentage of cation not bound by DNA ( Bodycote Testing Group , Portland , OR , USA ) . Values represented are the percent of cations bound to DNA . Overnight cultures were grown in LB medium or BM2 defined medium ( 20 mM succinate ) supplemented with 2 , 1 or 0 . 02 mM Mg2+ and extracellular DNA as indicated , diluted 1/100 into 100 µl of culture medium in 96-well black plates with a transparent bottom ( 9520 Costar; Corning Inc . ) and overlayed with 50 µl of mineral oil to prevent evaporation . Microplate planktonic cultures were incubated at 37°C in a Wallac Victor3 luminescence plate reader ( Perkin-Elmer ) and optical density ( growth , OD600 ) and luminescence ( gene expression , CPS ) readings were taken every 20 minutes throughout growth . For DNAse treatment experiments , 2% salmon sperm DNA was treated for 48 hrs at 37°C with 500 µg/ml of DNAseI enzyme in 40 mM Tris , 10 mM MgSO4 and CaCl2 . Biofilms were cultivated on 96-well format , polystyrene pegs ( Nunc-TSP ) that were immersed in 200 µl of growth medium . After biofilm cultivation , non-adherent cells were removed by rinsing the pegs in 0 . 9% NaCl . Gene expression in peg-adhered biofilms was measured by luminescence readings in the Wallac MicroBeta Trilux multi-detector ( Perkin-Elmer ) . Biofilm formation on the pegs was quantitated by crystal violet ( CV ) staining ( OD600 ) as previously described [81] . P . aeruginosa biofilms were tested for susceptibility using the Calgary Biofilm Device protocol [6] . Overnight cultures of P . aeruginosa PAO1 and PA3553::lux were grown in BM2 defined medium with magnesium concentrations as indicated and supplemented with 0 . 75% ( w/v ) salmon sperm DNA . This concentration of DNA was not toxic in BM2 medium with 2 mM Mg2+ . Starter cultures were diluted in the appropriate medium and inoculated at a concentration of 1 . 5×106 cfu/well . Biofilms were cultivated on the peg lids by shaking the plate at 37°C for 24 hours . The pegs were rinsed twice in 0 . 9% NaCl and transferred to challenge plates , which consisted of a serial two-fold dilution gradient of polymyxin B , colistin , gentamycin or tobramycin . Peg-adhered biofilms were challenged in the same media in which they were cultivated . Following a 24-hour antibiotic challenge , the MIC values were determined by measuring growth ( OD600 ) in the challenge plate . After biofilm challenge , the surviving cells in peg-adhered biofilms were rinsed twice in 0 . 9% NaCl , DNAseI treated ( 25 µg/ml ) for 30 mins and sonicated for 10 mins to remove attached cells . The surviving cells were enumerated by serial dilution and plate counts to determine the MBEC value .
|
Pseudomonas aeruginosa is an opportunistic pathogen , which causes a variety of serious infections in immunocompromised patients and cystic fibrosis ( CF ) sufferers . The biofilm-forming ability of P . aeruginosa is thought to contribute to chronic P . aeruginosa infection of the CF lung . Biofilms are dense communities of bacteria , encased in an extracellular matrix , that are practically impossible to eradicate using available antimicrobial therapies . Understanding the mechanisms by which biofilm bacteria develop resistance to antibiotics is paramount to expanding the treatment options available to patients with chronic biofilm infections . In this study we have identified a novel mechanism of biofilm-specific antibiotic resistance . Extracellular DNA , a known component of biofilms , was found to induce antibiotic resistance . This previously unidentified function of DNA was due to its ability to bind and sequester cations , including magnesium , from the surrounding environment . This environmental cue was then detected by P . aeruginosa leading to induction of genes involved in modification of the cell surface component , lipopolysaccharide ( LPS ) , resulting in physical alterations in the bacterial outer membrane ( OM ) . These results demonstrate a novel function for DNA in biofilms and identify cation chelation by DNA as a previously unrecognized mechanism , which can explain the increased resistance of biofilms to antimicrobial agents .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"microbiology/environmental",
"microbiology",
"microbiology/innate",
"immunity",
"microbiology",
"infectious",
"diseases/bacterial",
"infections",
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis",
"microbiology/medical",
"microbiology",
"infectious",
"diseases/antimicrobials",
"and",
"drug",
"resistance"
] |
2008
|
Extracellular DNA Chelates Cations and Induces Antibiotic Resistance in Pseudomonas aeruginosa Biofilms
|
Genome rearrangement often produces chromosomes with two centromeres ( dicentrics ) that are inherently unstable because of bridge formation and breakage during cell division . However , mammalian dicentrics , and particularly those in humans , can be quite stable , usually because one centromere is functionally silenced . Molecular mechanisms of centromere inactivation are poorly understood since there are few systems to experimentally create dicentric human chromosomes . Here , we describe a human cell culture model that enriches for de novo dicentrics . We demonstrate that transient disruption of human telomere structure non-randomly produces dicentric fusions involving acrocentric chromosomes . The induced dicentrics vary in structure near fusion breakpoints and like naturally-occurring dicentrics , exhibit various inter-centromeric distances . Many functional dicentrics persist for months after formation . Even those with distantly spaced centromeres remain functionally dicentric for 20 cell generations . Other dicentrics within the population reflect centromere inactivation . In some cases , centromere inactivation occurs by an apparently epigenetic mechanism . In other dicentrics , the size of the α-satellite DNA array associated with CENP-A is reduced compared to the same array before dicentric formation . Extra-chromosomal fragments that contained CENP-A often appear in the same cells as dicentrics . Some of these fragments are derived from the same α-satellite DNA array as inactivated centromeres . Our results indicate that dicentric human chromosomes undergo alternative fates after formation . Many retain two active centromeres and are stable through multiple cell divisions . Others undergo centromere inactivation . This event occurs within a broad temporal window and can involve deletion of chromatin that marks the locus as a site for CENP-A maintenance/replenishment .
Chromosome inheritance requires essential chromosomal loci , namely centromeres , telomeres and origins of replication . Origins ensure precise copying of the entire genome , telomeres protect chromosome termini from degradation and deletion , and centromeres partition the copied genome to daughter cells . Defects in any of these functions lead to genome instability , rearrangement , and aneuploidy . Chromosome abnormalities are major factors in disease , reproductive failure , miscarriage and infertility . In addition , genome rearrangements ( deletions , duplications , translocations , insertions , inversions ) are a hallmark of many cancers [1] . The vast number of recurrent and non-recurrent cancer-related chromosome rearrangements highlights the scope of human genome instability ( http://cgap . nci . nih . gov/Chromosomes/Mitelman ) [2] , [3] . Constitutive chromosome abnormalities also underlie congenital human diseases . Notwithstanding the frequency of these abnormalities , their origin and behavior at the time of formation are less clear , and can usually be inferred only from patient samples that are studied long after the rearrangements have occurred . In humans , the most common structural chromosome rearrangement is the Robertsonian translocation ( ROB ) [4] . First described in insects [5] , ROBs are formed by fusion at the centromere region between two acrocentric chromosomes . The term “acrocentric” refers to a chromosome in which the centromere is located very near one end , and the short arm may be difficult to observe cytologically . Humans have five pairs of acrocentric chromosomes , Homo sapiens chromosome ( HSA ) 13 , HSA14 , HSA15 , HSA21 and HSA22 . All acrocentric short arms contain homologous , but compositionally heterogeneous blocks of repetitive sequences that span the estimated 10–15Mb between the telomere and the α-satellite DNA of the centromere . These repeats include multiple copies of the ribosomal genes ( rDNA ) composed of subunits of 18S , 5 . 8S and 28S rDNA and an intergenic spacer [6] . The rDNA clusters appear as nucleolar organizing regions ( NORs ) around which the nucleolus is formed . Tandemly repeated rDNA units are flanked by multiple subfamilies of ß-satellite DNA [7] , [8] . In addition , several different subfamilies of satellite III DNA are located between proximal ß-satellite arrays and the α-satellite DNA of the centromere [7] , [9]–[11] . ROBs in humans are rarely formed by breakage within the centromere . Most are actually short arm fusions , and breaks occur within satellite III DNA [12]–[16] . Consequently , >90% of patient-derived ROBs have two centromeres and are structurally dicentric . In humans , rob ( 13;14 ) and rob ( 14;21 ) account for approximately 85% of all ROBs , suggesting that the participation of the five human acrocentric chromosomes in ROBs is non-random [12] , [17] , [18] . Genomic organization or chromosome-specific interactions may favor the formation of these particular ROBs . Alternatively , the prevalence of rob ( 13;14 ) and rob ( 14;21 ) may simply reflect a population bias for the most viable developmental outcomes . The prevailing view of dicentric behavior , first described by Barbara McClintock in the late 1930s , is that they are inherently unstable , often going through successive rounds of anaphase bridging and breakage [19]–[22] . However , dicentric ROBs in humans can be unusually stable and are often inherited through meiosis . Their stability has been attributed to centromere inactivation , a process by which one centromere is functionally silenced [23]–[26] . Testing mechanisms of centromere inactivation has proven difficult because there are few experimental systems to produce de novo dicentric human chromosomes . Much more progress has been made in understanding normal centromere structure and function . Our view of inactive centromeres is largely based on comparisons between active and inactive centromeres . The centromere is a complex chromosomal locus where the kinetochore is formed and microtubules attach during cell division . A major component of functional centromeres is CENP-A ( Centromere Protein A ) , a histone H3 variant that replaces canonical H3 to create unique centromeric nucleosomes [27] , [28] . CENP-A marks centromeres physically by assembling into largely homotypic nucleosomes ( two copies of CENP-A ) that have a more rigid conformation than H3-containing nucleosomes [29] , [30] . Centromeric chromatin is arranged as multiple subunits of CENP-A nucleosomes periodically interspersed with subunits of H3 nucleosomes that are dimethylated at lysine 4 [31] , [32] . It is thought that the physically distinct nucleosomes and long-range chromatin organization together make a platform upon which the kinetochore is formed and to which additional centromere and kinetochore proteins are recruited [33]–[35] . In genomic terms , multi-megabase regions of repetitive α-satellite that are concentrated at the primary constriction define the human centromere locus [36] , [37] . However , CENP-A and other centromere/kinetochore proteins sequentially assemble on only a portion of the α-satellite array [38]–[40] . Inactive centromeres of dicentric chromosomes lack key centromere and kinetochore proteins , such as CENP-A , CENP-C , and CENP-E [25] , [26] . At metaphase , they do not have a defined primary constriction and morphologically resemble chromosome arms . Thus , centromere inactivation is predicted to involve exclusion of centromere proteins and chromatin remodeling so that the two centromeres on the dicentric are functionally distinct . Some inactive centromeres are underacetylated and heterochromatic , although it is not clear if these features correlate with terminal states of inactivation [41] , [42] or are representative of all inactive centromeres . Patient-derived dicentric chromosomes are discovered by chance , and probably represent the most stable dicentric chromosomes . The events responsible for centromeric silencing must have occurred long before ascertainment of the chromosome rearrangement . This assumption is supported by studies in model organisms showing that most engineered dicentric chromosomes in yeast , plants , and Drosophila are unstable [20] , [22] , [43] . A system in which human dicentrics could be created reproducibly and studied immediately at the time of formation would provide valuable insight into mechanisms of dicentric behavior and centromere inactivation and allow for comparisons to studies in model organisms . Here , we describe formation of de novo dicentric human chromosomes in vitro using transient expression of a mutant version of telomere protein TRF2 that disrupts telomere function [44] . Chromosome fusions that result from telomere dysfunction are non-random , with the majority of induced fusions occurring between acrocentric chromosomes that represent ∼20% of the human karyotype . Our studies suggest that one mechanism of centromere inactivation involves deletion of a fraction of the multi-megabase array of α-satellite DNA upon which CENP-A chromatin is assembled .
As expected , long inductions of dnTRF2 ( 3 or 5 days ) resulted in 3–15 fusions per cell ( Figure 1A and 1B ) that were often complex , multi-chromosome arrangements ( Figure 1A , Figure S1A and S1B ) . Induction of dnTRF2 for 30–45 hours produced fewer dicentric chromosomes per cell , after which cells proliferated indefinitely when continuously grown in dox+ media . Cells from independent inductions were analyzed using FISH with chromosome-specific painting probes and/or M-FISH ( multiplex fluorescence in situ hybridization ) . Less than 30% of uninduced cells contained a fusion , but in 36-hour inductions , >60% of cells contained two or more fusions . The number of induced cells that lacked fusions entirely was less than 15% . Expression of dnTRF2 expression for 36 hours produced more dicentric fusions in a greater number of cells and an increased number of dicentric fusions per cell compared to uninduced control/parental cells . Consequently , we focused our studies on the short-term , reversible inductions . Chromosome fusions occurred non-randomly . In two independently induced lines T4 and T19 , ∼80% of fusions involved acrocentrics ( Figure 1C , Figure S1C ) . Most acrocentric fusions ( 138/174 ) involved two acrocentric chromosomes joined at the short ( p ) arms ( Figure 1A , left panels ) , although we did observe p-q ( short arm-long arm ) and q-q fusions ( Tables S1 and S2 ) . This result was extremely statistically significant ( χ2 test , p<0 . 0001 ) when compared to the expected proportion of random acrocentric interactions ( 4 . 6% ) . The remaining fusions involved either two non-acrocentric chromosomes , or a non-acrocentric and an acrocentric . It was clear from these experiments that interactions among the acrocentric chromosomes occurred quickly and frequently . When dnTRF2 was expressed for 3 or 5 days , the number of fusions per cell increased even more dramatically ( Figure 1B , Figure 2 ) , and this group included more non-acrocentric chromosome fusions ( Figure 1C , Figure 2 ) . Complex rearrangements ( i . e . chains of 3–7 chromosomes ) , ring chromosomes , chromatid fusions , deleted chromosomes , and chromosome fragments were all observed ( Figure S1; Tables S1 and S2 ) . Importantly , even in longer inductions when more fusions occurred per cell , acrocentric-acrocentric fusions did not decrease . They consistently represented the largest proportion of all chromosome interactions in the cell population ( Figure 1C , Figure S1C ) . ROBs are the most prevalent chromosome translocation in humans . Acrocentric fusions ( i . e . induced ROBs , hereafter referred to as iROBs ) were also the most frequent in the inducible assay . In humans , rob ( 13;14 ) and rob ( 14;21 ) account for 85% of all ROBs [18] . However , irob ( 13;14 ) and irob ( 14;21 ) corresponded to only 16% ( 97/605 ) of all iROBs , suggesting that in our experimental system , all acrocentrics interact or more fusions are retained without potential selection bias ( Figure S2A and S2B ) . The types and frequencies of iROBs differed over time , such that certain acrocentric associations occurred sooner than others ( Figure S2A and S2B ) . Those involving the larger acrocentric chromosomes ( HSA13 , HSA14 , and HSA15 ) occurred at similar frequencies in both short and long inductions , with the most prevalent iROB being irob ( 13;15 ) ( 16% ) , followed by irob ( 13;14 ) ( 13% ) . Fusions involving HSA21 and HSA22 were more common after 5-day inductions , with the exception of irob ( 15;22 ) . Overall , iROBs occur non-randomly when telomeres are destabilized by dnTRF2 . Some types of iROBs formed first , suggesting that some acrocentrics may be more predisposed to interact with each other or are more sensitive to telomere disruption . Spatial location of nuclear chromosomal territories ( CTs ) within the nucleus influences chromosome interactions and translocation partners , particularly among non-random constitutive genomic exchanges and acquired translocations in cancers [3] , [4] , [47]–[49] . In humans , t ( 11;22 ) is the most prevalent constitutive non-Robertsonian translocation , while t ( 8;14 ) , t ( 8;22 ) , t ( 9;22 ) and t ( 15;17 ) are common acquired rearrangements in cancers [3] , [50] . In our assay , the common constitutive and acquired rearrangements occurred at significantly lower rates than predicted by chance ( Table S3 ) . Interactions between chromosomes distantly located ( HSA18-HSA19 or HSAX-HSA22 ) also did not exceed random fusion rates ( p = 0 . 3 ) ( Figure 2 and Figure S3 , Tables S1 , S2 , and S3; Text S1 ) , implying that the telomeres of these chromosomes were not closely located . Conversely , other fusions involving HSA1 , HSA9 , HSA17 and HSA18 occurred more frequently ( Tables S1 , S2 , and S3 ) . Two-dimensional analysis of CTs using chromosomal painting probes showed that HSA1 , HSA17 and HSA18 were all peripherally located ( Figure S3 ) . Previous studies have shown that these same CTs intermingle [51] , so presumably their telomeres reside near one another . We conclude that spatial characteristics of chromosomes contribute to non-random fusion that may be accentuated by destabilized telomeres . Clearly , this experimental system produced massively heterogeneous populations of chromosomal fusions , opening the possibility to study many aspects of dicentric behavior . Although many non-acrocentric interactions were observed , the number of acrocentric fusions/iROBs still surpassed all others . Thus , as a first step , we concentrated our efforts on this subset of predominant chromosome fusions . Considering the biological basis of the dnTRF2 assay , we expected that most induced dicentrics were telomere-telomere fusions [44] , [46] , [52] . However , ∼20% ( 38/198 ) of iROBs lacked cytologically detectable telomeres at the point of fusion ( Figure 3A , 3A′ , and 3B ) . The remaining iROBs had variable numbers of telomere signals ( 1–3 ) ( Figure S2C ) . Most acrocentric/non-acrocentric fusions ( 60% ) also lacked detectable telomeric sequences at breakpoint junctions ( Figure 3B ) . We next tested for sequences proximal to the telomere . In three independent clones , 32% ( 84/259 ) of iROBs lacked one or more short arm repeats , including rDNA sequences and pß4 ß-satellite arrays ( Figure 3C , 3C′ , 3C″ , and 3D ) . In subclone T19SC3 , one or more of the short arm sequences were missing in half of the iROBs ( 36/71 ) . Whether a particular type of iROB retained all or some repeats appeared non-random ( Figure S2D ) . Most irob ( 13;13 ) and irob ( 13;22 ) maintained all repeats ( 90% and 92% , respectively ) . Overall , though , one or both acrocentric chromosomes in the iROBs lacked one or more short arm repeats ( Figure S2D ) . Consequently , iROBs produced in this assay exhibited variable inter-centromeric distances , conservatively estimated to range from 2Mb to 20Mb . From these experiments , we conclude that dnTRF2 not only affects telomere function , but impacts the stability of acrocentric short arm DNA located several megabases away from the telomere . Unprotected chromosomal ends are recognized as double-strand breaks , triggering a DNA damage response and recruitment of histone variant H2A . X phosphorylated at serine 139 ( γH2AX ) and DNA repair proteins [46] , [53] , [54] . Since many iROBs lacked telomere and short arm sequences , we used immunocytochemistry to determine if DNA breaks occurred in the acrocentric short arm at the time of dicentric formation . We observed the expected increase in γH2AX foci in interphase nuclei after expressing dnTRF2 for 36–48 hours ( Figure S4A , S4B , S4C , S4A′ , S4B′ , and S4C′ ) . Significantly more foci were associated with telomeres compared to control cells ( Figure S4A , S4A′ , and S4D; p<0 . 0001 ) . The number of γH2AX foci that coincided with ß-satellite DNA was also significantly higher in dnTRF2 cells ( Figure S4B , S4B′ and S4D; p<0 . 001 ) . Although DNA damage was more prevalent throughout the nucleus after dnTRF2 expression , we did not observe increased γH2AX at control genomic sites ( non-acrocentric satellite repeats or two euchromatic loci ) ( Figure S4C , S4C′ , and S4D ) , suggesting that the increased damage was specific to telomeres and acrocentric short arms . Telomere detection and breakpoint analyses were performed at the time of iROB formation ( 36–48 hours ) , before cells had progressed through additional cell cycles . Thus , it seems unlikely that ongoing dicentric instability via breakage and re-fusion cycles was responsible for heterogeneous iROB structure . We conclude that iROBs in dnTRF2 cells were formed by telomere-telomere fusion or breakage within acrocentric short arm repeats . To better understand mechanisms of iROB formation in our experimental assay , we considered circumstances under which the five pairs of acrocentric chromosomes preferentially interact . The tandem arrays of ribosomal RNA gene ( rDNA ) clusters or nucleolar organizing regions ( NORs ) are present on each acrocentric short arm . After mitosis , numerous mini-nucleoli arise around the NORs , coalescing into one or a few large nucleoli during interphase that bring the acrocentric short arms into close proximity [10] , [55]–[58] . To test if dnTRF2 perturbed acrocentric associations , we visualized nucleolar organization in control ( HT1080 and uninduced cells ) and dnTRF2-expressing cells by immunostaining for two nucleolar proteins , Ki-67 and fibrillarin on three-dimensionally preserved nuclei ( Figure 4A and 4B ) . HT1080 cells were also studied to exclude the possibility that the presence of the inducible mutant TRF2 construct , even when transcriptionally repressed , affected nucleolar and acrocentric organization . After 36–45 hours of dnTRF2 expression , Ki-67 immunostaining appeared ruffled rather than tightly compacted when compared to control nuclei ( Figure 4A and 4B ) . Fibrillarin antibody staining was even more dramatically altered in dnTRF2 cells ( Figure 4A and 4B ) . It was dispersed throughout the nucleus such that the nucleolus appeared unraveled . The morphology was reminiscent of “nucleolar necklaces” that have been previously described in cells treated with RNA polymerase II inhibitors [59] , [60] . These experiments indicated that dnTRF2 profoundly affected nucleolar integrity . Since nucleolar assembly depends on rDNA within the acrocentric short arm , we also examined the shape and positioning of rDNA and ß-satellite DNA arrays within interphase nuclei . In control nuclei , the arrays appeared as multiple punctate foci ( 90% , n = 30 ) ( Figure 4C ) . After dnTRF2 was expressed for 45 hours , rDNA and ß-satellite FISH signals were diffuse and unraveled ( Figure 4D ) , particularly rDNA . Over 85% of nuclei exhibited dispersed or scattered rDNA and ß-satellite signals . Collectively , these experiments suggest that dnTRF2 expression not only destabilizes telomeres , but leads to nucleolar disruption , short arm satellite instability , and DNA damage within the acrocentric short arms . An advantage of the in vitro assay is the ability to monitor centromere function and dicentric stability immediately after formation . We used immunostaining for various centromere proteins ( CENPs ) to evaluate centromere function over time ( Figure 5A ) [26] , [61] . It is important to note that in each induced line , every cell contained one or more different dicentrics . We analyzed this heterogeneous population of dicentrics that were formed after 40 hours of dnTRF2 expression . Cells were then returned to dox+ media and analyzed again after 4 days and 20 days of continuous culturing ( Figure 5A ) . At 40 hours ( i . e . time of dicentric formation ) , >95% of dicentrics , including iROBs with closely spaced centromeres ( ∼20Mb or less ) and non-acrocentric dicentrics in which centromeres were distantly located ( estimated to be >50Mb apart ) , had two functional centromeres ( Figure 5B , Figure 6A ) . Only one dicentric showed CENP-A at one of the two α-satellite regions , suggesting that centromere inactivation had occurred soon after formation ( Figure 6A ) . Even after 4 days of continuous culture ( ∼4 cell divisions ) , the number of functionally dicentric chromosomes was unchanged ( 97% ) ( Figure 6A ) . These dicentric chromosomes included iROBs and non-acrocentric dicentrics . In fact , 80% of non-acrocentric dicentrics were functionally dicentric , including those with large inter-centromeric distances ( Figure 5C and 5C′ ) . Centromere inactivation was observed at 4 days , but in a minority of fusions ( Figure 6A ) . For instance , a structurally tricentric chromosome lacked CENP-A at one of its three centromeres ( Figure 5C′ ) . The centromeres that remained active were located at opposite ends of the chromosome . At 20 days after dicentric formation , the number of functionally monocentric chromosomes increased from 2–3% to 6% ( Figure 5D″ ) , and the number of functionally dicentric chromosomes decreased slightly ( Figure 6A ) . Nevertheless , even after ∼20 cell divisions , both functionally dicentric iROBs and non-acrocentric fusions were still observed ( Figure 5D and 5D′ ) . A functionally tricentric chromosome was even observed ( Figure 6A ) . We conclude that human chromosomes with two ( or more ) active centromeres are mitotically stable for many cell divisions after their formation . In patient-derived dicentrics , centromere distance is thought to influence whether a dicentric has one or two active centromeres [16] , [62] . Our experimental data support a model for centromere function on newly-formed dicentric chromosomes that is less dependent on centromere distance . Clearly , de novo dicentric behavior is more complex than previously appreciated from studies of patient-derived dicentrics . To monitor longer-term dicentric behavior , the time course experiments were extended . First , the population of dicentrics was enriched for the most prevalent dicentrics . This was achieved by sub-cloning the induced cell lines for several weeks to yield cell lines that contained only a few types of iROBs . At 6 weeks after formation , many induced iROBs had undergone centromere inactivation , but at least half remained functionally dicentric ( Figure 6B ) . We then monitored the number of functionally monocentric and dicentric iROBs in two subclones , T19SC1 and T19SC2 , over the next 14 weeks . We observed functionally dicentric iROBs even after 14 weeks of continuous division ( ∼100 cell divisions ) ( Figure 5E–5E″ , Figure 6B ) , although some iROBs underwent centromere inactivation ( Figure 5F–5F″ , Figure 6B ) . During the 14 weeks , the proportion of functional dicentrics in clone T19SC1 decreased , and in clone T19SC2 , the number of monocentrics increased from 15% to nearly 25% ( Figure 6B ) . Since we evaluated cells that contained more than one iROB and only a subset of cells from the population were assayed at each timepoint , it was important to consider the trend across the entire timecourse . In general , the proportion of functionally dicentric chromosomes did not increase in either clone , and functionally monocentric chromosomes either remained consistent or increased . These results indicated that centromere inactivation occurs within several weeks after dicentric formation . However , many newly formed dicentrics remained functionally dicentric for ∼180 cell divisions . A caveat of the previous experiment is that it broadly evaluated all dicentrics in the cell populations of T19SC1 and T19SC2 . Thus , we monitored centromere function of specific iROBs . These included two independent versions of irob ( 13;14 ) , an irob ( 13;13 ) , and an irob ( 14;14 ) . Other iROBs , such as an irob ( 15;22 ) and several irob ( 22;22 ) , were also evaluated ( data not shown ) . Centromere function was analyzed every 2 weeks by immunostaining for CENP-A , CENP-C , and/or CENP-E ( Figure S5A ) . We observed iROBs that remained functionally dicentric ( Figure 5E–5E″ ) , while others underwent inactivation ( Figure 5F–5F″ ) . Even iROBs involving the same acrocentrics behaved differently over the 14-week period . At the starting point of the timecourse ( 6 weeks after formation ) , one irob ( 13;14 ) was functionally monocentric ( Figure S5A ) and the other was functionally dicentric ( Figure S5B ) . However , both were functionally dicentric in most or all cells at 20 weeks ( Figure S5A and S5B ) . Conversely , other iROBs showed evidence of centromere inactivation . For instance , an irob ( 13;13 ) had already undergone inactivation in half the cells at 6 weeks , but was functionally monocentric in most cells by 14 weeks ( Figure S5C ) . The irob ( 14;14 ) was functionally dicentric at 6 weeks , but had undergone inactivation in nearly all cells at 20 weeks ( Figure S5D ) . These experiments revealed notable differences in centromere function of iROBs , although the molecular basis is still unclear . It is unlikely that the identity of the chromosomes involved in the iROB determines its fate since this experiment revealed that the same type of iROB could behave differently over time . For instance , one irob ( 13;14 ) ( Figure S5B ) was present as a functional dicentric over the entire time course , but the other irob ( 13;14 ) existed in both functionally dicentric and functionally monocentric states within the same cell population ( Figure S5A ) . This might reflect instability of centromere function on the same iROB over time , hierarchical centromere disassembly , or differences in timing of inactivation in each cell . This set of experiments provides evidence that de novo structurally dicentric human chromosomes are mitotically stable in either functionally monocentric or functionally dicentric configurations . The timeframe during which centromere inactivation happens is broad , occurring several weeks to months after dicentric formation . Current models for centromere inactivation , derived from studies in yeast and maize , implicate centromeric deletion or chromatin remodeling at one centromere of a dicentric [63] , [64] . Experimental evidence for the molecular mechanism ( s ) of centromere inactivation in humans has been limited [65] , [66] . Between 4 days and 20 weeks after dicentric formation , we detected small chromosomal fragments in many of the same cells that contained iROBs . Many fragments showed CENP-A antibody staining ( Figure 7A ) . To investigate their genomic origin , we used combined CENP-A immunostaining and FISH to evaluate cells between weeks 6 and 20 . Approximately 60% of all the CENP-A positive fragments contained acrocentric α-satellite sequences ( Figure 7B and 7C ) . The non-acrocentric fragments were most likely derived from other α-satellite arrays or non-centromeric regions that were lost when large dicentrics underwent breakage . The number of fragments increased by 4 days after dicentric formation , peaking at 6 weeks , and then decreased thereafter ( Figure 7C ) . As the number of functionally dicentric chromosomes decreased over time , the proportion of chromosome fragments also declined . In many cells that contained both CENP-A/α-satellite fragments and a dicentric chromosome , the dicentric had undergone inactivation and the identity of the chromosomal fragment corresponded to the inactivated centromere ( Figure 7B ) . Our interpretation of these results is that during centromere inactivation , partial deletion of one α-satellite array occurred . Since α-satellite DNA but not CENP-A was detected at the inactivated centromere , we propose that deletion removed the region of chromatin containing CENP-A nucleosomes . CENP-A is assembled on only a portion ( 30–50%; 0 . 2–2Mb ) of the multi-megabase arrays of α-satellite [38] , [40] ( L . L . Sullivan et al . , unpublished data ) . Removal of the CENP-A portion of an α-satellite array should measurably reduce total array size . To test this hypothesis , we used a quantitative FISH approach in which the intensity and number of pixels from a fluorescent probe hybridized to a centromere is correlated to the size of an α-satellite array ( Figure S6 ) [67] , [68] . Specifically , we measured centromeric probe intensities for two independent irob ( 14;21 ) s . Molecular studies of α-satellite array sizes were considered difficult and most likely inconclusive , since multiple acrocentrics share the same sequence ( i . e 13/21 and 14/22 ) , and there were 3–4 copies of each acrocentric chromosome in the HTC lines . An advantage of the cytological approach is that the chromosomes could be visually identified so that the same centromere and chromosome could be studied before and after dicentric formation . Furthermore , the HSA21 homologues of the HTC lines were easily distinguishable . One pair exhibited a bright , large α-satellite FISH signal ( CEN21L ) , while the other pair had a small FISH signal ( CEN21S ) ( Figure S6 ) . There were also 4 copies of HSA14 in the cells , but the FISH signals appeared the same , suggesting that the α-satellite arrays were similarly sized . However , one HSA14 was structurally abnormal , and contained duplicated material on the distal q arm . We could easily exclude this HSA14 from our analyses , since it was not involved in either irob ( 14;21 ) . The fluorescence intensities of FISH probes for the centromeres of the free-lying acrocentric chromosomes were measured in control ( uninduced ) lines ( Figure S6 ) . Then the CEN14 and CEN21 signals were measured on the irob ( 14;21 ) , and compared to signal intensities from the free-lying centromeres in control cells . Since the CEN21 signals from the two sets of HSA21 homologues were distinctive , it was obvious which HSA21 was involved in both iROBs . In both irob ( 14;21 ) s , the HSA21s with CEN21L remained free-lying ( Figure S6 ) , implying that one HSA21 with CEN21S was involved in the iROB . CEN21ROB FISH signal intensities were significantly smaller than both CEN21L and CEN21S ( p<0 . 05 ) , while active CEN14 intensities were unchanged ( p>0 . 1 ) ( Figure 8A and 8B ) . As controls for this assay , we measured α-satellite intensities on two independent irob ( 13;14 ) s that were functionally dicentric . The fluorescence intensities of CEN13 and CEN14 before and after dicentric formation were similar ( Figure 8C and data not shown ) . Partial α-satellite deletion may not be the only mechanism of inactivation since one irob ( 15;22 ) did not exhibit any differences in α-satellite FISH intensities/array size between CEN15 before and after its involvement in the iROB ( Figure 8D ) . This centromere was very small on the free-lying HSA15 , so perhaps there is a size threshold below which centromeric deletion is less likely to occur . We conclude that CEN15 is inactivated via an epigenetic-dependent mechanism , which is in agreement with studies of other human dicentric chromosomes [69] . Overall , these experiments suggest several mechanisms of centromere inactivation , one of which involves deletion of α-satellite DNA associated with CENP-A .
After dnTRF2 induction , unprotected acrocentric chromosome ends would appear as double-strand breaks ( DSB ) s to be repaired by non-homologous end joining ( NHEJ ) , presumably using the nearest neighbor which would be another acrocentric . Surprisingly , one-fifth of the iROBs lacked visible telomeric repeats at their fusion points , suggesting an alternative or more complex mechanism of fusion . Although small amounts of telomeric repeats might be present below the level of FISH detection , the absence of repetitive sequences immediately adjacent to the telomere provided compelling evidence that telomeric DNA , as well as other acrocentric short arm DNA , had been deleted during dicentric formation . How the DNA damage is repaired is not clear . Heterogeneity in the amount of short arm repeats retained on each iROB suggests a mechanism of NHEJ . However , we cannot discount that more complex mechanisms of homologous or heterologous recombination are also involved . Studies in other organisms have illustrated that NHEJ , recombination , or break-induced replication ( BIR ) can result in compound genomic signatures on end-to-end fusions and dicentric chromosomes [20] , [70]–[72] . In our system , once dicentrics formed , acrocentric short arm composition did not noticeably change , even after months in culture , arguing against a model of molecular heterogeneity due to ongoing breakage , re-fusion and reorganization of iROB short arms . Although small rearrangements might have existed below the detection of FISH , the most consistent interpretation of our data is that in addition to telomere function , mutant TRF2 impacts acrocentric short arm stability . A major challenge of this experimental system is that inducible , parental cell lines contain >15 acrocentric chromosomes , and molecular identification of precise breakpoints is difficult due to shared satellite DNA homologies and complicated arrangements of repeat blocks . Abnormal nucleolar morphology and dispersal of acrocentric short arm satellite repeats in nuclei of dnTRF2-expressing cells suggested a potential extra-telomeric role for TRF2 . TRF2 is transiently associated with the nucleolus during the cell cycle , and when its release is prevented by the RNA polymerase inhibitor actinomycin D , chromosome end-to-end fusions occur [73] . Movement of TRF2 to and from the nucleolus may ensure proper nuclear and chromosome architecture , although it is unclear if TRF2 directly interacts with acrocentric DNA and/or nucleolar proteins or regulates rDNA transcription . Acrocentric telomere sequences cluster around the nucleolar periphery [74] , so simple proximity of the telomere to the acrocentric short arms may explain why TRF2 is detected at the nucleolus . Still , TRF2 has been reported to bind at non-telomeric sites on acrocentric short arms , near sites of upstream binding factor ( UBF ) and B23/nucleophosmin [73] . UBF and other nucleolar proteins from the previous cell cycle form the nucleolus in the subsequent cell cycle [75] , so dnTRF2 may disrupt putative TRF2-UBF interactions , UBF-acrocentric arm associations , or even TRF2-acrocentric DNA interactions . It remains to be determined if nucleolar disruption results from dnTRF2-induced telomere dysfunction , unstable acrocentric short arm sequences , or formation of multiple acrocentric fusions that lack rDNA . We have observed that acrocentric fusions form non-randomly in human cells transiently expressing exogenous Cre recombinase ( K . M . Stimpson and B . A . Sullivan , unpublished observation ) . In these cells , nucleolar organization and assembly remain intact , so we conclude that iROB formation can be discounted as the primary mechanism responsible for nucleolar defects observed in the present study . Dicentric chromosomes in many organisms undergo classical breakage-fusion-bridge cycles [19] , [21] . For instance , engineered Drosophila dicentrics are unstable and break during mitosis [20] , [76] , although they can segregate accurately in female meiosis [77] . In budding yeast , dicentric plasmids or linear chromosomes are also unstable , but become less so under conditions in which one centromere is deleted , inter-centromeric distances are decreased , or transcription is forced through the centromere [43] , [63] , [78] . Centromere inactivation has not been described to occur naturally in yeast , but is more frequent in human dicentrics [62] , [66] . As such , former conclusions drawn about dicentric instability in model organisms may not have revealed similarities in dicentric behavior between plants and mammals . Indeed , recent studies in plants have suggested that centromere inactivation occurs at formerly under-appreciated frequencies [64] . In these studies , dicentric stability was accompanied by chromosome breakage , a phenomenon that we also observed in our study . We also observed centromeric deletion as a mechanism of dicentric stabilization . Our studies emphasize several parallels in dicentric behavior among model organisms and humans . An aspect of dicentric behavior that appears to be unique to humans is the observation that dicentrics often exist as functionally dicentric chromosomes [62] , [66] , [79] . In patient-derived dicentrics ( i . e . dicentric Xs and many de novo ROBs ) short inter-centromeric distances have been proposed to influence centromere function , so that dicentrics with closely spaced centromeres are more likely to remain functionally dicentric . However , over 80% of patient-derived ROBs undergo centromere inactivation and even dicentric Xs with closely spaced centromeres experience centromere inactivation [16] , [26] , [80] . So do inter-centromeric distance and dicentric centromere function correlate exactly or randomly ? Our present study explored this question , as the largest estimated distance between the centromeres of some iROBs was ∼20Mb . Such short inter-centromeric distances might explain why functionally dicentric iROBs were maintained for up to 6 months . Nevertheless , about half of the iROBs underwent centromere inactivation , even those in which centromeres were maximally separated . Even more compellingly , fusions involving non-acrocentric chromosomes also remained functionally dicentric ( or tricentric ) for up to 20 cell divisions . The centromeres were estimated to be at least 50Mb apart , yet these dicentrics were retained for several weeks . In fact , extensive chromosome fragmentation or breakage was not observed until 6 weeks after dicentric formation . Our studies of de novo dicentrics argue that centromeric distance is not the strongest predictor of the functional state of a dicentric chromosome . Centromere inactivation in iROBs might rely instead on chromosome-specific features or may occur differently in each cell . It is possible that induced dicentrics with larger inter-centromere distances eventually undergo inactivation , as observed in patient-derived dicentrics , or experience breakage as predicted from model organisms . Future long-term studies using this and other experimental systems should address these questions in more detail . We observed that centromere inactivation occurred 2–20 weeks after dicentric formation . This timeframe is consistent with studies of maize dicentric chromosomes that undergo centromere inactivation at 10 weeks after formation [22] , [64] . However , dicentrics can also exhibit more complex patterns of centromere function . Dicentric human chromosomes are sometimes present in both functionally dicentric or monocentric states within the same individual or cell line [62] , [66] . Some induced dicentrics also exhibited this behavior , and in these cases , the same centromere lacked CENP-A , -C or -E staining when the dicentric was in the functionally monocentric configuration . Centromeres have been reported to change functional states in both fission yeast and clonal lines of isodicentric Xs [66] , [69] , [81] . Centromere switching in human dicentrics has been defined as the presence of the dicentric in functionally monocentric and dicentric states and is considered more prevalent in dicentrics that have genomically identical centromeres [69] . However , we observed variable centromere states in different cells and at different times for induced dicentrics with non-homologous centromeres . The biological mechanism for this phenomenon is unclear . Perhaps inactivation had not occurred completely at a given assay point . Centromere disassembly may occur in stages and centromere proteins for which we had not assayed might have been maintained on some versions of the iROBs . A model for hierarchical centromere inactivation in which centromere proteins are lost sequentially has emerged from a recent study of conditional centromeres on human artificial chromosomes [82] . Alternatively , timing of inactivation might vary among cells , so that our observations reflect differences in the number of cells containing a dicentric that had or had not undergone inactivation . Future experiments are required to distinguish between models of centromere switching , incomplete centromere inactivation , or cell-specific differences in the timing of centromere inactivation . Genomic deletion has been implicated in inactivation of yeast dicentrics and inferred from studies of patient-derived dicentric Y chromosomes [65] , [67] . However , since this is not the case for dicentric X chromosomes [69] , it was unclear if such a mechanism might be yeast or Y-specific . Our study provides the first experimental evidence that newly-formed dicentrics in human cells can be stabilized by centromeric deletion . A notable difference between this type of mechanism in yeast and humans is that α-satellite DNA , the genomic marker of the human centromere , was not completely removed during inactivation . Instead , only the portion associated with CENP-A and what presumably identifies the site of kinetochore assembly , appeared to be eliminated ( Figure S7 ) . Spatial and temporal incorporation of the centromeric H3 variant CENP-A maintains the location and function of the centromere . CENP-A is at the top of the centromere assembly hierarchy , recruiting other centromere and kinetochore proteins . Newly synthesized CENP-A is loaded into chromatin in late telophase/early G1 by the escort protein HJURP [83] , [84] . Thus , removal of CENP-A , and other centromere/kinetochore proteins , from a centromere destined for inactivation must occur in addition to blocking the loading of new CENP-A . It is not known if existing CENP-A nucleosomes , recruitment of additional factors , or H3-containing nucleosomes within centromeric chromatin guide incorporation of new CENP-A or if such factors are recognized by HJURP or intermediates . It would be consistent with any of these models if centromere inactivation occurred by simultaneously deleting existing CENP-A and nearby accessory chromatin that target new CENP-A deposition . This study provides evidence for a genomic mechanism of centromere inactivation that occurs in some dicentrics . Future studies will be important for determining the molecular basis for which inactivation pathway ( genomic versus epigenetic ) is taken . Events that initiate centromere inactivation and influence the fate of a dicentric may be triggered randomly , or by chromosome-specific features , such as α-satellite array size . The ability to produce de novo dicentrics in human cells should reveal additional insights into molecular mechanisms of centromere inactivation and dicentric behavior and offer a means by which centromere inactivation can be directly manipulated or perturbed .
HTC75 T19 and T4 clonal cell lines containing the Tet-inducible truncation allele of TRF2 ( ΔBΔM ) [44] were cultured in MEM alpha ( Invitrogen ) supplemented with 10% FBS , antibiotics ( Invitrogen ) , 5 mM filter-sterile glucose , and 100ng/ml doxycycline hyclate ( Fluka ) . Inductions of dominant-negative TRF2ΔBΔM expression were carried out in doxycycline-free media for 24–40 hours , 3 days , and 5 days . Metaphase chromosomes were harvested using methanol∶acetic acid fixation ( 3∶1 v/v ) . Metaphase spreads were prepared as previously described [26] . For IF on nuclei , cells were grown on glass slides . Cells were fixed in 4% paraformaldehyde in PBS and in the case of nuclei preps , permeabilized with PBS+Triton X-100 . Antibodies included: mouse monoclonal anti-CENP-A antibodies ( Abcam ab13939; 1∶500 ) , rabbit polyclonal anti-CENP-A antibodies ( Upstate 30217; 1∶200 ) , rabbit polyclonal anti-CENP-B ( Abcam 25734; 1∶400 ) , mouse monoclonal anti-CENP-C ( Abcam ab50974; 1∶200 ) , mouse monoclonal anti-CENP-E antibodies ( Abcam ab5093; 1∶200 ) , mouse monoclonal to TRF2 ( Imgenex; IMG-124A , 1∶200 ) , mouse monoclonal to fibrillarin ( Abcam ab18380; 1∶1000 ) , rabbit polyclonal Ki67 ( Novocastra; 1∶500 ) , and rabbit polyclonal to gamma H2A . X phospho S139 ( Abcam ab2894 , 1∶400 ) . Antibodies were detected with donkey anti-mouse or anti-rabbit secondary antibodies conjugated to Alexa Fluor 488 ( Molecular Probes ) , Cy3 or Cy5 ( Jackson Immunoresearch , Inc . ) . ß-satellite repeats were detected using plasmid pß4 [8] , satellite III DNA with plasmids pTRS-47 and pTRS-63 , and HSA15 α-satellite DNA with pTRA-20 . Biotinylated HSA15 satellite III probe ( D15Z1 ) was from Oncor , Inc . Probes for 18s rDNA , HSA13/21 α-satellite , and HSA 14/22 α-satellite were created from cloned PCR products [85]–[87] . Plasmids were labeled with biotin-16-dUTP and digoxygenin-11-dUTP ( Roche ) by nick-translation . Whole-arm chromosome-specific DNA was amplified by PCR and labeled with biotin or digoxygenin [88] ( Text S1 ) . Telomere repeats were detected with biotin-labeled LNA probe ( T2AG3 ) 3 ( Exiqon ) or FITC-conjugated PNA probe ( C3TA2 ) 3 ( Biosynthesis ) . FISH and IF-FISH were performed as described [26] . Metaphase chromosomes were denatured in 70% formamide/2× SSC pH 7 at 73°C for 2 minutes ( conventional FISH ) or at 80°C for 8 minutes ( IF-FISH ) . Chromosome painting probe hybridization mixtures contained 10mg/mL Cot-1 DNA . Denatured Cot-1 DNA and painting probes were pre-annealed at 37°C after denaturation . Probes were detected with Cy3- , Cy5-conjugated anti-digoxin ( Jackson Immunoresearch ) , or Alexa Fluor 488-streptavidin antibodies ( Molecular Probes ) . Images were acquired on an inverted Olympus IX-71 attached to the Deltavision RT restoration imaging system ( Applied Precision , Inc . ) equipped with a Photometrics CoolSNAP HQ CCD camera . Images were captured using the SoftWoRx Acquire 3D software using 40× ( N . A . 1 . 35 ) , 60× ( N . A . 1 . 42 ) , or 100× ( N . A . 1 . 40 ) oil objectives ( Olympus ) . Images were collected as z-stacks of 0 . 1–0 . 5mm increments ( 1–15 sections total ) , depending on the fixation technique . Image stacks were deconvolved using 10 iterations using a conservative algorithm , then collapsed using the Quick Projection option . Projections were converted to Adobe Photoshop for viewing and analysis . Multi-color FISH was used to hybridize α-satellite probes recognizing the centromeres of HSA13/21 , HSA14/22 and HSA15 to metaphase chromosomes from control cells ( HT1080 , HTC75 T19 uninduced ) and 36-hour induction subclones T19SC1 and T19SC2 . Digital images were collected using an epifluorescence microscope ensuring that no signal reached pixel saturation . Pseudo-colored three-color ( RGB ) images were separated into individual wavelengths for the green ( 488nm ) and red ( 568nm ) channels . Individual centromere signals from the same image were segmented by interactive intensity thresholding ( via segmentation command ) in IPLab/iVision ( BioVision Technologies ) . The fluorescence intensity/integrated density ( i . e . the sum of the values for all pixels within the region defined as the α-satellite array for each centromere ) that corresponded to FISH signal intensity was measured in each segmented area/centromere . Integrated densities ( ID ) of centromeres on free-lying acrocentrics ( in control and dnTRF2 cells ) and iROB centromeres were collected from multiple images from the same experiment and compiled as arbitrary fluorescence units ( AFUs ) . AFUs for the inactivated centromere were then compared to AFUs of α-satellite signal intensities for that particular centromere when on free-lying chromosomes . Chromosomal interactions after dnTRF2 induction were plotted and displayed using Circos program [89] . Statistical significance of the proportion of fusions formed between induction timepoints was determined using a Students t-test at a confidence interval of 95% . Datasets including fluorescent signal intensities of centromeres on free-lying chromosomes and on chromosomes after iROB formation were tested for significance using parametric ( t-test ) and nonparametric tests ( Mann-Whitney U test ) . Reported P values in Figure 8 were derived from Mann-Whitney tests . P values indicating the significance of specific chromosomal fusions were calculated using 2×2 contingency tables of observed versus expected ratios and the Chi-Square ( χ2 ) test . The t-tests , Mann-Whitney U tests , and χ2 tests were performed using either Excel ( Microsoft Corporation ) , Graph Pad Statistics software available online ( http://www . graphpad . com/quickcalcs/index . cfm ) or SOCR ( Statistics Online Computational Resource; http://www . socr . ucla . edu/ ) . A p value that was less than 0 . 05 was considered statistically significant .
|
Endogenous human centromeres are defined by large arrays of α-satellite DNA . A portion of each α-satellite array is assembled into CENP-A chromatin , the structural and functional platform for kinetochore formation . Most chromosomes are monocentric , meaning they have a single centromere . However , genome rearrangement can produce chromosomes with two centromeres ( dicentrics ) . In most organisms , dicentrics typically break during cell division; however , dicentric human chromosomes can be stable in mitosis and meiosis . This stability reflects centromere inactivation , a poorly understood phenomenon in which one centromere is functionally silenced . To explore molecular and genomic events that occur at the time of dicentric formation , we describe a cell-based system to create dicentric human chromosomes and monitor their behavior after formation . Such dicentrics can experience several fates , including centromere inactivation , breakage , or maintaining two functional centromeres . Unexpectedly , we also find that dicentrics with large ( >20Mb ) inter-centromeric distances are stable through at least 20 cell divisions . Our results highlight similarities and differences in dicentric behavior between humans and model organisms , and they provide evidence for one mechanism of centromere inactivation by centromeric deletion in some dicentrics . The ability to create dicentric human chromosomes provides a system to test other mechanisms of centromere disassembly and dicentric chromosome stability .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cell",
"biology/cell",
"growth",
"and",
"division",
"molecular",
"biology/centromeres",
"molecular",
"biology/chromosome",
"structure",
"genetics",
"and",
"genomics/chromosome",
"biology",
"genetics",
"and",
"genomics/epigenetics"
] |
2010
|
Telomere Disruption Results in Non-Random Formation of De Novo Dicentric Chromosomes Involving Acrocentric Human Chromosomes
|
The tapeworm Echinococcus granulosus is responsible for cystic echinococcosis ( CE ) , a cosmopolitan disease which imposes a significant burden on the health and economy of affected communities . Little is known about the molecular mechanisms whereby E . granulosus is able to survive in the hostile mammalian host environment , avoiding attack by host enzymes and evading immune responses , but protease inhibitors released by the parasite are likely implicated . We identified two nucleotide sequences corresponding to secreted single domain Kunitz type protease inhibitors ( EgKIs ) in the E . granulosus genome , and their cDNAs were cloned , bacterially expressed and purified . EgKI-1 is highly expressed in the oncosphere ( egg ) stage and is a potent chymotrypsin and neutrophil elastase inhibitor that binds calcium and reduced neutrophil infiltration in a local inflammation model . EgKI-2 is highly expressed in adult worms and is a potent inhibitor of trypsin . As powerful inhibitors of mammalian intestinal proteases , the EgKIs may play a pivotal protective role in preventing proteolytic enzyme attack thereby ensuring survival of E . granulosus within its mammalian hosts . EgKI-1 may also be involved in the oncosphere in host immune evasion by inhibiting neutrophil elastase and cathepsin G once this stage is exposed to the mammalian blood system . In light of their key roles in protecting E . granulosus from host enzymatic attack , the EgKI proteins represent potential intervention targets to control CE . This is important as new public health measures against CE are required , given the inefficiencies of available drugs and the current difficulties in its treatment and control . In addition , being a small sized highly potent serine protease inhibitor , and an inhibitor of neutrophil chemotaxis , EgKI-1 may have clinical potential as a novel anti-inflammatory therapeutic .
The dog tapeworm Echinococcus granulosus is one of a group of medically important parasitic helminths of the family Taeniidae ( Platyhelminthes; Cestoda; Cyclophyllidea ) . Its life cycle involves two mammals: an intermediate host , usually a domestic or wild ungulate , with humans being accidental hosts , and a canine definitive host such as the domestic dog . The larval metacestode stage causes cystic echinococcosis ( CE ) ( hydatidosis; cystic hydatid disease ) , a chronic cyst-forming disease in the intermediate/human host [1] . Hermaphrodite adult worms of E . granulosus reside in the small intestine of canines and pass eggs containing embryos ( oncospheres ) in feces . Following ingestion by a human or an intermediate host such as a sheep , the egg hatches in the intestine to release the oncosphere which penetrates through the gut wall and is carried in the blood system to various internal organs , mainly the liver or lungs , where it develops into a hydatid cyst . Dogs and other canines get infected by eating offal with fertile hydatid cysts containing larval protoscoleces . These larvae evaginate , attach to the gut , and develop into 3–6 mm long adult parasites which reach sexual maturity 4–5 weeks later [2] . The molecular mechanisms whereby the adult worms are able to survive in the dog gut without being damaged from host intestinal proteases and how oncospheres evade host immune attack in the blood system remain largely unknown . However , the recent deciphering of the E . granulosus genome and transcriptome [3 , 4] provides insights as to how these processes might occur . Kunitz type proteins , which belong to the I2 family of protease inhibitors , have been characterized from many organisms including sea anemone [5] , cone snail [6] , scorpion [7] , spider [8] , ticks and biting flies [9 , 10] , parasitic helminths [11–13] and mammals [14] . Bovine pancreatic trypsin inhibitor ( BPTI ) is the classic member of this family of proteins and was the first Kunitz-type protease inhibitor described [15] . In invertebrates , Kunitz inhibitors are involved in various physiological processes such as blood coagulation , fibrinolysis , inflammation and ion channel blocking with or without protease inhibition [16] . These proteins possess one or more Kunitz domains; the Kunitz-type motif consists of around 60 amino acids and has six conserved cysteine residues which connect with three disulphide bonds in a characteristic pattern ( C1-C6 , C2-C4 , and C3-C5 ) . The C1-C6 and C3-C5 bonds are required for the maintenance of native confirmation [17] whereas the C2-C4 bond stabilizes the folded structure [18] . Position P1 [19] of the reactive site is the major determinant of the energetic and specificity of protease recognition by Kunitz inhibitors [20] . A previous study described a multigene family of eight ( EgKU1-EgKU8 ) secreted monodomain Kunitz proteins from E . granulosus protoscoleces preferentially expressed by pepsin/H ( + ) -treated worms [21] . Structural modeling revealed EgKU1 was a cation-channel blocker but only EgKU8 behaved as a potential protease inhibitor suggesting the majority of these Kunitz proteins were involved in functions other than protease inhibition [21] . By interrogation of the available genome sequence data for E . granulosus [3 , 4] we identified two gene sequences ( designated EgKI-1 and EgKI-2 ) encoding two polypeptides similar to single domain Kunitz proteins . The two cDNAs were expressed in Escherichia coli , and the recombinant proteins ( rEgKI-1 and rEgKI-2 ) were purified and functionally characterized . EgKI-2 reacted as a typical trypsin inhibitor , whereas EgKI-1 , a potent inhibitor of chymotrypsin and neutrophil elastase , was able to significantly reduce neutrophil infiltration in the λ-carrageenan mouse air pouch model of local inflammation , and is the first Kunitz type serine protease inhibitor shown to bind calcium .
Two nucleotide sequences ( EgKI-1 and EgKI-2 ) , encoding single domain Kunitz type protease inhibitors , were identified by interrogation of available E . granulosus genomic sequence for Kunitz domains . Searches for similar nucleotide sequences were performed using BLAST ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) on the NCBI ( National Centre for Biotechnology Information ) web site . The presence of a signal sequence in both proteins was checked using signalP 4 . 1 ( http://www . cbs . dtu . dk/services/SignalP/ ) [22] . Protein domains were identified by searching the PROSITE database ( http://prosite . expasy . org/ ) [23] and multiple sequence alignment was generated with the Clustal Omega program ( http://www . ebi . ac . uk/Tools/msa/clustalo/ ) [24] . Protein structure prediction was performed with the Phyre2 online program ( www . sbg . bio . ic . ac . uk/phyre2/ ) [25] and binding site predictions were carried out with 3DLigandSite ( http://www . sbg . bio . ic . ac . uk/3dligandsite/ ) [26] . Other amino acid sequences of E . granulosus , homologous to Kunitz proteins , were searched by blast using the GeneDB online database ( http://www . genedb . org/Homepage/Egranulosus ) . Cladogram phylogenetic analysis was performed with Phylogeny . fr ( http://www . phylogeny . fr/ ) [27] . PCR primers with introduced N-terminal 6×His tag and NcoI and EcoRI restriction sites , ( EgKI-1: FW- CATGCCATGGCACATCATCATCATCATCACGAAGAGGATGTCTGCAACCTACC , RV- GATCGAATTCCTATAAGTGCAAATTTTTAACACAAGCAC; EgKI-2: FW- CATGCCATGGCACATCATCATCATCATCACCTTCACAGAGACTGCAAGGATCC , RV- GATCGAATTCCTAGGCGATGGAGCATTGG ) were designed and produced by Sigma Aldrich ( St . Louis , MO , USA ) . Both EgKI-1 and EgKI-2 were PCR-amplified using cDNA from adult worms and MyTaq DNA polymerase . Purified PCR products were digested with the restriction enzymes NcoI and EcoRI and ligated into the pET28a expression vector . Recombinant protein production was induced after transforming the plasmids containing the EgKI genes into E . coli BL21 ( DE3 ) cells . Recombinant protein production , refolding and purification were carried out as described [11] . The concentrations of purified EgKI-1 and EgKI-2 were determined using the Bradford assay and the proteins were stored at -80°C . Aliquots of the recombinant EgKI proteins were subjected to electrophoresis on 15% ( w/v ) sodium dodecyl sulphate ( SDS ) polyacrylamide gels and stained with Coomassie Blue to determine their purity and relative molecular mass . Preparations of cDNA from adult worms ( AW ) , protoscoleces ( PSC ) , hydatid cyst membranes ( HCM ) and oncospheres ( ONC ) were used for real time PCR ( qPCR ) ; primers ( EgKI-1: FW- CGAAGAGGATGTCTGCAACC , RV- TCCACAACCACCGTAGATGA; EgKI-2: FW- ACTGCAAGGATCCCATTGAC , RV- TCCTCCAGCGTCTCAAAGTT ) were designed using the online primer design software , Primer3 ( http://simgene . com/Primer3 ) . Each cDNA sample ( 25 ng per reaction ) was tested in quadruplicate and all reactions were performed twice . E . granulosus eukaryotic translation initiation factor ( Eg-eif ) was used as housekeeping gene for the normalization of data . The confidence threshold ( CT ) of the second results set was normalized to the first set before evaluation by importing the standard curve of the first set , to the second . The results were analyzed using Rotor-Gene 6000 software . Purified recombinant EgKI-1 and EgKI-2 were dialyzed in PBS using 3500 MWCO Slide-A-Lyzer dialysis cassettes following the manufacturer’s instructions . Antiserum production was undertaken using six Swiss mice per protein . For the first immunization each mouse was inoculated subcutaneously with 50 μg of protein ( dissolved in 50 μl PBS ) emulsified with an equal amount of Freund’s complete adjuvant . Subcutaneous boosts of 50 μg of protein ( dissolved in 50 μl PBS ) , emulsified with the same amount of Freund’s incomplete adjuvant , were undertaken on two occasions at two weekly intervals . Mice were bled for serum one week after the third immunization and the presence of serum anti-EgKI antibodies was confirmed by Western blotting . EgKI-1 , EgKI-2 , BPTI ( Roche diagnostics , Mannheim , Germany ) , urinary trypsin inhibitor ( UTI ) ( Prospec-Tany TechnoGene Ltd , Ness Ziona , Israel ) and SmKI protein , 0 . 5 μg each , were fractioned on a 15% ( w/v ) SDS-PAGE gel and transferred to Immun-Blot low fluorescence-PVDF membrane . Overnight blocking was performed with Odyssey buffer at 4°C . Then , the membrane was subjected to incubation with the mouse anti-EgKI-1 or -EgKI-2 anti-serum ( 1:2 , 000 dilution in Odyssey buffer and 0 . 1% Tween-20 ) for 1 h followed by incubation with IRDye-labeled rabbit anti-mouse antibody ( LI-COR Biosciences ) ( 1:15 , 000 diluted in Odyssey buffer with 0 . 1% Tween-20 and 0 . 01% SDS ) for 1 h on a shaker in a dark chamber . After a final wash with distilled water , the membrane was allowed to dry and visualized using the Odyssey imaging system . Western blotting was carried out with both anti-EgKI-1 and anti-EgKI-2 antibodies using soluble native extracts of AW , PSC , HCM and ONC as described [28] . Paraffin blocks were made by embedding AW , HCM and PSC fixed in 10% phosphate buffered formalin in wax-filled moulds . Sections ( 4 μm ) of these paraffin blocks were then adhered onto microscope slides . Following de-paraffinization and rehydration , antigen retrieval was done with RevealtA solution ( Biocare Medical , Concord , CA , USA ) . Then the tissue sections were blocked with 1% ( v/v ) bovine serum albumin in Tris buffered saline ( TBS ) for 1 h and incubated with the mouse anti-EgKI-1 or -EgKI-2 anti-serum ( 1:200 ) at 4°C overnight . After washing with TBS-T ( TBS with 0 . 1% Tween20 ) , the sections were incubated with Alexa Fluor 488 goat anti-mouse IgG ( 1:500 ) ( Invitrogen , Carlsbad , USA ) at 37°C for 60 min . Nuclei in the tissue sections were counterstained with DAPIgold ( Invitrogen , Carlsbad , USA ) and observed under an EVOS fluorescence microscope . The inhibitory activity of the two recombinant EgKI proteins was tested using several commercially available mammalian serine proteases . Enzyme and EgKI protein mixture ( ranging from 20 pM to 200 mM ) were first incubated together in 96 well plates at 37°C for 10 min . Subsequently a chromogenic or fluorogenic substrate was added at concentrations ranging from 100 mM to 5 μM and product release was measured using a plate reader every min for 30 min . Bovine pancreatic trypsin , bovine pancreatic α-chymotrypsin and the fluorogenic substrates Nα-Benzoyl-L-arginine-7-amido-4-methylcoumarin hydrochloride and N-Succinyl-Ala-Ala-Pro-Phe-7-amido-4-methylcoumarin were purchased from Sigma Aldrich ( St Louis , USA ) . Trypsin and chymotrypsin assays were performed in 200 mM Tris-HCl ( pH 8 . 2 ) containing 20 mM CaCl2 and 0 . 1% PEG 8000 . The kinetic rate of substrate hydrolysis was measured at excitation/emission wavelengths of 370/460 nm with a fluorescence microplate reader . Inhibitory activity of porcine pancreatic elastase ( PPE ) was observed using the Enzcheck elastase assay kit ( Life technologies , Carlsbad , USA ) following the manufacturer’s instructions . Fluorescence signals were measured at 505/515 nm . Neutrophil elastase , Cathepsin G and Proteinase 3 , with corresponding substrates N-Methoxysuccinyl-Ala-Ala-Pro-Val-7-amino-4-methylcoumarin , Suc-Ala-Ala-Pro-Phe-pNA and Boc-Ala-Ala-Nva-SBzl , respectively , were purchased from Enzolifesciences ( NY , USA ) . The neutrophil elastase ( NE ) inhibition assay was carried out with buffer containing 100 mM HEPES , 300 mM NaCl and 0 . 05% Tween-20 ( pH 8 ) with 2 . 5 nM enzyme and fluorescence signals were detected at 370/460 nm . Cathepsin G activity was determined in 100 mM Tris-HCl , 1 . 6 M NaCl buffer ( pH 7 . 5 ) with 100 nM enzyme and release of Pro-Phe-pNA was measured at 405 nm . Buffer containing 100 mM HEPES pH 7 . 5 , 500 mM NaCl , 10% DMSO was used to detect Proteinase 3 activity and substrate hydrolysis was detected at 412 nm following the addition of 170 μM 5 , 5'-dithiobis ( 2-nitrobenzoic acid ) ( DTNB ) . The percentage of the relative activity of the EgKI proteins was calculated using the formula: Percentage of relative activity = ( ΔRU of inhibitor/ ΔRU of enzyme control ) × 100% Δ Relative fluorescence/ absorbance unit ( RU ) = R2—R1 , Readings R1 and R2 were taken at t1 and t2 time points respectively , when the reaction was in the linear range . The data obtained with different substrate concentrations ( [S] ) and several inhibitor concentrations ( [I] , each EgKI protein ) were fitted into the substrate-velocity curves of competitive enzyme inhibition . Ki values were calculated using Graph Pad Prism version 6 . 02 software by nonlinear regression according to the formula; v = Vmax [S]/ Km ( 1+ [I]/Ki ) + [S] , where v is velocity , Vmax is the maximum velocity in the absence of inhibitor , and Km is the Michaelis constant of the substrate . Values were corrected after subtracting background signals and all experiments were performed in triplicates . The clotting of blood involves multiple serine proteases so we performed two standard tests ( activated partial thromboplastin time , APTT; and the prothrombin time , PT ) to determine whether the two recombinant EgKIs had any effect on the intrinsic and the extrinsic pathways of coagulation . Fresh healthy human blood ( 30 ml ) was collected into sodium citrate vacutainers and the plasma was separated . Then , 800 μl plasma was mixed with 50 μl of each EgKI protein ( final concentrations of 200 pM , 200 nM and 2 μM for EgKI-2 and 200 pM , 200 nM and 10 μM for EgKI-1 ) and incubated in a 37°C water bath for 10 min . After adding CaCl2 to the mixture , the time taken for clot formation was measured by a Sta-R coagulometer ( Diagnostica stago , Asnières , France ) . TriniCLOT APTT HS ( Trinity Biotech , Bray , Co Wicklow , Ireland ) and Thromborel S ( Siemens , Malvern , PA , USA ) kits were used for the determination of the APTT and the PT , respectively . Aprotinin ( Sigma Aldrich , St Louis , USA ) and FVII negative plasma ( Helena Laboratories , Texas , USA ) were used as positive controls for APTT and PT , respectively . As the 3DLigandsite predicted that EgKI-1 would bind calcium , a calcium binding assay was carried out using a published procedure [29] with minor modifications to confirm the prediction . Recombinant EgKI-1 , recombinant EgKI-2 and bovine serum albumin ( BSA ) ( New England BioLabs ) , as positive control , were separated on 15% ( w/v ) SDS-PAGE gels and the electrophoresed proteins transferred to a PVDF membrane . The membrane was washed with wash buffer ( 10 mM imidazole , 60 mM potassium chloride , 5 mM magnesium chloride , pH 6 . 8 ) for 1 h at 37°C with gentle shaking . After rinsing with distilled water , the blot was incubated with 1 mM CaCl2 for 1 h . Following washing ( 3X ) with 20% ( v/v ) ethanol and a final wash with distilled water , the membrane was incubated with 1 mM Quin-2 ( AM ) ( Sigma Aldrich ) for 1 h . Subsequently the membrane was washed with distilled water and visualized using a UV transilluminator ( Vilber Lourmat ) at 365 nm . Quin-2 emits fluorescence signals in the presence of Ca++ , enabling detection of proteins on the PVDF membrane bound to calcium . The subcutaneous air pouch model , which is an in vivo model that can be used to study acute and chronic inflammation , was used according to a published protocol [30] . Briefly , female BALB/c mice weighing <25g were anesthetized with isoflurane and a subcutaneous dorsal pouch was made in each animal by injecting 3 ml sterile air . The pouch was reinjected with 1 . 5 ml sterile air , after two days . On the sixth day , a 15 μM protein sample ( rEgKI-1 or the anti-inflammatory drug Ulinastatin [Prospec-Tany TechnoGene Ltd , Ness Ziona , Israel] as positive control: 200 μl ) in PBS or PBS alone as negative control was injected into the pouch . An LPS control ( L2880; Sigma Aldrich ) was also included and consisted of 12 μg of LPS , which was an amount equivalent to the calculated LPS contamination of the bacterially produced rEgKI-1 protein measured using a Pierce LAL Chromogenic Endotoxin Quantitation Kit ( Thermo Fisher Scientific Inc . , IL , USA ) with a sensitivity of 0 . 1 EU/ml ( approximately 0 . 01 ng endotoxin per ml ) . After 30 mins , 300 μl of 1% ( w/v ) lambda ( λ ) carrageenan ( Sigma Aldrich ) , which acts as an inflammatory stimulus [31] , in sterile saline , was injected into the air pouch . On the following day , mice were euthanized and the pouches were washed with 1 ml ice cold lavage solution ( 0 . 5% EDTA in 0 . 9% saline ) . The collected lavage solution was immediately placed on ice and then centrifuged at 200 g for 10 min at 4°C . The resulting cell pellet was then resuspended in 500 μl lavage solution . Thin smears were made on microscope slides with the cell solution , stained with Diffquick stain and examined under x100 magnification . Differential cell counts were performed with the stained thin smears by counting 300 cells in total with the percentage of neutrophils in each sample calculated . The total cell count in the lavage solution was determined using a hemocytometer . Then , the total neutrophil count per mouse in the lavage solution was calculated by multiplying the percentage of neutrophils by the total cell count . The experiment was performed twice and in each case eight mice per group were used . Statistical analysis was performed with one-way analysis of variance ( ANOVA ) using GraphPad prism 6 . Statistical significance was established at P < 0 . 05 compared with the PBS control and the LPS control . Human venous blood was prepared from a healthy volunteer after he/she provided informed written consent and following review by the QIMRB Medical Research Institute ( QIMRB ) Human Ethics Committee . All animal experimentation was conducted in strict accordance with protocols approved by the QIMRB Animal Ethics Committee ( project number P384 ) , which adheres to the Australian code of practice for the care and use of animals for scientific purposes , as well as the Queensland Animal Care and Protection Act 2001; Queensland Animal Care and Protection Regulation 2002 .
EG_08721 ( GenBank: EUB56407 . 1 ) , reported as being highly expressed in oncospheres and EG_07242 ( GenBank: EUB57880 . 1 ) , reportedly highly expressed in adult worms [4] were selected for further characterization and named EgKI-1 and EgKI-2 respectively . The full-length EgKI-1 and EgKI-2 cDNAs have open reading frames of 240 and 252 nucleotides . Both translated peptides contain 18 amino acid signal sequences ( Fig 1A ) and have molecular weights of 8 . 08 kDa ( EgKI-1 ) and 8 . 3 kDa ( EgKI-2 ) . EgKI-1 has six conserved cysteine residues whereas EgKI-2 has only five , apparently lacking the second disulphide bond ( Fig 1B ) . A Clustal alignment comparison of EgKI-1 and EgKI-2 with well characterized Kunitz inhibitors available in GenBank revealed that the Kunitz family signature is highly conserved among different species ( Fig 2A ) . Phylogenetic analysis of the two EgKIs ( Fig 2B ) confirmed their relatedness with Kunitz protein sequences from other taxa . Further interrogation indicated the presence of several other putative Kunitz inhibitors in the E . granulosus genome and transcriptome [4] , suggesting these proteins likely play an important role in the parasite’s biology . Some of these Kunitz proteins contain a non inhibitory P1 amino acid whereas others contain a protease inhibitory amino acid . Also , several contain only Kunitz domains , whereas some associate with other domains such as Ig or spondin . Clustal alignment performed with the ten putative single domain Kunitz inhibitors of E . granulosus identified nine typical trypsin inhibitors and one typical chymotrypsin inhibitor ( S1 Fig ) . EgKI-1 was highly expressed in oncospheres , the infective stage of E . granulosus for humans and intermediate hosts , such as sheep , whereas EgKI-2 was more highly expressed in adult worms compared with EgKI-1 ( Fig 3 ) . Purified yields of 1 mg/ L and 0 . 4 mg/ L , respectively , were obtained for recombinant EgKI-1 and EgKI-2 ( Fig 4A ) . There was no cross immuno-reactivity between EgKI-1 and EgKI-2 as shown by western blotting of the recombinant proteins with antisera from mice immunized with the two proteins; moreover , neither antisera reacted with E . coli produced SmKI or to the mammalian Kunitz proteins , BPTI and UTI ( Fig 4B and 4C ) . There was no positive reactivity with soluble antigen extracts from AW , PSC , HCM or ONC by either the anti-EgKI-1 or anti-EgKI-2 murine antisera in western blots . A positive reaction was evident with the EgKI-2 anti serum along the tegument of sections of adult worms ( Fig 5 ) but no positive reactivity was observed with sections from any of the life cycle stages probed with the EgKI-1 antiserum . According to the 3DLigandsite prediction , calcium ions bind with the glutamine ( Glu ) residue at position 49 in EgKI-1 ( Fig 6A ) . Recombinant EgKI-1 is shown to bind calcium more strongly than EgKI-2 ( Fig 6B ) . In serine protease inhibition assays ( Table 1 and Fig 7 ) EgKI-2 reacted as a typical trypsin inhibitor , having no significant inhibitory activity against the other tested proteases . EgKI-1 inhibited all tested proteases , except proteinase3 , showing a high potency for inhibiting chymotrypsin and neutrophil elastase ( Table 1 and Fig 7 ) . Varying the pre-incubation time period of the EgKI proteins with the serine proteases did not affect their inhibitory capacity suggesting that they are not “slow binders” [32] . Moreover , the denatured EgKI-1 and EgKI-2 proteins in 4 and 8 M Urea showed no or only minor inhibitory activity indicating the necessity of correct folding for inhibitory function ( S2 Fig ) . Neither recombinant EgKI-1 nor EgKI-2 interfered with the blood coagulation pathway when tested for APTT and PT . Neither protein prolonged the time taken for clot formation in either test , indicating no or minimal inhibition of proteases involved in the coagulation cascade ( S3 Fig ) . The results of the mouse air pouch model indicated that the infiltration of neutrophils to the inflammatory site was significantly reduced by around 50% ( P value <0 . 05 ) in the presence of 15 μM EgKI-1or the positive control Ulinastatin compared with the PBS control; injection of the LPS control had no effect on the numbers of neutrophils infiltrating the pouch ( Fig 8 ) .
The host-parasite relationship is complex being mediated both by parasite virulence factors and exacerbated by host responses . The presence of Kunitz type proteins in numerous phylogenetically diverse species suggests that these molecules perform important biological functions even though their precise role in each organism is not yet fully understood . We focused on two nucleotide sequences ( EgKI-1 and EgKI-2 ) encoding secreted single domain Kunitz type protease inhibitors ( EgKI-1 and EgKI-2 ) from E . granulosus , that we had identified by interrogation of the available genomic sequence . EgKI-1 has all six conserved cysteine residues present giving rise to three disulphide bonds ( C1-C6 , C2-C4 and C3-C5 ) but EgKI-2 lacks the 2nd disulphide bond ( C2-C4 ) . Previous studies have shown that the lack of the C2-C4 bond and even selective inhibition of the same bond has no effect on the stability of Kunitz proteins or their inhibitory properties [33 , 34] . Conkunitzin-S1 is a neurotoxin from the venom of the cone snail Conus striatus which , despite missing the C2-C4 bond , retains its functional activity [35] . Furthermore , similar to EgKI-2 , Huwentoxin-XI ( HWTX-XI ) from the venom of tarantula spiders ( Ornithoctonus sp ) , is a potent trypsin inhibitor despite lacking the 2nd disulphide bond [36] . Real time PCR showed EgKI-1 is highly expressed in the oncosphere which is the stage infective to humans and ungulate hosts . The fact that EgKI-1 inhibited the activities of trypsin , chymotrypsin and PPE may be a feature that helps protect oncospheres from digestion by these enzymes in the small intestine of susceptible mammals . In contrast EgKI-2 is highly expressed in adult worms and , as shown by immunofluorescence , the protein is localized to the tegument , suggesting a possible role in protecting the parasite from the constant trypsin exposure that it is subjected to in the small intestine of the canine definitive host . Due to the considerable risk of handling and the difficulties in obtaining material , immunolocalization of the two EgKIs in activated oncospheres was not possible . The fact that neither murine anti-EgKI-1 nor anti-EgKI-2 antisera showed any positive reactivity with any of the western blot-tested soluble antigen extracts from different life cycle stages of E . granulosus suggests that either both EgKI proteins are produced in low quantity or are only expressed following an external stimulus , such as when the worm comes in contact with host proteases . A recent transcriptomic study identified five protease inhibitors , including EgKU8 [37] , in the excretory/secretory products of E . granulosus protoscoleces but more sensitive technologies may be required to identify other proteins expressed at lower levels of abundance as may be the case with EgKI-1 and EgKI-2 . The two Kunitz proteins were recombinantly expressed and purified by column refolding from induced lysates of E . coli cells transformed with EgKI/pET28a plasmids . Refolding of bacterially produced protein inclusion bodies immobilized by nickel chelating chromatography is a proven method for reconstituting the native properties of recombinant proteins and making them suitable for structural and functional analysis [38] . As a result , we were able to show that both EgKI proteins are potent serine protease inhibitors . Nanomolar range inhibition of trypsin activity was disclosed for EgKI-2 , while EgKI-1 inhibited chymotrypsin and neutrophil elastase in the picomolar range . The specificity of a protease inhibitor against a protease is mainly determined by the nature of the amino acid residue at position P1 of its active site . The results we obtained with the EgKI proteins are in agreement with previous findings of Kunitz inhibitors from other taxa , where typical trypsin inhibitors have Arg ( R ) or Lys ( K ) at P1 , and chymotrypsin inhibitors have Leu ( L ) or Met ( M ) [39]; EgKI-1 and EgKI-2 have Leu and Arg residues at the P1 site , respectively . The EgKIs are likely to play an important role in E . granulosus survival within their mammalian hosts and thus have potential as new drug and/or vaccine targets as control interventions . EgKI-1 may prevent the oncosphere from being digested in the gut by inhibiting trypsin , chymotrypsin and pancreatic elastase before it penetrates the intestinal wall . Similarly , trypsin inhibition by EgKI-2 may help provide protection to the adult worms while residing in the small intestine of the canine host . Inflammatory responses occur after surgery , trauma and infection , and involve neutrophil activation and infiltration into the injured tissue . Neutrophil infiltration also occurs in the early stages of echinococcal infection [40] . Activated neutrophils release proteases such as neutrophil elastase , cathepsin G and proteinase 3 which , if not appropriately controlled , can result in severe damage to healthy tissue . Uncontrolled proteolysis can lead to various diseases/disease syndromes including emphysema , idiopathic pulmonary fibrosis , respiratory distress syndrome , cystic fibrosis , rheumatoid arthritis and glomerulonephritis [41] . Neutrophil elastase is the major protease responsible for extracellular proteolysis and it plays a pivotal role in the inflammatory response [42] . By releasing neutrophil elastase in the presence of foreign material in blood , infiltrating neutrophils activate a signalling pathway which triggers macrophages to secrete cytokines as well as to attract more neutrophils [43] . The most potent , specific human neutrophil elastase inhibitor described to date is a protein engineered from the Kunitz domain of human inter α inhibitor ( EPI-HNE-4 ) , which was shown to have a Ki value of 5 . 45 x 10−12 M [44] . Excessive accumulation of neutrophil elastase in pulmonary fluids and tissues of patients with cystic fibrosis ( CF ) is thought to act on the lungs , compromising their structure and function , so that EPI-HNE-4 has been suggested as an anti-inflammatory compound for the treatment of CF [44] . We show here that EgKI-1 is also a highly potent inhibitor ( Ki = 6 . 42x10-11 M ) of neutrophil elastase and it thus warrants further investigation as a potentially effective therapeutic for treating acute and chronic inflammatory diseases . Cathepsin G has chymotrypsin-like catalytic activity and also has potent pro-inflammatory activity [42] . Calcium mobilization is one of the earliest events that occurs with neutrophil activation and is a key factor for modulating numerous neutrophil biological responses [45] . Once stimulated , the intracellular Ca++ concentration within neutrophils rises rapidly due to mobilization of ions from intracellular pools and influxes from the extracellular medium [46] . As well as playing a role in preventing proteolytic damage to oncospheres by neutrophil elastase and cathepsin G once they enter the blood circulation , EgKI-1 , being a secretory protein , may also act to suppress further neutrophil activation by binding calcium ions [45] in the extracellular medium , thus making them unavailable to neutrophils . Neutrophil chemotaxis plays an important role in the inflammatory response and , when excessive or persistent , may augment tissue damage . The fact that neutrophils have a short life span of around 6–8 hours after purification from whole blood is a limitation for performing assays with primary neutrophils [47] . Resting neutrophils become primed and then mobilized to the site of infection which involves receptor activation and secretion of cytokines , chemokines and other components [41] . Because of this , the molecular properties of primed neutrophils are very different to their resting state . The regulatory functions of macrophages are also shared by primed neutrophils . Hence , in vitro experiments with freshly isolated neutrophils can often fail to recognize their full functional activity [41] . For a more complete understanding of the functional activities of EgKI-1 , we undertook in vivo experiments using the carrageenan induced mouse air pouch model . Subcutaneous injections of air over several days cause morphological changes in the cellular lining of the pouch and resemble a synovial cavity [48] . As an irritant , λ-carrageenan induces localized inflammation characterized by the infiltration of cells and a marked increase in the production of biochemical mediators . Thus , this model has been proven for use in pre-clinical studies of anti-inflammatory drugs [30] . It is notable therefore that EgKI-1 significantly reduced neutrophil infiltration to the inflammatory site , most likely as a result of its inhibition of neutrophil elastase . However , further studies are needed for a more complete understanding of the activity of EgKI-1 against inflammatory cytokines . There is much current interest in developing novel potent drugs as treatments for inflammatory-related diseases to inhibit excessive and uncontrolled neutrophil serine protease activity [49 , 50] . Clinical therapies that utilize protease inhibitors in controlling sepsis are currently restricted to the use of urinary trypsin inhibitor ( UTI ) , mainly in Japan [51] . UTI , also referred to as ulinastatin or bikunin , is a multivalent Kunitz type serine protease inhibitor found in human urine and produces several anti-inflammatory effects [52] . Proteases also play important roles beyond their involvement in inflammation . In different types of cancers , the secretion of various proteases correlates with the aggressiveness of the tumour . Kunitz type inhibitors have been shown to exhibit promising anti-cancer properties which may be used in their development as novel cancer therapies [53] . Bikunin [54] , TFPI-2 [55] and SPINT2 [56] are Kunitz proteins with anti-cancer effects , and as potent protease inhibitors , the EgKI proteins may also exhibit similar properties which can be exploited in cancer therapy . In summary , this study further broadens knowledge of E . granulosus biology and emphasises the potential importance of the EgKI Kunitz proteins in protecting the worm by inhibiting or inactivating host digestive enzymes in the gut . As such they represent novel intervention targets for the control of cystic echinococcosis . Being a small molecule , a potent neutrophil elastase inhibitor and an inhibitor of neutrophil chemotaxis , EgKI-1 warrants further study as a potential therapeutic agent against inflammatory diseases [57] . As well , the potential anti-cancer effects of the EgKIs and their possible interactions with cytokines should be investigated .
|
The hydatid tapeworm Echinococcus granulosus is able to survive in its mammalian hosts for many years without being digested by proteases . Two E . granulosus Kunitz proteins with potent protease inhibitory properties were identified and characterized . These Kunitz proteins may provide protection to the parasite from proteolytic digestion . These newly identified proteins are promising targets for developing new control interventions against echinococcosis , and one has potential as a novel anti-inflammatory therapeutic .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Cloning and Characterization of Two Potent Kunitz Type Protease Inhibitors from Echinococcus granulosus
|
A unique vertical bar among horizontal bars is salient and pops out perceptually . Physiological data have suggested that mechanisms in the primary visual cortex ( V1 ) contribute to the high saliency of such a unique basic feature , but indicated little regarding whether V1 plays an essential or peripheral role in input-driven or bottom-up saliency . Meanwhile , a biologically based V1 model has suggested that V1 mechanisms can also explain bottom-up saliencies beyond the pop-out of basic features , such as the low saliency of a unique conjunction feature such as a red vertical bar among red horizontal and green vertical bars , under the hypothesis that the bottom-up saliency at any location is signaled by the activity of the most active cell responding to it regardless of the cell's preferred features such as color and orientation . The model can account for phenomena such as the difficulties in conjunction feature search , asymmetries in visual search , and how background irregularities affect ease of search . In this paper , we report nontrivial predictions from the V1 saliency hypothesis , and their psychophysical tests and confirmations . The prediction that most clearly distinguishes the V1 saliency hypothesis from other models is that task-irrelevant features could interfere in visual search or segmentation tasks which rely significantly on bottom-up saliency . For instance , irrelevant colors can interfere in an orientation-based task , and the presence of horizontal and vertical bars can impair performance in a task based on oblique bars . Furthermore , properties of the intracortical interactions and neural selectivities in V1 predict specific emergent phenomena associated with visual grouping . Our findings support the idea that a bottom-up saliency map can be at a lower visual area than traditionally expected , with implications for top-down selection mechanisms .
Visual selection of inputs for detailed , attentive , processing often occurs in a bottom-up or stimulus driven manner , particularly in selections immediately or very soon after visual stimulus onset [1–3] . For instance , a vertical bar among horizontal ones or a red dot among green ones perceptually pops out automatically to attract attention [4 , 5] , and is said to be highly salient pre-attentively . Physiologically , a neuron in the primary visual cortex ( V1 ) gives a higher response to its preferred feature , e . g . , a specific orientation , color , or motion direction , within its receptive field ( RF ) when this feature is unique within the display , rather than when it is one of the elements in a homogenous background [6–12] . This is the case even when the animal is under anesthesia [9] , suggesting bottom-up mechanisms . This occurs because the neuron's response to its preferred feature is often suppressed when this stimulus is surrounded by stimuli of the same or similar features . Such contextual influences , termed iso-feature suppression , and iso-orientation suppression in particular , are mediated by intracortical connections between nearby V1 neurons [13–15] . The same mechanisms also make V1 cells respond more vigorously to an oriented bar when it is at the border , rather than at the middle , of a homogeneous orientation texture , as physiologically observed [10] , since the bar has fewer iso-orientation neighbors at the border . These observations have prompted suggestions that V1 mechanisms contribute to bottom-up saliency for pop-out features like the unique orientation singleton or the bar at an orientation texture border ( e . g . , [6–10] ) . This is consistent with observations that highly salient inputs can bias responses in extrastriate areas receiving inputs from V1 [16 , 17] . Behavioral studies have examined bottom-up saliencies extensively in visual search and segmentation tasks [4 , 18 , 19] , showing more complex , subtle , and general situations beyond basic feature pop-outs . For instance , a unique feature conjunction , e . g . , a red vertical bar as a color-orientation conjunction , is typically less salient and requires longer search times; ease of searches can change with target-distractor swaps; and target salience decreases with background irregularities . However , few physiological recordings in V1 have used stimuli of comparable complexity , leaving it open how generally V1 mechanisms contribute to bottom-up saliency . Meanwhile , a model of contextual influences in V1 [20–23] , including iso-feature suppression and colinear facilitation [24 , 25] , has demonstrated that V1 mechanisms can plausibly explain these complex behaviors mentioned above , assuming that the V1 cell with the highest response to a target determines its salience and thus the ease of a task . Accordingly , V1 has been proposed to create a bottom-up saliency map , such that the RF location of the most active V1 cell is most likely selected for further detailed processing [20 , 23] . We call this proposal the V1 saliency hypothesis . This hypothesis is consistent with the observation that microstimulation of a V1 cell can drive saccades , via superior colliculus , to the corresponding RF location [26] , and that higher V1 responses correlate with shorter RTs to saccades to the corresponding RFs [27] . It can be clearly expressed algebraically . Let ( O1 , O2 , …OM ) denote outputs or responses from V1 output cells indexed by i = 1 , 2 , . . . M , and let the RFs of these cells cover locations ( x1 , x2 , …xM ) , respectively , then the location selected by bottom-up mechanisms is ◯ = xî where î is the index of the most responsive V1 cell ( mathematically , î = argmaxiOi ) . It is then clear that ( 1 ) the saliency SMAP ( x ) at a visual location x increases with the response level of the most active V1 cell responding to it , and the less-activated cells responding to the same location do not contribute , regardless of the feature preferences of the cells; and ( 2 ) the highest response to a particular location is compared with the highest responses to other locations to determine the saliency of this location , since only the RF location of the most activated V1 cell is the most likely selected ( mathematically , the selected location is ◯ = argmaxxSMAP ( x ) ) ) . As salience merely serves to order the priority of inputs to be selected for further processing , only the order of the salience is relevant [23] . However , for convenience we could write Equation 1 as SMAP ( x ) = [ ] /[ ] , or simply SMAP ( x ) = Note that the interpretation of xi = x is that the RF of cell i covers location x or is centered near x . In a recent physiological experiment , Hegde and Felleman [28] used visual stimuli composed of colored and oriented bars resembling those used in experiments on visual search . In some stimuli the target popped out easily ( e . g . , the target had a different color or orientation from all the background elements ) , whereas in others , the target was more difficult to detect , and did not pop out ( e . g . , a color-orientation conjunction search , where the target is defined by a specific combination of orientation and color ) . They found that the responses of the V1 cells , which are tuned to both orientation and color to some degree , to the pop-out targets were not necessarily higher than responses to non-pop-out targets , and thus raising doubts regarding whether bottom-up saliency is generated in V1 . However , these doubts do not disprove the V1 saliency hypothesis since the hypothesis does not predict that the responses to pop-out targets in some particular input images would be higher than the responses to non-pop-out targets in other input images . For a target to pop out , the response to the target should be substantially higher than the responses to all the background elements . The absolute level of the response to the target is irrelevant: what matters is the relative activations evoked by the target and background . Since Hegde and Felleman [28] did not measure the responses to the background elements , their findings do not tell us whether V1 activities contribute to saliency . It is likely that the responses to the background elements were higher for the conjunction search stimuli , because each background element differed greatly from many of its neighbors , and , as for the target , there would have been weak iso-feature suppression on neurons responding to the background elements . On the other hand , each background element in the pop-out stimuli always had at least one feature ( color or orientation ) the same as all of its neighbors , so iso-feature suppression would have reduced the responses to the background elements , making them substantially lower than the response to the target . Meanwhile , it remains difficult to test the V1 saliency hypothesis physiologically when the input stimuli are more complex than those of the singleton pop-out conditions . Psychophysical experiments provide an alternative means to ascertain V1′s role in bottom-up salience . While previous works [20–23] have shown that the V1 mechanisms can plausibly explain the commonly known behavioral data on visual search and segmentation , it is important to generate from the V1 saliency hypothesis behavioral predictions that are hitherto unknown experimentally so as to test the hypothesis behaviorally . This hypothesis testing is very feasible for the following reasons . There are few free parameters in the V1 saliency hypothesis since ( 1 ) most of the relevant physiological mechanisms in V1 are established experimental facts that can be modeled but not arbitrarily distorted , and ( 2 ) the only theoretical input is the hypothesis that the RF location of the most responsive V1 cell to a scene is the most likely selected . Consequently , the predictions from this hypothesis can be made precise , making the hypothesis falsifiable . One such psychophysical test confirming a prediction has been reported recently [29] . The current work aims to test the hypothesis more systematically , by providing nontrivial predictions that are more indicative of the particular nature of the V1 saliency hypothesis and the V1 mechanisms . For our purpose , we first review the relevant V1 mechanisms in the rest of the Introduction section . The Results section reports the derivations and tests of the predictions . The Discussion section will discuss related issues and implications of our findings , discuss possible alternative explanations for the data , and compare the V1 saliency hypothesis with traditional saliency models [18 , 19 , 30 , 31] that were motivated more by the behavioral data [4 , 5] than by their physiological basis . The relevant V1 mechanisms for the saliency hypothesis are the RFs and contextual influences . Each V1 cell [32] responds only to a stimulus within its classical receptive field ( CRF ) . Input at one location x evokes responses ( Oi , Oj… ) from multiple V1 cells i , j , … having overlapping RFs covering x . Each cell is tuned to one or more particular features including orientation , color , motion direction , size , and depth , and increases its response monotonically with the input strength and resemblance of the stimulus to its preferred feature . We call cells tuned to more than one feature dimension conjunctive cells [23]; e . g . , a vertical rightward conjunctive cell is simultaneously tuned to rightward motion and vertical orientation [32] , a red horizontal cell to red color and horizontal orientation [33] . Hence , for instance , a red vertical bar could evoke responses from a vertical tuned cell , a red tuned cell , a red vertical conjunctive cell , and another cell preferring orientation two degrees from vertical but having an orientation tuning width of 15° , etc . The V1 saliency hypothesis states that the saliency of a visual location is dictated by the response of the most active cell responding to it [20 , 23 , 34] , , rather than the sum of the responses to this location . This makes the selection easy and fast , since it can be done by a single operation to find the most active V1 cell ( ) responding to any location and any feature ( s ) . We will refer to saliency by the maximum response , as the MAX rule , to saliency by the summed response as the SUM rule . It will be clear later that the SUM rule is not supported , or is less supported by data , nor is it favored by computational considerations ( see Discussion ) . Meanwhile , intracortical interactions between neurons make a V1 cell's response context-dependent , a necessary condition for signaling saliency , since , e . g . , a red item is salient in a green but not in a red context . The dominant contextual influence is the iso-feature suppression mentioned earlier , so that a cell responding to its preferred feature will be suppressed when there are surrounding inputs of the same or similar feature . Given that each input location will evoke responses from many V1 cells , and that responses are context-dependent , the highest response to each location to determine saliency will also be context-dependent . For example , the saliency of a red vertical bar could be signaled by the vertical tuned cell when it is surrounded by red horizontal bars , since the red tuned cell is suppressed through iso-color suppression by other red tuned cells responding to the context . However , when the context contains green vertical bars , its saliency will be signaled by the red tuned cells . In another context , the red vertical conjunctive cell could be signaling the saliency . This is natural since saliency is meant to be context-dependent . Additional contextual influences , weaker than the iso-feature suppression , are also induced by the intracortical interactions in V1 . One is the colinear facilitation to a cell's response to an optimally oriented bar when a contextual bar is aligned to this bar as if they are both segments of a smooth contour [24 , 25] . Hence , iso-orientation interaction , including both iso-orientation suppression and colinear facilitation , is not isotropic . Another contextual influence is the general , feature-unspecific , surround suppression to a cell's response by activities in nearby cells regardless of their feature preferences [6 , 7] . This causes reduced responses by contextual inputs of any features , and interactions between nearby V1 cells tuned to different features . The most immediate and indicative prediction from the hypothesis is that task-irrelevant features can interfere in tasks that rely significantly on saliency . This is because at each location , only the response of the most activated V1 cell determines the saliency . In particular , if cells responding to task-irrelevant features dictate saliencies at some spatial locations , the task-relevant features become “invisible” for saliency at these locations . Consequently , visual attention is misled to task-irrelevant locations , causing delay in task completion . Second , different V1 processes for different feature dimensions are predicted to lead to asymmetric interactions between features for saliency . Third , the spatial or global phenomena often associated with visual grouping are predicted . This is because the intracortical interactions depend on the relative spatial relationship between input features , particularly in a non-isotropic manner for orientation features , making saliency sensitive to spatial configurations , in addition to the densities , of inputs . These broad categories of predictions will be elaborated in the next section in various specific predictions , together with their psychophysical tests .
Consider stimuli having two different features at each location , one task-relevant and the other task-irrelevant . For convenience , we call the V1 responses to the task-relevant and -irrelevant stimuli , relevant and irrelevant responses , respectively , and from the relevant and irrelevant neurons , respectively . If the irrelevant response ( s ) is stronger than the relevant response ( s ) at a particular location , this location's salience is dictated by the irrelevant response ( s ) according to the V1 saliency hypothesis , and the task-relevant features become “invisible” for saliency . In visual search and segmentation tasks that rely significantly on saliency to attract attention to the target or texture border , the task-irrelevant features are predicted to interfere with the task by directing attention irrelevantly or ineffectively . Figure 1 shows the texture patterns ( Figure 1A–1C ) to illustrate this prediction . Pattern A has a salient border between two iso-orientation textures of left oblique and right oblique bars , respectively , activating two populations of neurons each for one of the two orientations . Pattern B is a uniform texture of alternating horizontal and vertical bars , evoking responses from another two groups of neurons for horizontal and vertical orientations , respectively . When all bars are of the same contrast , the neural response from the corresponding neurons to each bar would be the same ( ignoring neural noise ) if there were no intracortical interactions giving rise to contextual influences . With iso-orientation suppression , neurons responding to the texture border bars in pattern A are more active than neurons responding to other bars in pattern A; this is because they receive iso-orientation suppression from fewer active neighboring neurons , since there are fewer neighboring bars of the same orientation . For ease of explanation , let us say the highest neural responses to a border bar and a background bar are ten and five spikes/second , respectively . This V1 response pattern makes the border more salient , so it pops out in a texture-segmentation task . Each bar in pattern B has the same number of iso-orientation neighbors as a texture border bar in pattern A , so it evokes a comparable level of ( highest ) V1 response , i . e . , ten spikes/second , to that evoked by a border bar in pattern A . If patterns A and B are superimposed , to give pattern C , the composite pattern will activate all neurons responding to patterns A and B , each neuron responding approximately as it does to A or B alone ( for simplicity , we omitted the general suppression between neurons tuned to different orientations , without changing our conclusion , see below ) . According to the V1 saliency hypothesis , the saliency at each texture element location is dictated by the most activated neuron there . Since the ( relevant ) response to each element of pattern A is lower than or equal to the ( irrelevant ) response to the corresponding element of pattern B , the saliency at each element location in pattern C is the same as for B , so there is no texture border highlight in such a composite stimulus , making texture segmentation difficult . For simplicity in our explanation , our analysis above included only the dominant form of contextual influence , the iso-feature suppression , but not the less dominant form of the contextual influence , the general surround suppression and colinear facilitation . Including the weaker forms of contextual influences , as in the real V1 or our model simulations [21–23] , does not change our prediction here . So , for instance , general surround suppression between local neurons tuned to different orientations should reduce each neuron's response to pattern C from that to pattern A or B alone . Hence , the ( highest ) responses to the task-relevant bars in pattern C may be , say , eight and four spikes/second , respectively , at the border and background . Meanwhile , the responses to the task-irrelevant bars in pattern C should be , say , roughly eight spikes/second everywhere , leading to the same prediction of interference . In the rest of this paper , for ease of explanation without loss of generality or change of conclusions , we include only the dominant iso-feature suppression in our description of the contextual influences , and ignore the weaker or less dominant colinear facilitation and general surround suppression unless their inclusion makes a qualitative or relevant difference ( as we will see in the section Emergent Grouping of Orientation Features by Spatial Configurations ) . For the same reason , our arguments do not detail the much weaker responses from cells not as responsive to the stimuli concerned , such as responses from motion direction selective cells to a nonmoving stimulus , or the response from a cell tuned to 22 . 5° to a texture element in pattern C composed of two intersecting bars oriented at 0° and 45° , respectively . ( Jointly , the two bars resemble a single bar oriented at 22 . 5° only at a scale much larger or coarser than their own . Thus , the most activated cell tuned to 22 . 5° would have a larger RF , much of which would contain no ( contrast or luminance ) stimulus , leading to a response weaker than cells preferring both the scale and the orientation of the individual bars . ) This is because these additional but nondominant responses at each location are “invisible” to saliency by the V1 saliency hypothesis and thus do not affect our conclusions . Figure 1D shows that segmenting the composite texture C indeed takes much longer than segmenting the task-relevant component texture A , confirming the prediction . The RTs were taken in a task when subjects had to report the location of the texture border , as to the left or right of display center , as quickly as possible . ( The actual stimuli used are larger , see Materials and Methods . ) In pattern C , the task-irrelevant horizontal and vertical features from component pattern B interfere with segmentation by relevant orientations from pattern A . Since pattern B has spatially uniform saliency values , the interference is not due to the noisy saliencies of the background [19 , 35] . One may wonder whether each composite texture element in Figure 1C may be perceived by its average orientation at each location , see Figure 2F , thereby making the relevant orientation feature noisy to impair performance . Figure 2E demonstrates by our control experiment that this would not have caused as much impairment; RT for this stimulus is at least 37% shorter than that for the composite stimulus . If one makes the visual search analog of the texture segmentation tasks in Figure 1 , by changing stimulus Figure 1A ( and consequently stimulus Figure 1C ) such that only one target of left- ( or right- ) tilted bar is in a background of right- ( or left- ) tilted bars , qualitatively the same result ( Figure 1E ) is obtained . Note that the visual search task may be viewed as the extreme case of the texture-segmentation task when one texture region has only one texture element . Note that , if saliency were computed by the SUM rule ( rather than the MAX rule ) to sum the responses Oi from cells preferring different orientations at a visual location x , interference would not be predicted since the summed responses at the border would be greater than those in the background , preserving the border highlight . Here , the texture border highlight Hborder ( for visual selection ) is measured by the difference Hborder = Rborder − Rground between the ( summed or maxed ) response Rborder to the texture border and the response Rground to the background ( where response Rx at location x means or , under the SUM or MAX rule , respectively ) . This is justified by the assumption that the visual selection is by the winner-take-all of the responses Rx in visual space x , hence the priority of selecting the texture border is measured by how much this response difference is compared with the level of noises in the responses . Consequently , the SUM rule applied to our example of response values gives the same border highlight Hborder = 5 spikes/second with or without the task-irrelevant bars , while the MAX rule gives Hborder = 0 and 5 spikes/second , respectively . If the border highlight is measured more conservatively by the ratio Hborder = Rborder/Rground ( when a ratio Hborder = 1 means no border highlight ) , then the SUM rule predicts , in our particular example , Hborder = ( 10 + 10 ) / ( 5 + 10 ) = 4/3 with the irrelevant bars , and Hborder = 10/5 = 2 without , and thus some degree of interference . However , we argue below that even this measure of Hborder by the response ratio makes the SUM rule less plausible . Behavioral and physiological data suggest that , as long as the saliency highlight is above the just-noticable difference ( JND , [36] ) , a reduction in Hborder should not increase RT as dramatically as observed in our data . In particular , previous findings [36 , 37] and our data ( in Figure 2E ) suggest that the ease of detecting an orientation contrast ( assessed using RT ) does not reduce by more than a small fraction when the orientation contrast is reduced , say , from 90° to 20° as in Figure 2A and Figure 2D [36 , 37] , even though physiological V1 responses [38] to these orientation contrasts suggest that a 90° orientation contrast would give a highlight of H90° ∼ 2 . 25 and a 20° contrast would give H20° ∼ 1 . 25 using the ratio measurement for highlights . ( Jones et al . [38] illustrated that the V1 response to a 90° and 20° orientation contrast , respectively , can be 45 and 25 spikes/second , respectively , over a background response of 20 spikes/second . ) Hence , the very long RT in our texture segmentation with interference implies that the border should have a highlight Hborder ≈ 1 or below the JND , while a very easy segmentation without interference implies that the border should have Hborder ≫ 1 . If Oborder and Oground are the relevant responses to the border and background bars , respectively , for our stimulus , and since Oborder also approximates the irrelevant response , then applying the SUM rule gives border highlight Hborder = 2Oborder/ ( Oborder + Oground ) and Oborder/Oground , with and without interference , respectively . Our RT data thus require that Oborder/Oground ≫ 1 and 2Oborder/ ( Oborder + Oground ) ≈ 1 should be satisfied simultaneously—this is difficult since Oborder/Oground > 2 means 2Oborder/ ( Oborder + Oground ) > 4/3 , and a larger Oborder/Oground would give a larger 2Oborder/ ( Oborder + Oground ) , making the SUM rule less plausible . Meanwhile , the MAX rule gives a border highlight Hborder = Oborder/Oborder = 1 with interference and Hborder = Oborder/Oground > 1 without . These observations strongly favor the MAX over the SUM rule , and we will show more data to differentiate the two rules later . From our analysis above , we can see that the V1 saliency hypothesis also predicts a decrease of the interference if the irrelevant feature contrast is reduced , as demonstrated when comparing Figure 2G–2I with Figure 2A–2C , and confirmed in our data ( Figure 2E ) . The neighboring irrelevant bars in Figure 2I are more similarly oriented , inducing stronger iso-feature suppression between them , and decreasing their evoked responses , say , from ten to seven spikes/second . ( Although colinear facilitation is increased by this stimulus change , since iso-orientation suppression dominates colinear facilitation physiologically , the net effect is decreased responses to all the task-irrelevant bars . ) Consequently , the relevant texture border highlights are no longer submerged by the irrelevant responses . The degree of interference would be much weaker , though still nonzero , since the irrelevant responses ( of seven spikes/second ) still dominate the relevant responses ( of five spikes/second ) in the background , reducing the relative degree of border highlight from five to three spikes/second . Analogously , interference can be increased by decreasing task-relevant contrast , as demonstrated by comparing Figure 2J–2L and Figure 2G–2I , and confirmed in our data ( Figure 2E ) . Reducing the relevant contrast makes the relevant responses to the texture border weaker , say from ten to seven spikes/second , making these responses more vulnerable to being submerged by the irrelevant responses . Consequently , interference is stronger in Figure 2L than in Figure 2I . Essentially , the existence and strength of the interference depend on the relative response levels to the task-relevant and -irrelevant features , and these response levels depend on the corresponding feature contrasts and direct input strengths . When the relevant responses dictate saliency everywhere and their response values or overall response pattern are little affected by the existence or absence of the irrelevant stimuli , there should be little interference . Conversely , when the irrelevant responses dictate saliency everywhere , interference for visual selection is strongest . When the relevant responses dictate the saliency value at the location of the texture border or visual search target but not in the background of our stimuli , the degree of interference is intermediate . In both Figure 2C and Figure 2L , the irrelevant responses ( approximately ) dictate the saliency everywhere , so the texture borders are predicted to be equally nonsalient . This is confirmed across subjects in our data ( Figure 2E ) , although there is a large variation between subjects , perhaps because the bottom-up saliency is so weak in these two stimuli that subject specific top-down factors contribute significantly to the RTs . Can task-irrelevant features from another feature dimension interfere ? Figure 3A illustrates orientation segmentation with irrelevant color contrasts . As in Figure 1 , the irrelevant color contrast increases the responses to the color features since the iso-color suppression is reduced . At each location , the response to color could then compete with the response to the relevant orientation feature to dictate the saliency . In Figure 1C , the task-irrelevant features interfere because they evoke higher responses than the relevant features , as made clear by demonstrations in Figure 2 . Hence , whether color can interfere with orientation or vice versa depends on the relative levels of V1 responses to these two feature types . Color and orientation are processed differently by V1 in two aspects . First , cells tuned to color , more than cells tuned to orientation , are usually in V1′s cytochrome oxidase–stained blobs which are associated with higher metabolic and neural activities [39] . Second , cells tuned to color have larger RFs [33 , 40]; hence , they are activated more by larger patches of color . In contrast , larger texture patches of oriented bars can activate more orientation-tuned cells , but do not make individual orientation-tuned cells more active . Meanwhile , in the stimulus for color segmentation ( e . g . , Figure 3B ) , each color texture region is large so that color-tuned cells are most effectively activated , making their responses easily the dominant ones . Consequently , the V1 saliency hypothesis predicts: ( 1 ) task-irrelevant colors are more likely to interfere with orientation than the reverse; ( 2 ) irrelevant color contrast from larger color patches can disrupt an orientation-based task more effectively than that from smaller color patches; and ( 3 ) the degree of interference by irrelevant orientation in color-based task will not vary with the patch size of the orientation texture . These predictions are apparent when viewing Figure 3A and 3B . They are confirmed by RT data for our texture segmentation task , shown in Figure 3C–3J . Irrelevant color contrast can indeed raise RT in orientation segmentation , but is effective only for sufficiently large color patches . In contrast , irrelevant orientation contrast does not increase RT in color segmentation regardless of the sizes of the orientation patches . In Figure 3C–3E , the irrelevant color patches are small , activating the color-tuned cells less effectively . However , interference occurs under small orientation contrast which reduces responses to relevant features ( as demonstrated in Figure 2 ) . Larger color patches can enable interference even to a 90° orientation contrast at the texture border , as apparent in Figure 3A , and has been observed by Snowden [41] . In Snowden's design , the texture bars were randomly rather than regularly assigned one of two iso-luminant , task-irrelevant colors , giving randomly small and larger sizes of the color patches . The larger color patches made task-irrelevant locations salient to interfere with the orientation segmentation task . Previously , the V1 saliency hypothesis predicted that Snowden's interference should become stronger when there are more irrelevant color categories; e . g . , each bar could assume one of three rather than two different colors . This is because more color categories further reduce the number of iso-color neighbors for each colored bar and thus the iso-color suppression , increasing responses to irrelevant color . This prediction was subsequently confirmed [29] . In Figure 3G–3I , the relevant color contrast was made small to facilitate interference by irrelevant orientation , though unsuccessfully . Our additional data showed that orientation does not significantly interfere with color-based segmentation even when the color contrast was reduced further . The patch sizes , of 1 × 1 and 2 × 2 , of the irrelevant orientation textures ensure that each bar in these patches evoke the same levels of responses , since each has the same number of iso-orientation neighbours ( this would not hold when the patch size is 3 × 3 or larger ) . Such an irrelevant stimulus pattern evokes a spatially uniform level of irrelevant responses , thus ensuring that interference cannot possibly arise from non-uniform or noisy response levels to the background [19 , 35] . Patch sizes for irrelevant colors in Figure 3C–3E were made to match those of irrelevant orientations in Figure 3G–3I , so as to compare saliency effects by color and orientation features . Note that , as discussed in the section Interference by Task-Irrelevant Features , the SUM rule would predict the same interference only if saliency highlight Hborder is measured by the ratio between responses to the border and background . With this measure of Hborder , our data in this subsection , showing that the interference only increases RT by a small fraction , cannot sufficiently differentiate the MAX from the SUM rule . A visual location can be salient due to two simultaneous feature contrasts . For instance , at the texture border between a texture of green , right-tilted bars and another texture of pink , left-tilted bars , in Figure 4C , both the color and orientation contrast could make the border salient . We say that the texture border has a color-orientation double-feature contrast . Analogously , a texture border of an orientation–orientation double contrast , and the corresponding borders of single-orientation contrasts , can be made as in Figure 4E–4G . We can ask whether the saliency of a texture border with a double-feature contrast can be higher than both of those of the corresponding single-feature–contrast texture borders . We show below that the V1 saliency hypothesis predicts a likely “yes” for color-orientation double feature but a definite “no” for orientation–orientation double feature . V1 has color-orientation conjunctive cells that are tuned to both color and orientation , though their tuning to either feature is typically not as sharp as that of the single feature–tuned cells [33] . Hence , a colored bar can activate a color-tuned cell , an orientation-tuned cell , and a color-orientation conjunctive cell , with cell outputs Oc , Oo , and Oco , respectively . The highest response max ( Oc , Oo , Oco ) from these cells should dictate the saliency of the bar's location . Let the triplet of response be at an orientation texture border , at a color border , and at a color-orientation double-feature border . Due to iso-feature suppression , responses of a single feature cell is higher with than without its feature contrast , i . e . , and . The single-feature cells also have comparable responses with or without feature contrasts in other dimensions , i . e . , and . Meanwhile , the conjunctive cell should have a higher response at a double than a single feature border , i . e . , and , since it has fewer neighboring conjunctive cells responding to the same color and same orientation . The maximum could be , or to dictate the saliency of the double-feature border . Without detailed knowledge , we expect that it is likely that , in at least some nonzero percentage of many trials , is the dictating response , and when this happens , is larger than all responses from all cells to both single-feature contrasts . Consequently , averaged over trials , the double-feature border is likely more salient than both of the single-feature borders and thus should require a shorter RT to detect . In contrast , there are no V1 cells tuned conjunctively to two different orientations; hence , a double orientation–orientation border definitely cannot be more salient than both of the two single-orientation borders . The above considerations have omitted the general suppression between cells tuned to different features . When this is taken into account , the single feature–tuned cells should respond less vigorously to a double feature than to the corresponding effective single feature contrast . This means , for instance , ≲ and ≲ . This is because general suppression grows with the overall level of local neural activities . This level is higher with double-feature stimuli which activate some neurons more , e . g . , when and ( at the texture border ) . In the color-orientation double-feature case , ≲ and ≲ mean that could not guarantee that must be larger than all neural responses to both of the single feature borders . This consideration could somewhat weaken or compromise the double-feature advantage for the color-orientation case , and should make the double-orientation contrast less salient than the more salient one of the two single-orientation contrast conditions . In any case , the double-feature advantage in the color-orientation condition should be stronger than that of the orientation–orientation condition . These predictions are indeed confirmed in the RT data . As shown in Figure 4D and 4H , the RT to locate a color-orientation double-contrast border Figure 4C is shorter than both RTs to locate the two single-feature borders Figure 4A and Figure 4B . Meanwhile , the RT to locate a double-orientation contrast of Figure 4G is no shorter than the shorter one of the two RTs to locate the two single-orientation contrast borders Figure 4E and Figure 4F . The same conclusion is reached ( unpublished data ) if the irrelevant bars in Figure 4E or Figure 4F , respectively , have the same orientation as one of the relevant bars in Figure 4F or Figure 4E , respectively . Note that , to manifest the double feature advantage , the RTs for the single-feature tasks should not be too short , since RT cannot be shorter than a certain limit for each subject . To avoid this RT floor effect , we have chosen sufficiently small feature contrasts to make RTs for the single-feature conditions longer than 450 ms for experienced subjects and even longer for inexperienced subjects . Nothdurft [42] also showed the saliency advantage of the double-feature contrast in color orientation . The shortening of RT by feature doubling can be viewed phenomenologically as a violation of a race model which models the task's RT as the outcome of a race between two response decision making processes by color and orientation features , respectively . This violation has been used to account for the double-feature advantage in RT also observed in visual search tasks when the search target differs in both color and orientation from uniform distractors observed previously [43] , and in our own data ( Table 1A ) . In our framework , we could interpret the RT for color-orientation double feature as a result from a race between three neural groups—the color-tuned , the orientation-tuned , and the conjunctive cells . It is notable that the findings in Figure 4H cannot be predicted from the SUM rule . With single- or double-orientation contrast , the ( summed ) responses to the background bars are approximately unchanged , since the iso-orientation suppression between various bars is roughly unchanged . Meanwhile , the total ( summed ) response to the border is larger when the border has double-orientation contrast ( even considering the general , feature unspecific , suppression between neurons ) . Hence , the SUM rule would predict that the double-orientation contrast border is more salient than the single-contrast one , regardless of whether one measures the border highlight Hborder by the difference or ratio between the summed response to the texture border and that to the background . Combining iso-orientation suppression and colinear facilitation , contextual influences between oriented bars depend non-isotropically on spatial relationships between the bars . Thus , spatial configurations of the bars can influence saliency in ways that cannot be simply determined by densities of the bars , and properties often associated with grouping can emerge . Patterns A–G in Figure 5A–5G are examples of these , and the RT to segment each texture will be denoted as RTA , RTB , … , RTG . Patterns A and B both have a 90° orientation contrast between two orientation textures . However , the texture border in B seems more salient . Patterns C and D are both made by adding , to A and B , respectively , task-irrelevant bars ±45° relative to the task-relevant bars and containing a 90° irrelevant orientation contrast . However , the interference is stronger in C than in D . Patterns E and G differ from C by having zero orientation contrast among the irrelevant bars , pattern F differs from D analogously . As demonstrated in Figure 2 , the interference in E and G should thus be much weaker than that in C , and that in F much weaker than that in D . The irrelevant bars are horizontal in E and vertical in G , on the same original pattern A containing only the ±45° oblique bars . Nevertheless , segmentation seems easier in E than in G . These peculiar observations all seem to relate to what is often called visual “grouping” of elements by their spatial configurations , and can in fact be predicted from the V1 saliency hypothesis when considering that the contextual influences between oriented bars are non-isotropic . To see this , we need to abandon the simplification used so far to approximate contextual influences by only the dominant component—iso-feature suppression . Specifically , we now include in the contextual influences the subtler components: ( 1 ) facilitation between neurons responding to colinear neighboring bars and ( 2 ) general feature-unspecific surround suppression between nearby neurons tuned to any features . Due to colinear facilitation , a vertical border bar in pattern B is salient not only because a neuron responding to it experiences weaker iso-orientation suppression , but also because it additionally enjoys full colinear facilitation due to the colinear contextual bars , whereas a horizontal border bar in B , or an oblique border bar in A , has only half as many colinear neighbors . Hence , in an orientation texture , the vertical border bars in B , and in general colinear border bars parallel to a texture border , are more salient than border bars not parallel to the border given the same orientation contrast at the border . Hence , if the highest response to each border bar in A is ten spikes/second , then the highest response to each border bar in B could be , say , 15 spikes/second . Indeed , RTB < RTA , as shown in Figure 5H . ( Wolfson and Landy [44] observed a related phenomenon , more details in Li [22] ) . Furthermore , the highly salient vertical border bars make segmentation less susceptible to interference by task-irrelevant features , since their evoked responses are more likely dominating to dictate salience . Hence , interference in D is much weaker than in C , even though the task-irrelevant orientation contrast is 90° in both C and D . Indeed , RTD < RTC ( Figure 5H ) , although RTD is still significantly longer than RTB without interference . All these are not due to any special status of the vertical orientation of the border bars in B and D , for rotating the whole stimulus patterns would not eliminate the effects . Similarly , when the task-irrelevant bars are uniformly oriented , as in patterns E and G ( for A ) and F ( for B ) , the border in F is more salient than those in E and G , as confirmed by RTF < RTE and RTG . The “protruding through” of the vertical border bars in D likely triggers the sensation of the ( task-irrelevant ) oblique bars as grouped or belonging to a separate ( transparent ) surface . This sensation arises more readily when viewing the stimulus in a leisurely manner rather than in the hurried manner of an RT task . Based on the arguments that one usually perceives the “what” after perceiving the “where” of visual inputs [45 , 46] , we believe that this grouping arises from processes subsequent to the V1 saliency processing . Specifically , the highly salient vertical border bars are likely to define a boundary of a surface . Since the oblique bars are neither confined within the boundary nor occluded by the surface , they have to be inferred as belonging to another , overlaying ( transparent ) , surface . Given no orientation contrast between the task-irrelevant bars in E–G , the iso-orientation suppression among the irrelevant bars is much stronger than that in C and D , and is in fact comparable in strength to that among the task-relevant bars sufficiently away from the texture border . Hence , the responses to the task-relevant and -irrelevant bars are comparable in the background , and no interference would be predicted if we ignored general surround suppression between the relevant and irrelevant bars ( detailed below ) . Indeed , RTE , RTG ≪ RTC , and RTF < RTD . However , the existence of general surround suppression introduces a small degree of interference , making RTE , RTG > RTA , and RTF > RTB . Consider E for example , let us say that , without considering the general surround suppression , the relevant responses are ten spikes/second and five spikes/second at the border and background , respectively , and the irrelevant responses are five spikes/second everywhere . The general surround suppression enables nearby neurons to suppress each other regardless of their feature preferences . Hence , spatial variations in the relevant responses cause complementary spatial variations in the irrelevant responses ( even though the irrelevant inputs are spatially homogeneous ) ; see Figure 5I for a schematic illustration . For convenience , denote the relevant and irrelevant responses at the border as Oborder ( r ) and Oborder ( ir ) respectively , and as Onear ( r ) and Onear ( ir ) , respectively , at locations near but somewhat away from the border . The strongest general suppression is from Oborder ( r ) to Oborder ( ir ) , reducing Oborder ( ir ) to , say , four spikes/second . This reduction in turn causes a reduction of iso-orientation suppression on the irrelevant responses Onear ( ir ) , thus increasing Onear ( ir ) to , say , six spikes/second . The increase in Onear ( ir ) is also partly due to a weaker general suppression from Onear ( r ) ( which is weaker than the relevant responses sufficiently away from the border because of the extra strong iso-orientation suppression from the very strong border responses Oborder ( r ) [47] ) . Mutual ( iso-orientation ) suppression between the irrelevant neurons is a positive feedback process that amplifies any response difference . Hence , the difference between Oborder ( ir ) and Onear ( ir ) is amplified so that , say , Oborder ( ir ) = 3 and Onear ( ir ) = 7 spikes/seconds , respectively . Therefore , Onear ( ir ) dominates Onear ( r ) somewhat away from the border , dictating and increasing the local saliency . As a result , the relative saliency of the border is reduced and some degree of interference arises , causing RTE > RTA . The same argument leads similarly to conclusions RTG > RTA and RTF > RTB , as seen in our data ( Figure 5H ) . If colinear facilitation is not considered , the degree of interference in E and G should be identical , predicting RTE = RTG . As explained below , considering colinear facilitation additionally will predict RTE < RTG , as seen in our data for three out of four subjects ( Figure 5H ) . Stimuli E and G differ in the direction of the colinear facilitation between the irrelevant bars . The direction is across the border in E but along the border in G , and , unlike iso-orientation suppression , facilitation tends to equalize responses Onear ( ir ) and Oborder ( ir ) to the colinear bars . This reduces the spatial variation of the irrelevant responses across the border in E such that , say , Oborder ( ir ) = 4 and Onear ( ir ) = 6 spikes/second , thus reducing the interference . The SUM rule ( over V1′s neural responses ) would predict qualitatively the same directions of RT variations between conditions in this section only when the texture border highlight Hborder is measured by the ratio rather than by the difference between the ( summed ) response to the border and that to the background . However , using the same argument as in the section Interference by Task-Irrelevant Features , our quantitative data would make the SUM rule even more implausible than it is in that section ( since , using the notations from that section , we note that Oground approximates the irrelevant responses in E and G , whose weak interference would require a constraint of Hborder = ( Oborder + Oground ) /2Oground > 1 + δ with δ ≫ 0 , in addition to the other stringent constraints in that section that made the SUM rule less plausible ) . We also carried out experiments in visual search tasks analogous to those in Figures 3–5 , as we did in Figure 1E analogous to Figure 1D . Qualitatively the same results as those in Figures 3 and 4 were found; see Table 1 . For visual search conditions corresponding to those in Figure 5 , however , since there were no elongated texture borders in the stimuli , grouping effects arising from the colinear border , or as the result of the elongated texture border , are not predicted , and indeed , not reflected in the data; see Table 2 . This confirmed additionally that saliency is sensitive to spatial configurations of input items in the manner prescribed by V1 mechanisms .
In summary , we tested and confirmed several predictions from the hypothesis of a bottom-up saliency map in V1 . All these predictions are explicit since they rely on the known V1 mechanisms and an explicit assumption of a MAX rule , ; i . e . , among all responses Oi to a location x , only the most active V1 cell responding to this location determines its saliency . In particular , the predicted interference by task-irrelevant features and the lack of saliency advantage for orientation–orientation double features are specific to this hypothesis since they arise from the MAX rule . The predictions of color-orientation asymmetry in interference , the violation in the RT for color-orientation double feature of a race model between color and orientation features , the increased interference by larger color patches , and the grouping by spatial configurations , stem one way or another from specific V1 mechanisms . Hence , our experiments provided direct behavioral test and support of the hypothesis . As mentioned in the Interference by Task-Irrelevant Features , the predicted and observed interference by irrelevant features , particularly those in Figures 1 and 2 , cannot be explained by any background “noise” introduced by the irrelevant features [19 , 35] , since the irrelevant features in our stimuli have a spatially regular configuration and thus would by themselves evoke a spatially uniform or non-noisy response . The V1 saliency hypothesis does not specify which cortical areas read out the saliency map . A likely candidate is the superior colliculus which receives input from V1 and directs eye movements [48] . Indeed , microstimulation of V1 makes monkeys saccade to the RF location of the stimulated cell [26] , and such saccades are believed to be mediated by the superior colliculus . While our experiments support the V1 saliency hypothesis , the hypothesis itself does not exclude the possibility that other visual areas contribute additionally to the computation of bottom-up saliency . Indeed , the superior colliculus receives inputs also from other visual areas [48] . For instance , Lee et al . [49] showed that pop-out of an item due to its unique lighting direction is associated more with higher neural activities in V2 than those in V1 . It is not inconceivable that V1′s contribution to bottom-up saliency is mainly for the time duration immediately after exposure to the visual inputs . With a longer latency , especially for inputs when V1 signals alone are too equivocal to select the salient winner within that time duration , it is likely that the contribution from higher visual areas will increase . This is a question that can be answered empirically through additional experiments ( e . g . , [50] ) beyond the scope of this paper . These contributions from higher visual areas to bottom-up saliency are in addition to the top-down selection mechanisms that further involve mostly higher visual areas [51–53] . The feature-blind nature of the bottom-up V1 selection also does not prevent top-down selection and attentional processing from being feature selective [18 , 54 , 55] , so that , for example , the texture border in Figure 1C could be located through feature scrutiny or recognition rather than saliency . It is notable that while we assume that our RT data are adequate to test bottom-up saliency mechanisms , our stimuli remained displayed until the subjects responded by button press , i . e . , for a duration longer than the time necessary for neural signals to propagate to higher level brain areas and feedback to V1 . Although physiological observations [56] indicate that preparation for motor responses contribute a long latency and variations in RTs , our work needs to be followed up in the future to further validate our hopeful assumption that our RT data sufficiently manifest bottom-up saliency to be adequate for our purpose . We argue that to probe the bottom-up processing behaviorally , requiring subjects to respond to a visual stimulus ( which stays on before the response ) as soon as possible , is one of the most suitable methods . We believe that this method should be more suitable than an alternative method to present stimulus briefly , with , or especially without , requiring the subjects to respond as soon as possible . After all , turning off the visual display does not prevent the neural signals evoked by the turned-off display from being propagated to and processed by higher visual areas [57] , and , if anything , it reduces the weight of stimulus-driven or bottom-up activities relative to the internal brain activities . Indeed , it is not uncommon for subjects to experience in RT tasks that they could not cancel their erroneous responses in time even though the error was realized way before the response completion and at the initiation of the response according to EEG data [58] , suggesting that the commands for the responses were issued considerably before the completion of the responses . Traditionally , there have been other frameworks for visual saliency [18 , 19 , 30] , mainly motivated by and developed from behavioral data [4 , 5] when there was less knowledge of their physiological basis . Focusing on their bottom-up aspect , these frameworks can be paraphrased as follows . Visual inputs are analyzed by separate feature maps , e . g . , red feature map , green feature map , vertical , horizontal , left-tilt , and right-tilt feature maps , etc . , in several basic feature dimensions such as orientation , color , and motion direction . The activation of each input feature in its feature map decreases roughly with the number of the neighboring input items sharing the same feature . Hence , in an image of a vertical bar among horizontal bars , the vertical bar evokes a higher activation in the vertical feature map than that by each of the many horizontal bars in the horizontal map . The activations in separate feature maps are summed to produce a master saliency map . Accordingly , the vertical bar produces the highest activation at its location in this master map and attracts visual selection . The traditional theories have been subsequently made more explicit and implemented by computer algorithms [31] . When applied to the stimulus in Figure 1C , it becomes clear that the traditional theories correspond to the SUM rule for saliency determination when different responses Oi to different orientations at the same location x represent responses from different feature maps . As argued , our data ( in the sections Interference from Task-Irrelevant Features , The Color Orientation Asymmetry in Interference , and Emergent Grouping of Orientation Features by Spatial Configurations ) on interference by task-irrelevant features are incompatible with or unfavorable for the SUM rule , and our data ( in the section Advantage for Color-Orientation Double Feature but Not Orientation–Orientation Double Feature ) on the lack of advantage for the double-orientation contrast are contrary to the SUM rule . Many of our predictions from the V1 saliency hypothesis , such as the color-orientation asymmetry in the section The Color Orientation Asymmetry in Interference and the section Advantage for Color-Orientation Double Feature but Not Orientation–Orientation Double Feature , and the emergent grouping phenomenon in the section Emergent Grouping of Orientation Features by Spatial Configuration arise specifically from V1 mechanisms , and could not be predicted by traditional frameworks without adding additional mechanisms or parameters . The traditional framework also contrasted with the V1 saliency hypothesis by implying that the saliency map should be in higher-level cortical areas where neurons are untuned to features , motivating physiological experiments searching for saliency correlates in areas such as the lateral intraparietal area which , downstream from V1 , could reflect bottom-up saliences in its neural activities [59 , 60] . Nevertheless , the traditional frameworks have provided an overall characterization of previous behavioral data on bottom-up saliency . These behavioral data provided part of the basis on which the V1 theory of saliency was previously developed and tested by computational modeling [20–23] . One may seek alternative explanations for our observations predicted by the V1 saliency hypothesis . For instance , to explain interference in Figure 1C , one may assign a new feature type to “two bars crossing each other at 45° , ” so that each texture element has a feature value ( orientation ) of this new feature type . Then , each texture region in Figure 1C is a checkerboard pattern of two different feature values of this feature type . So the segmentation could be more difficult in Figure 1C , just like it could be more difficult to segment a texture of “ABABAB” from another of “CDCDCD” in a stimulus pattern “ABABABABABCDCDCDCDCD” than to segment “AAA” from “CCC” in “AAAAAACCCCCC . ” This approach of creating new feature types to explain hitherto unexplained data could of course be extended to accommodate other new data . So for instance , new stimuli can easily be made such that new feature types may have to include other double feature conjunctions ( e . g . , color-orientation conjunction ) , triple , quadruple , and other multiple feature conjunctions , or even complex stimuli like faces , and it is not clear how long this list of new feature types needs to be . Meanwhile , the V1 saliency hypothesis is a more parsimonious account since it is sufficient to explain all the data in our experiments without evoking additional free parameters or mechanisms . It was also used to explain visual searches for , e . g . , a cross among bars or an ellipse among circles without any detectors for crosses or circles/ellipses [20 , 23] . Hence , we aim to explain the most data by the fewest necessary assumptions or parameters . Additionally , the V1 saliency hypothesis is a neurally based account . When additional data reveal the limitation of V1 for bottom-up saliency , searches for additional mechanisms for bottom-up saliency can be guided by following the neural basis suggested by the visual pathways and the cortical circuit in the brain [48] . Computationally , bottom-up visual saliency serves to guide visual selection or attention to a spatial location to give further processing of the input at that location . Therefore , by nature of its definition , bottom-up visual saliency is computed before the input objects are identified , recognized , or decoded from the population of ( V1 ) neural responses to various primitive features and their combinations . More explicitly , recognition or decoding from ( V1 ) responses requires knowing both the response levels and the preferred features of the responding neurons , while saliency computation requires only the former . Hence , saliency computation is less sophisticated than object identification; it can thus be achieved more quickly ( this is consistent with previous observations and arguments that segmenting or knowing “where is the input” is before or faster than classifying “what is the input” [45 , 46] ) , as well as more easily impaired or susceptible to noise . On the one hand , the noise susceptibility can be seen as a weakness or a price paid for a faster computation; on the other , a more complete computation at the bottom-up selection level would render the subsequent , attentive , processing more redundant . This is particularly relevant when considering whether the MAX rule or the SUM rule , or some other rule ( such as a response power summation rule ) in between these two extremes , is more suitable for saliency computation . The MAX rule to guide selection can be easily implemented in a fast and feature-blind manner , in which a saliency map readout area ( e . g . , the superior colliculus ) can simply treat the neural responses in V1 as values in a universal currency bidding for visual selection , to select ( stochastically or deterministically ) the RF location of the highest bidding neuron [34] . The SUM rule , or for the same reason the intermediate rule , is much more complicated to implement . The RFs of many ( V1 ) neurons covering a given location are typically non-identically shaped and/or sized , and many are only partially overlapping . It would be nontrivial to compute how to sum the responses from these neurons , whether to sum them linearly or nonlinearly , and whether to sum them with equal or non-equal weights of which values . More importantly , we should realize that these responses should not be assumed as being evoked by the same visual object—imagine an image location around a green leaf floating on a golden pond above an underlying dark fish—deciding whether and how to sum the response of a green tuned cell and that of a vertical tuned cell ( which could be responding to the water ripple , the leaf , or the fish ) would likely require assigning the green feature and the vertical feature to their respective owner objects , i . e . , to solve the feature-binding problem . A good solution to this assignment or summation problem would be close to solving the object-identification problem , making the subsequent attentive processing , after selection by saliency , redundant . These computational considerations against the SUM rule are also in line with the finding that statistical properties of natural scenes also favor the MAX rule [61] . While our psychophysical data also favor the MAX over the SUM rule , it is currently difficult to test conclusively whether our data could be better explained by an intermediate rule . This is because , with the saliency map SMAP , RT = f ( SMAP , β ) ( see Equation 4 ) depend on decision making and motor response processes parameterized by β . Let us say that , given V1 responses O , the saliency map is , generalizing from Equation 3 , SMAP = SMAP ( O , γ ) , where γ is a parameter indicating whether SMAP is made by the MAX rule or its softer version as an intermediate between MAX and SUM . Then , without precise ( quantitative ) details of O and β , γ cannot be quantitatively determined . Nevertheless , our data in Figure 4H favor a MAX rather than an intermediate rule for the following reasons . The response level to each background texture bar in Figure 4E–4G is roughly the same among the three stimulus conditions , regardless of whether the bar is relevant or irrelevant , since each bar experiences roughly the same level of iso-orientation suppression . Meanwhile , let the relevant and irrelevant responses to the border bars be OE ( r ) and OE ( ir ) , respectively , for Figure 4E , and OF ( r ) and OF ( ir ) , respectively , for Figure 4F . Then the responses to the two sets of border bars in Figure 4G are approximately OE ( r ) and OF ( r ) , ignoring , as an approximation , the effect of increased level of general surround suppression due to an increased level of local neural activities . Since both OE ( r ) and OF ( r ) are larger than both OE ( ir ) and OF ( ir ) , an intermediate rule ( unlike the MAX rule ) combining the responses to two border bars would yield a higher saliency for the border in Figure 4G than for those in Figure 4E and Figure 4F , contrary to our data . This argument , however , cannot conclusively reject the intermediate rule , especially one that closely resembles the MAX rule , since our approximation to omit the effect of the change in general surround suppression may not hold . Due to the difference between the computation for saliency and that for discrimination , it is not possible to predict discrimination performance from visual saliency . In particular , visual saliency computation could not predict subjects' sensitivities , e . g . , their d prime values , to discriminate between two texture regions ( or to discriminate the texture border from the background ) . In our stimuli , the differences between texture elements in different texture regions are far above the discrimination threshold with or without task-irrelevant features . Thus , if instead of an RT task , subjects performed texture discrimination without time pressure in their responses , their performance will not be sensitive to the presence of the irrelevant features ( even for briefly presented stimuli ) since the task essentially probes the visual process for discrimination rather than saliency . Therefore , our experiments to measure RT in a visual segmentation or search task , requiring subjects to respond quickly regarding “where” rather than “what” about the visual input by pressing a button located congruently with “where , ” using trivially discriminable stimuli , are designed to probe bottom-up saliency rather than the subsequent object recognition ( identification ) or discrimination performance . This design assumes that a higher saliency of the texture border or the search target makes its selection easier and thus faster , manifesting in a shorter RT . This is why our findings in RTs cannot be explained by models of texture discrimination ( e . g . , [62] ) , which are based on discriminating or identifying texture features , i . e . , based on visual processing after visual selection by saliency . While our subjects gave different RTs to different stimuli , their response error rates are typically very small ( <5% ) to all stimuli—as our RT task is not to measure discrimination sensitivities ( or d prime values ) . For the same reason , if one were to explain the interference in Figure 1C by the noise added by the task-irrelevant features , this feature noise would not be strong enough to sufficiently affect the error rate , since the feature differences ( between those of the irrelevant and relevant features ) are many times larger than the just-noticeable feature difference for feature discrimination . Of course , some visual search tasks , especially those using hardly discriminable stimuli , rely more on the recognition and/or less on bottom-up saliency computation . These tasks , while interesting to study for other purposes , would not be suitable for testing hypotheses on the bottom-up saliency , and we expect that cortical areas beyond V1 would be more involved for them and would have to read out from V1 the preferred features ( labeled lines ) and activities of more and less active neurons ( i . e . , beyond reading out the SMAP ) . Our observations are related to Gestalt principles of perceptual organization and many previous observations of visual grouping and emergent properties [63 , 64] . This suggests that V1 mechanisms could be the neural basis for many grouping phenomena , as has been shown in some examples [47 , 65] . For instance , the main Gestalt principle of grouping by similarity is related to iso-feature suppression in V1 , since iso-feature suppression , responsible for feature singleton pop-out , also makes a region of items of similar features less salient apart from the region border , which bounds , and induces the perception of , the region as a whole . Similarly , the principle of grouping by proximity is related to the finite length of the intracortical connections in V1 for contextual influences , and the principle of grouping by good continuation is related to the colinear facilitation in V1 . Pomerantz [63] showed that certain features , particularly ones involving spatial properties such as orientation , interact in complex ways to produce emergent perceptual configurations that are not simply the sum of parts . One of his notable examples of what is termed “configuration superiority effect” is shown in Figure 6 . One stimulus of a left-tilted bar among three right-tilted bars becomes a composite stimulus of a triangle among three arrows , when a non-informative stimulus of four identical “L”-shaped items is added . As a result , the triangle is easier to detect among the arrows than the left-tilted bar among right-tilt ones in the original stimulus , as if the triangle is an emergent new feature . This superiority effect by spatial configurations of bars , the opposite of interference by irrelevant features in our data , could be accounted for by the following mechanism beyond V1 . The added irrelevant “L”s made the target triangle shape unique , while the original target bar was a rotated version of the bar distractors . It was recently shown [66] that , when the bottom-up saliency is not sufficiently high ( as manifested in the longer-than-1 , 000-ms RTs in Pomerantz's data , likely due to a small set size ) , object rotational invariance between target and distractors could introduce object-to-feature interference to drastically prolong RT . This interference is because the original target , identically shaped as distractors , is confused as a distractor object . Whereas Gestalt principles and many psychological studies of emergent phenomena have provided excellent summaries and descriptions of a wealth of data , the V1 mechanisms provide explanations behind at least some of these data . Meanwhile , the psychological data in the literature , including the vast wealth of data on visual grouping , can in turn predict the physiology and anatomy of V1 through the V1 saliency hypothesis , thus providing opportunities to further test the hypothesis through physiological/anatomical experiments . Such tests should help to explore the potentials and the limitations of the V1 mechanisms to explain the bottom-up selection factors . For example , knowing that color-orientation conjunctive search is difficult ( e . g . , [37] , searching for a red vertical target among red horizontal and green vertical distractors ) and that color-orientation double feature is advantageous allow us to predict that , in V1 , intracortical ( disynaptic ) suppressive connections should link conjunctive cells with other cells preferring either the same color and/or the same orientation . Data by Hegde and Felleman [28] are consistent with this prediction , although more direct and systematic tests of the prediction are desirable . Similarly , the ease to search for a unique motion–orientation ( or motion–form ) conjunction predicts [23] that V1 cells tuned to motion–orientation conjunctions tend to connect to other cells preferring both the same orientation and the same motion direction . The V1 mechanisms for bottom-up saliency also have implications for mechanisms of top-down attention . First , if V1 creates a bottom-up saliency map for visual selection , then it would not be surprising that subsequent cortical areas/stages receiving input from V1 should manifest much interaction between bottom-up and top-down selectional and attentional factors . Second , by the V1 saliency hypothesis , the most active V1 cell attracts attention automatically to its RF location . This cell may be tuned to one or a few feature dimensions . Its response does not provide information about other feature dimensions to which it is un-tuned . Thus , such a bottom-up selection does not bind different features at the same location , and the top-down attention may have to bind the features subsequently [4] . Meanwhile , the conjunctive cells in V1 bind two ( or more ) features at the same location into a single cell by default ( which may or may not be veridical ) . This suggests that top-down attentional mechanisms are required to determine , from the responses of the conjunctive and nonconjunctive cells , not only the relative strengths of the two features , but also whether the two features belong to the same objects or whether the two features need to be unbound . Our findings reported here should motivate researchers in new directions for research into the mechanisms and frameworks of bottom-up and top-down attentional selection , and post-selectional processes for problems including feature binding .
In all our experiments , each stimulus pattern had 22 rows × 30 columns of items ( of single or double bars ) on a regular grid with unit distance 1 . 6° of visual angle . Each bar was a white ( CIE illuminant C ) , 1 . 2 × 0 . 12 degree rectangle ( for experiments in orientation feature dimensions only ) , or a colored 1 . 2 × 0 . 24 degree rectangle ( for experiments involving color and orientation features ) . All bars had a luminance of 14 cd/m2 unless otherwise stated , and the background was black . The colored bars were green or pink specified by their CIE 1976 coordinates ( u′ , v′ ) , with hue angles huv = 130° or 310° , respectively , where tan ( huv ) = ( v′ – vn′ ) / ( u′ – un′ ) , and ( un′ , vn′ ) are the coordinates of CIE illuminant C ( 0 . 201 , 0 . 461 ) . All bars within a stimulus had the same saturation For segmentation experiments , the vertical texture border between two texture regions was located randomly left or right , at 7 , 9 , or 11 interelement distances laterally from the display centre . Stimuli in search tasks were made analogously to those in texture-segmentation tasks , by reducing one of the two texture regions into a single target item . In each trial , the target was positioned randomly in one of the middle 14 rows; given the target's row number , its column number was such that the target was positioned randomly left or right , as close as possible to 16 . 8° of visual angle from the display centre . The noncoloured bars are oriented either as specified in captions of the figures and tables presented , or are oriented horizontally , vertically , or ±45° from vertical . The color and orientation of the target or left texture region in each trial were randomly green or pink ( for colored stimuli ) and left- or right-tilted ( or horizontal or vertical ) in the relevant orientations . Subjects are adults with normal or corrected to normal vision , and they are identified by letters , such as LZ , in the figures and tables . Most subjects are naive to the purpose of the study , except for LZ ( one of the authors ) , LJ , and ASL . Some subjects are more experienced at RT tasks than others . AP , FE , LZ , NG , and ASL participated in more experiments than others ( such as KC , DY , and EW ) who only participated in one or a few experiments . Subjects were instructed to fixate centrally until stimulus onset , to freely move their eyes afterward , and to press a left or right key ( located to their left or right hand side ) using their left or right hand , respectively , quickly and accurately to indicate whether the target or texture border ( present in each trial ) was in the left or right half of the display . The stimulus pattern stayed after onset until the subject's response . There were 96 trials per subject per stimulus conditions shown . Average RTs were calculated ( and shown in the figures and tables ) excluding trials that were erroneous or had an RT outside three standard deviations from the mean . The number of such excluded trials was usually less than 5% of the total for each subject and condition , and our results did not change qualitatively even when we included all trials in calculating RTs or considered the speed–accuracy tradeoff in performances . The error bars shown are standard errors . The experiments were carried out in a dark room . Within each figure plot , and each part ( A , B , C , etc . ) of Table 1 or Table 2 , all the stimulus conditions were randomly interleaved within an experimental session such that the subjects could not predict before each trial which stimulus condition would appear . For texture segmentation , the subjects were told to locate the border between two textures regardless of the difference ( e . g . , whether in color or orientation or both ) between the two textures . For visual search , the subjects were told to locate the target which had a unique feature ( such as orientation , color , or both , regardless of which orientation ( s ) and/or which color ) , i . e . , the odd one out , within the display . The subjects were shown examples of the relevant stimulus conditions to understand the task before the data-taking . Experiments ( e . g . , the one for Figure 5 ) requiring more than 300–400 trials in total were broken down to multiple data–taking sessions such that each session typically took 10–20 minutes .
|
Only a fraction of visual input can be selected for attentional scrutiny , often by focusing on a limited extent of the visual space . The selected location is often determined by the bottom-up visual inputs rather than the top-down intentions . For example , a red dot among green ones automatically attracts attention and is said to be salient . Physiological data have suggested that the primary visual cortex ( V1 ) in the brain contributes to creating such bottom-up saliencies from visual inputs , but indicated little on whether V1 plays an essential or peripheral role in creating a saliency map of the input space to guide attention . Traditional psychological frameworks , based mainly on behavioral data , have implicated higher-level brain areas for the saliency map . Recently , it has been hypothesized that V1 creates this saliency map , such that the image location whose visual input evokes the highest response among all V1 output neurons is most likely selected from a visual scene for attentional processing . This paper derives nontrivial predictions from this hypothesis and presents their psychophysical tests and confirmations . Our findings suggest that bottom-up saliency is computed at a lower brain area than previously expected , and have implications on top-down attentional mechanisms .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"primates",
"neuroscience",
"computational",
"biology"
] |
2007
|
Psychophysical Tests of the Hypothesis of a Bottom-Up Saliency Map in Primary Visual Cortex
|
Biological systems are characterized by a high number of interacting components . Determining the role of each component is difficult , addressed here in the context of biological oscillations . Rhythmic behavior can result from the interplay of positive feedback that promotes bistability between high and low activity , and slow negative feedback that switches the system between the high and low activity states . Many biological oscillators include two types of negative feedback processes: divisive ( decreases the gain of the positive feedback loop ) and subtractive ( increases the input threshold ) that both contribute to slowly move the system between the high- and low-activity states . Can we determine the relative contribution of each type of negative feedback process to the rhythmic activity ? Does one dominate ? Do they control the active and silent phase equally ? To answer these questions we use a neural network model with excitatory coupling , regulated by synaptic depression ( divisive ) and cellular adaptation ( subtractive feedback ) . We first attempt to apply standard experimental methodologies: either passive observation to correlate the variations of a variable of interest to system behavior , or deletion of a component to establish whether a component is critical for the system . We find that these two strategies can lead to contradictory conclusions , and at best their interpretive power is limited . We instead develop a computational measure of the contribution of a process , by evaluating the sensitivity of the active ( high activity ) and silent ( low activity ) phase durations to the time constant of the process . The measure shows that both processes control the active phase , in proportion to their speed and relative weight . However , only the subtractive process plays a major role in setting the duration of the silent phase . This computational method can be used to analyze the role of negative feedback processes in a wide range of biological rhythms .
Biological systems involve a large number of components that interact nonlinearly to produce complex behaviors . How can we determine the role that a component plays in producing a given behavior of the system ? We approach this question in the relatively simple context of relaxation oscillations , since relaxation oscillator models and their extensions are used to describe a wide variety of biological behaviors [1] , such as the cell cycle [2] , electrical activity of cardiac and neural cells [3] , [4] , circadian patterns of protein synthesis [5] , metabolic oscillations [6] and episodic activity in neuronal networks [7] . Specifically , we use a model developed to describe the rhythmic activity of developing neural networks and whose formalism also applies to cellular pacemakers [8] . The activity of the system can be either high or low , and slow negative feedback processes switch the system back and forth between the active and silent states . Hence the rhythm consists of episodes of high activity separated by silent phases , repeated periodically . While relaxation oscillator models usually contain one negative feedback process to regulate the rhythmic activity , in biological systems two or more feedback processes are often present . Thus , we consider a model with two different types of negative feedback: divisive and subtractive . In the context of an excitatory network , synaptic depression ( weakening of synaptic connections between neurons ) is a divisive feedback ( decreasing the slope of the network input/output function ) while activation of a cellular adaptation process ( decreasing the neurons' excitability ) can be a subtractive feedback ( shifting the network input/output function ) [8] , [9] . With both types of negative feedback in the model , we seek to determine the contribution that each makes to episode initiation and termination . We begin by using two strategies based on the two broad types of experimental protocols . The correlative strategy seeks to detect associations between the time course of a variable and the system's behavior . To use the example of episodic activity generated by an excitatory neural network , we compare the variation of the fraction of undepressed synapses ( weighted by the synaptic conductance ) to the activation of the cellular adaptation current ( scaled by its conductance ) . Intuitively , the process that shows the greatest changes would be considered to affect activity the most , and thus contribute more to episode initiation/termination . The faster process covers a wider range during the active and silent phases [8] . This predicts that the faster a process and the larger its associated weight , the more it contributes to episode initiation and termination . The second strategy is to block one process , with the expectation that this will result in changes in activity that are directly related to the contribution of that process . Perhaps contrary to intuition , we find that blocking a slow process may provide little information on the role of that process in the rhythm generation , and that the correlative and blocking strategies may even lead to contradictory conclusions . We then develop a new strategy based on the idea that if a negative feedback process contributes significantly to episode termination , then increasing its time constant should significantly increase episode duration . Similarly , if recovery of such a process contributes to episode initiation , then increasing its time constant should significantly delay episode initiation . We develop a measure of the respective contribution of each process based on these ideas . This measure reveals that if the divisive and subtractive feedback processes have similar time scales and similar weight they contribute similarly to episode termination . In contrast , the subtractive process controls episode initiation , even if it is slower or has less weight . This also means that the divisive process only plays a minor role in episode initiation . This unexpected result was not revealed using the standard approaches , and demonstrates the utility of the new measure in pulling out the key dynamics involved in rhythm generation . These results demonstrate that the characteristics of the correlative and blocking methods limit their usefulness in the determination of which feedback process controls rhythmic activity . Instead , this question requires computational tools such as the ones developed here . Finally , we point out in Discussion that hybrid systems such as the dynamic clamp may allow experimental use of our method .
We consider a mean field type model describing the activity of an excitatory neural network subject to both synaptic depression and cellular adaptation as described previously ( Tabak et al . , 2006 ) . The variables of the model are a , the network activity ( firing rate averaged across population and time; a = 0 corresponds to all cells silent , a = 1 means all cells fire at their maximal frequency ) ; s , the fraction of undepressed synapses ( s = 0 means all synapses are depressed , s = 1 means all synapses are operational ) ; and θ , a cellular adaptation process that raises the neuronal firing threshold ( θ = 0 means no adaptation so the cellular threshold is at its baseline level θ0 , θ = 1 is the maximal adaptation ) . The model equations are: ( 1 ) ( 2 ) ( 3 ) where a∞ is an increasing sigmoidal network input/output function ( Table 1 ) . The two parameters w and θ0 set the global network excitability [8] . Connectivity ( w ) represents the amount of positive feedback due to excitatory connections , i . e . , it determines the fraction of network output ( activity ) fed back as input to the network . The average cellular threshold ( θ0 ) measures the cellular excitability , i . e . , it biases the cells' responses to synaptic inputs . In Eq ( 1 ) we see that synaptic depression , which decreases s , acts as a divisive factor , decreasing the amount of positive feedback , while cellular adaptation , which increases θ , is a subtractive factor . An additional parameter , g , can be adjusted to scale the strength of the adaptation process . Unless mentioned otherwise , g is set to 1 . The steady state functions s∞ and θ∞ are decreasing and increasing sigmoidal functions of activity , respectively . Thus , when activity is high , s decreases and θ increases , both of which contribute to active phase termination . During the silent phase , s increases and θ decreases , eventually initiating a new active phase . The active phase is defined as the period of activity for which a is above an arbitrarily determined threshold ( 0 . 35 ) . Below this threshold the system is in the silent phase . The network recruitment time constant , τa , is arbitrarily set to 1 and the time constant for the variations of s and θ are assumed much larger than τa . That is , s and θ are slow processes . All parameter values are given in Table 1 . Equations were solved numerically using the 4th order Runge Kutta method ( dt = 0 . 05 ) in XPPAUT [10] . The simulation code is freely available on RB's website http://www . math . fsu . edu/~bertram/software/neuron .
The rationale for this first approach is that if a process varies greatly during the high-activity episodes ( active phases ) and the inter-episode intervals ( silent phases ) , then it is likely that it contributes significantly to episode termination and onset . On the other hand , if the variations are small , it is likely that the contribution of the process is small . This approach thus relies on observing a relationship between the time course of a process and the system's behavior . Its pitfall , that correlation does not imply causation , is well known . Experimentally , one can record spontaneous or evoked postsynaptic potentials or currents in target neurons [11] , [12] , [13] , [14] . The variations of this postsynaptic response during the interval of time between two episodes of activity would represent the variations of the effective connectivity , or available synaptic strength , w . s . Similarly , one may record the degree of adaptation or the current responsible for this adaptation at various times during the silent phase [11] , [14] . The variations of the current with time would be equivalent to the variations of g . θ . Here we assume that there are only two slow feedback processes , represented by s and θ , which can be measured unequivocally and with sufficient precision . This is an ideal situation that will not often be encountered experimentally; we show that even with such ideal conditions we may not be able to determine the contributions of the two slow processes using the correlative approach . If s varies by Δs and θ by Δθ over one phase of the oscillation , then according to the correlative approach the ratio ( 4 ) measures the contribution of s relative to that of θ . We have shown previously [8] that if s and θ vary exponentially with time constants τs and τθ , then Δs/Δθ ≈ τθ/τs . Thus , ( 5 ) Assuming that w and g are similar – we set w and g to 1 unless noted otherwise – the ratio of the contributions of the two processes to the rhythmic activity is inversely proportional to the ratio of their time constants , so the faster process contributes more than the slower process . This is illustrated in Figure 1AB where we plot the variations of a ( network activity ) , s and θ for the cases r = τθ/τs = 0 . 1 ( A ) and r = τθ/τs = 10 ( B ) . In the case shown in Figure 1A , we expect s ( red curve ) to contribute more to episode onset/termination because it is the faster process , while in the case shown in Figure 1B θ ( blue curve ) is faster and thus expected to have the major contribution . We define a quantitative measure of the contribution of the two processes by ( 6 ) ( or , using the approximation given by Eq . 5 , c = ( r−1 ) / ( r+1 ) ) . C varies between −1 and 1 . If C is near 1 then s determines the episode onset and termination ( i . e . , θ has no role ) . If C≈−1 then θ controls episode onset and termination . Intermediate values of C indicate that both processes contribute . This measure is plotted as a function of r in Figure 1C , and clearly demonstrates the shift of control ( according to the correlative definition ) from θ to s as the s dynamics are made progressively faster relative to θ . The filled circles result from simulations with the cell excitability parameter θ0 set to 0 ( relatively high cell excitability ) . The open circles were obtained using θ0 = 0 . 18 ( low cell excitability ) . The differences are very small , showing that , according to this measure , the respective contributions of the two processes depend very weakly on θ0 . The dashed curve in Figure 1C is obtained by plotting c = ( r−1 ) / ( r+1 ) . Since the points obtained from plotting C lie almost on this curve , one concludes that , according to the correlative approach , the contributions of the two slow processes depend only on the ratio r = ( w/g ) ( τθ/τs ) . Thus , the faster that one process is relative to the other the greater its contribution will be to rhythm generation . Similarly , the greater the relative weight of a process , the greater its contribution . Finally , since each process covers the same range during the active and silent phase , these results do not distinguish between episode initiation and termination . That is , the correlative approach predicts that the contribution of each process is the same for episode initiation and termination . The rationale for this second approach is that if a process is important to a system's behavior , then removing it will have a large effect . This type of experiment is widely used in biology and includes pharmacological block , surgical ablation , and gene knockout . If , for example , θ represents the activation of a potassium current responsible for cellular adaptation , then one could block this current pharmacologically or genetically and measure the effect on network activity . We block the θ process by setting g = 0 and observe the effect on the length of both the active and silent phases after transient effects have died down . If we see a large increase in the active phase duration , then we conclude that this process is important in terminating the active phase . Similarly , if after the block we see a decrease in silent phase duration then we conclude that recovery of this process is important for episode initiation . The pitfall of this approach is that after blocking a process we obtain a different system . Figure 2 illustrates the results obtained with this approach , for different values of the parameter θ0 . Figure 2A shows the time course of network activity before and after blocking θ in the case τθ = τs . When cell excitability is too high ( e . g . , θ0 = 0 . 06 ) , synaptic depression alone cannot bring the network to a low activity state and rhythmicity is lost after the block . For lower cell excitability ( higher θ0 , middle and right columns ) , blocking θ leads to changes in the lengths of both the active and silent phases , to various degrees . These changes in active and silent phase durations ( AP and SP ) , after transient effects have died out , are represented on Figure 2B for different values of the ratio τθ/τs . Can we infer the importance of θ variations on rhythm generation from these changes ? We first note that for low θ0 rhythmic activity is lost after blocking θ , for all values of the ratio τθ/τs . Thus , variations in θ are required for rhythm generation in these cases . In the other cases shown , blocking θ has large effects on the active and silent phase durations , but these effects are difficult to interpret . For instance , we expect the block to increase the active phase in proportion to θ's contribution to episode termination . Thus , it seems that θ contributes significantly to episode termination in cases vi , viii and ix ( where there is a large increase in AP after the block ) , but does not contribute much to episode termination in case iii ( where there is no change in AP after the block ) . In cases ii and v the active phase duration actually decreases after the block , which is hard to interpret . Similarly , we expect the decrease in silent phase duration following θ block to be in accordance with θ's contribution to episode initiation , since residual adaptation delays episode onset . Thus , we would say that θ contributes significantly to episode onset in cases ii , iii , v , vi and viii . But again , we have an unexpected case ( ix ) where SP increases after the block . The blockade experiment illustrated in Figure 2 suggests that there are more cases where θ has a significant contribution on episode initiation ( ii , iii , v , vi , viii ) than on episode termination ( vi , viii , ix ) . This is in contradiction with the correlative approach , which suggested that θ had a similar contribution to both episode termination and initiation . There are also cases , such as vi and viii , where the effects of the block are similar , suggesting that θ's contribution to episodic behavior is similar in those cases . But cases vi and viii correspond to different values of the ratio τθ/τs . According to the correlative approach , the contribution of each process should vary with τθ/τs ( Figure 1C ) , so again the blockage approach and correlative approach disagree . Finally , on each row of Figure 2B the effect of the blockage varies with the value of the parameter θ0 . This again contradicts the correlative analysis , which showed little dependence on θ0 . The strong perturbation to the system effected by the block is responsible for the counterintuitive decrease in AP observed in cases ii , v and increase in SP observed for case ix . These changes reflect system compensation; after the block and after transients have died out , the unblocked process , s , covers a different range of values , so AP and SP are modified . This compensation could be avoided by measuring AP and SP just after the block instead of letting it equilibrate . This is illustrated in Figure 2Aii , where the block initially increases AP , then decreases it as SP is decreased by the absence of θ . Interpretation of the block experiment would therefore be facilitated by considering only transient behavior , but this would be difficult to do experimentally in most cases . For instance if we block a K+ channel pharmacologically then the kinetics of drug application and binding to the channels will interfere with the transient effects . In summary , we find that the correlative and blockage approaches suggest different interpretations about the contributions of the negative feedback processes to rhythm generation . In the following , we show that neither approach gives a satisfactory description of the contributions of the slow processes . This is because each approach suffers its own pitfall . The first approach is purely correlative , i . e . , it links variations in one process to the behavior of the system , but cannot establish causation . To obtain causation it is necessary to determine how the system responds to a perturbation to one of these processes , as in the blocking approach . Unfortunately , by perturbing the system , we change it . The loss of periodic activity after blocking θ ( as in cases i , iv , vii in Figure 2 ) shows that this process may be necessary for maintaining rhythmic activity , but it does not indicate what was the contribution of θ before the block . The goal here is to derive a measure that allows one to draw a causal link between each slow process and the activity pattern that does not involve a strong perturbation to the system . Suppose that s is the only negative feedback process regulating episodic activity , so it contributes 100% to both episode termination and initiation . Then doubling τs will ( approximately ) double both AP and SP . If s is not the only negative feedback process and therefore has only a partial contribution to episode termination and initiation , then doubling τs will still increase AP and SP but by a smaller factor . Thus , the contribution of s to the episodic activity can be determined by the fractional change in AP and SP durations following a change in τs . To illustrate this idea , we plot both AP and SP durations as either τs or τθ is varied in Figure 3A . Figure 3Ai shows that AP varies more with τs than does SP . This suggests that s has more influence on episode termination than on episode initiation . The variations of AP and SP with τθ ( Figure 3Aii ) show the opposite trend , suggesting that θ has more influence on episode initiation than on episode termination . These trends are also illustrated by the variations of the duty cycle ( = AP/ ( AP+SP ) ) with τs and τθ ( Figure 3B ) . The duty cycle increases with τs , but decreases with τθ . Finally , comparing Figures 3A i and ii , we observe that the variations of AP with τs and τθ are similar , suggesting that s and θ have comparable contributions on episode termination . On the other hand , SP varies more with τθ than with τs , suggesting that θ has a stronger influence on episode initiation than does s . This example suggests that the contributions made by the slow processes to the episodic activity can be determined by varying the time constants of the processes and observing the effects on AP and SP durations . We now use this idea to construct a quantitative measure of these contributions . We first construct a measure of the contribution of s to episode termination , as illustrated in Figure 4 . At the beginning of an episode , τs is increased by δτs . If s contributes to episode termination , slowing down s increases AP by δAP . We can quantify the contribution of s to episode termination by evaluating the ratio of the relative change in AP , δAP/AP , divided by the relative change in τs , δτs/τs . We thus define the normalized contribution of s to episode termination as ( 7 ) If s has no influence on episode termination , slowing it down has no effect and δAP = 0 . If s is the only process contributing to episode termination , then the active phase duration is the time it takes for s to decrease from its value at the beginning of an episode to its value at the transition between AP and SP . Since we consider relaxation oscillations , the transition time between active and silent states is negligible . Thus , a fractional change in τs leads to the same fractional change in AP ( δAP/AP = δτs/τs ) so that CsAP = 1 . Therefore , CsAP has a value between 0 ( s does not contribute to episode termination ) and 1 ( s is the only process contributing to episode termination ) . We quantify the contribution of s to episode initiation similarly using ( 8 ) We define the contributions of θ to episode termination and initiation in a similar way: ( 9 ) ( 10 ) These measures have the same motivation as the blockage experiment , but can be computed with small perturbations to the system . We use δτ/τ = 4% so the perturbation is small but nevertheless has a measurable effect . In addition , we look at the acute effect of the perturbation , i . e . , we do not wait until the system equilibrates . Figure 5A shows the contributions of s to episode termination ( CsAP ) and initiation ( CsSP ) as the ratio τθ/τs is varied , determined through numerical simulations as shown in Figure 4 . CsAP increases as this ratio is increased , that is , s contributes more to episode termination as it becomes faster relative to θ . When s is much slower than θ , CsAP is close to 0 . For s much faster than θ , CsAP is close to 1 . When s and θ have similar speed CsAP is close to 0 . 5 , suggesting that the divisive and subtractive feedback processes contribute equally to episode termination when their time constants are similar . This relationship between the contribution of feedback processes to episode termination and the ratio of their time constants is in agreement with the prediction from the correlative approach ( Figure 1C ) . However , the contribution of s to the silent phase , CsSP , varies differently with τθ/τs . Although it increases with τθ/τs , this increase is so weak that CsSP is below 0 . 1 even if s is 10 times faster than θ . This consistently low CsSP suggests that regardless of the relative time constants of the two negative feedback processes , s never contributes significantly to episode onset , in sharp contrast with the prediction from the correlative approach . Figure 5B shows that the contributions of θ to episode termination ( CθAP ) and initiation ( CθSP ) vary in the opposite way to CsAP and CsSP . If τs is much larger than τθ then s does not affect AP while θ strongly affects AP . As the ratio τθ/τs increases , the contribution of s to episode termination increases while the contribution of θ decreases , in such a way that the sum of the contributions of s and θ stays around 1 ( CsAP + CθAP ≈ 1 ) as shown in Figure 5C . The effect of θ on SP is always strong , while the effect of s is weak , regardless of τθ/τs . The sum of the contributions of s and θ to episode initiation also stays around 1 ( CsSP + CθSP≈1 ) . Thus s and θ have complementary contributions to the episodic activity and our measure is self-consistent . The relationship CsxP + CθxP≈1 is a consequence of the fact that s and θ are the only processes controlling AP and SP . That is , if we increase both of their time constants by a factor k , then AP and SP both increase by the same factor k ( Figure 3Aiii ) . This can be written , in the case of the active phase , as: AP ( k τs , k τθ ) = k AP ( τs , τθ ) . Application of Euler's theorem for homogeneous functions yields: and , after dividing each side by AP , results in CsAP + CθAP = 1 . Since we are dealing with only two slow processes , we can combine the measures defined for s and θ ( Figures 5A and 5B ) into single measures by defining ( 11 ) ( 12 ) With this definition , CAP and CSP vary between −1 to 1 . A value close to −1 signifies that θ is the dominant process; a value close to 1 signifies that s is the dominant process; a value near 0 means that s and θ have similar contributions . These are plotted in Figure 5D as a function of τθ/τs . We see that CAP rises from −1 to near 1 as τθ/τs increases , indicating that θ dominates the AP when it varies more rapidly than s , and s dominates when it varies more rapidly than θ . This agrees with the result obtained with the correlative approach ( dashed curve , c = ( r−1 ) / ( r+1 ) ) . In contrast , the SP is controlled by θ for the full range of τθ/τs; this was not predicted by the correlative approach . The contribution measures defined above are meaningful only if specific conditions are satisfied . The most important condition is that each variable or process contributes to the same aspect of system behavior . For instance we cannot compare the contribution of a slow negative feedback process , such as our s or θ , which terminates an episode of activity , to the contribution of a fast negative feedback variable that could be responsible for fast cycling during the high activity phase . Second , the variables must vary monotonically during each phase of the activity . If not , then increasing their time constant may not increase the duration of a phase in a predictable way and the sum of the contributions of the variables to that phase may not equal 1 . We use a relaxation oscillator with a clear distinction between active and silent phases . The measure can be applied to other types of oscillations , as long as active and silent phases can be clearly distinguished . In more complex cases , it may be necessary to divide a period of activity into more than two phases . More generally , the method could be applied to non-oscillatory systems , for example to determine the contribution that different variables make to return the system to an equilibrium following a perturbation . Also , the measure is not limited to two negative feedback processes . We have chosen feedback processes of different types , subtractive and divisive , because we find the problem of disentangling their relative contributions to be quite challenging . This measure can be applied with feedback processes of the same type , as long as they contribute to the same behavior . We have used the method to compute the respective contributions of two subtractive feedback processes to burst generation and shown that the results can be used to predict the occurrence of phase-independent resetting [15] . Finally , we use a deterministic model . Noise would not qualitatively affect our measure , as long as it does not affect the mechanisms for the transitions between phases . If noise is part of the transition mechanism [16] our method cannot be applied as it is , since noise would also contribute to the transitions . Since the measure requires a model of the system , the validity of its results depends on the validity of the model . Models may incorporate various degrees of realism , so it is important that the measure be robust to model details . For instance , if we add a fast variable to the relaxation oscillator model , so that fast oscillations ( spikes ) are produced during each active phase , the two slow negative feedback processes may still terminate episodes ( bursts ) like in the relaxation case . Thus , the relative contributions of each slow variable to burst onset and termination should not change qualitatively . We have demonstrated such robustness with a model of bursting in pancreatic islets [15] . We now evaluate how the parameters that control network excitability , w ( network connectivity ) and θ0 ( average cellular threshold ) , affect the contributions of s and θ to rhythm generation . Variations of CAP and CSP with w are represented in Figure 6 , for three different values of θ0 ( and for τθ/τs = 1 ) . Clearly , CAP increases with w , i . e . , synaptic depression contributes more to episode termination when network connectivity is high . However , this is not true for episode initiation , as CSP is almost unaffected by w . There is in fact a slight tendency for CSP to increase at the lowest values of w , which is more visible if s is faster than θ ( not shown ) . Changes in θ0 do not affect either CAP or CSP significantly . This is in agreement with the correlative approach , but in contrast to the results of the blockade experiment ( Figure 2 ) . In summary , the ratio τθ/τs and connectivity w – but not θ0 – strongly affect CAP , while none of these have a significant effect on CSP . In general , both feedback processes s and θ play roles in the episode termination , but only θ controls episode initiation . The relative influence of s and θ to episode termination varies with parameter values . The correlative approach is roughly correct for predicting the contributions of the two processes to episode termination , but not to episode initiation . This approach makes a direct comparison between the time scales of the two processes , scaled by their relative strength ( w and g ) , evaluating r = ( w/g ) ( τθ/τs ) . But this ratio is not the ratio of the contributions of the two processes to episode initiation . In fact , we show below that the weighted time scales cannot be compared directly but must be rescaled , the correct ratio beingwhere the scaling factor ak is the activity level at the transitions between active and silent phases . At episode termination , ak≈1 so the correlative approach is approximately right . However , at episode onset ak≈0 , so rrescaled ≈ 0 , meaning that s does not contribute significantly unless r >> 1 . Looking back at Eq . 1 , it is evident that s generally has little effect when activity is low . Such a simple fact was not revealed using the correlative and blockade approaches , stressing again that these standard experimental approaches are not always useful for determining the contributions of different variables to rhythmic activity . The analysis above suggests that the correlative approach can reasonably estimate the contribution of each process to episode termination , but misses the fact that s contributes little to episode onset ( Figure 5D ) . Results from both the blockade simulations and the analysis above suggest that θ is more important for episode initiation than episode termination . However , we have seen that the blockade approach does not typically provide a good indication of the contribution of θ to the AP and SP durations ( Figure 2B ) . To further demonstrate this , we plot in Figure 7 the variations of CAP and CSP with g ( curves ) , the maximal “conductance” of the adaptation process θ , in four of the cases illustrated in Figure 2B ( v , vi , viii , ix ) . The values of both CAP and CSP decrease as g is increased , indicating that the influence of θ in the control of the rhythm increases with g . As g decreases towards 0 , both CAP and CSP increase toward 1 since s is the only slow process when g = 0 . This is true for all four cases . However , CSP only increases noticeably when g approaches 0 , illustrating again that the subtractive feedback process controls the silent phase in most cases . Comparing Figure 7A–B , we see that the CAP curve is similar in both panels , as is the CSP curve . The bar plots show the effects of a blockade simulation , where g = 1 before the blockade and g = 0 afterwards . In Figure 7A the blockade results in a 50% reduction in the AP duration , while in Figure 7B there is a very large increase in the AP duration following blockade . Yet , according to the CAP curves the contribution of θ to the AP duration is nearly the same in both cases when g = 1 ( green and yellow boxes ) . Similarly , CSP is similar in panels C and D for g = 1 , yet the blockade results in decreased SP duration in C , but increased SP duration in D . Thus , the effects of the blockade on AP and SD durations do not provide much information on the respective contributions of the two processes before the blockade . Next , we compare cases shown in Figure 7B and 7C . We notice that CAP differs between the two cases , showing that when g = 1 the s variable contributes significantly to episode termination in one case ( Figure 7B ) but not the other ( Figure 7C ) . Yet , after blockade the changes in AP/SP ( bar plots ) are similar in both cases . Again , results from the blockade approach do not indicate what was the contribution of each process before the blockade . For the mathematically simple system used in this work , we can use a geometrical argument to derive approximate formulas for CSP and CAP . If the system is two-dimensional with one slow process , s , the trajectory could be drawn in the a , s-phase plane and would follow the a-nullcline ( except for fast jumps at the transitions between active and silent phase ) . For the three-dimensional system presented here , the trajectory in the three-dimensional a , s , θ-phase space follows the surface defined by da/dt = 0 [8] . We can project the three-dimensional trajectory and surface into the a , s-plane . This results in a two-dimensional trajectory that follows a dynamic a-nullcline ( Figure 8A ) . The effect of the third variable ( θ ) in this two-dimensional representation is to move and deform the dynamic a-nullcline ( the thin , black S-shaped curve in Figure 8A ) . Increasing θ moves the nullcline rightward . At the end of the active phase , the trajectory falls from the high- to the low-activity state and the dynamic nullcline is at its rightmost position ( thick , discontinuous , grey S-shaped curve on the right of the diagram ) . During the silent phase , s increases so the system's trajectory moves to the right while θ decreases so the a-nullcline is transformed leftward . When the trajectory passes the low knee ( LK ) of the nullcline , the trajectory jumps to the upper branch . At this point the nullcline has reached its leftmost position ( the thick grey S-shaped curve on the left ) , since θ will now again begin to increase and the a-nullcline will be transformed rightward . To compare the contributions of s and θ to the termination of the silent phase , we can therefore compare the length traveled by the trajectory ( controlled by s ) with the length traveled by the low knee ( controlled by θ ) . Assuming that their speeds are nearly uniform , we can compare the instantaneous variation of the trajectory's position ds to the instantaneous variation of the knee dsk due to the variation of θ , dθ . We can show [8] that ds ≈ dθ ( τθ/τs ) and that dsk ≈ ( g/w ) ( dθ/ak ) where ak is the activity level at the knee ( its value varies little with θ ) . Thus , the ratio of the contributions of s and θ is ( 13 ) This formula applies to both active and silent phases , however the activity level at the knee , ak , differs between the two phases . During the silent phase , ak is close to 0 so ds/dsk is very small , i . e . , s generally contributes little to the termination of the silent phase . On the other hand , during the active phase ak is close to 1 , so ds/dsk ≈ ( τθ/τs ) ( w/g ) . If ( τθ/τs ) ( w/g ) ≈1 then the two slow processes contribute similarly to active phase termination . This shows that the relative contributions of s and θ are qualitatively different for the different phases of activity . It also explains why the intuitive approach illustrated in Figure 1 is correct for the active phase ( where ak≈1 ) , since from Eq . 5 and Eq . 13 r ≈ ds/dsk . If ( τθ/τs ) ( w/g ) ≈ 1 , then r ≈ 1 and the correlative approach predicts equal contributions of the feedback variables ( Figure 1C ) . On the other hand , during the silent phase ak≈0 so r is not a good approximation to ds/dsk and the correlative approach is invalid . To compute ds/dsk for both phases , we must compute ak ( Eq . 13 ) for both knees of the dynamic a-nullcline shown Figure 8A . For this we note that the nullcline is defined by da/dt = 0 . Solving for s , we obtain ( 14 ) For each value of θ , the knees are defined by and differentiating Eq . 14 gives: ( 15 ) which has two solutions ak , each corresponding to a knee , provided the right hand side is greater than 2 . The values of θ at onset and termination of the episodes , to be used in Eq 15 , were obtained from the durations of the active and silent phases obtained from simulations [8] . Finally , when θ0 is changed there is a similar but opposite change in the range of variation of θ , so θ + θ0 is not affected much by a change in θ0 . Thus the solutions of Eq . 15 are not very sensitive to θ0 . This explains why the relative contributions of s and θ are little affected by θ0 , as seen in Figure 6 . Since we identify ds/dsk to the ratio of the contribution of the two slow variables for each phase , CsxP/CθxP , the combined measures CxP defined in Eq 11–12 correspond to the ratios ( ds/dsk − 1 ) / ( ds/dsk + 1 ) . These ratios are computed for both active and silent phases as a function of w and shown on Figure 8B . Comparison with Figure 6 ( middle panel ) shows that this geometric measure of the contributions of the slow processes is in good agreement with the empirical measure constructed above using sensitivities to the slow variables' time constants . Finally , we point out that there are rare situations when the two measures ( Eqs . 7–12 vs . Eq . 13 ) do not give similar results . Such a case is shown in Figure 8C , for which the parameter θ0 is large ( average cell excitability is low ) and τs is 10 times greater than τθ . Because θ0 is large , even when θ decreases to its minimum during the silent phase , s may not be sufficiently large for an episode to start , particularly if the connectivity is low . In that case , an episode is not started until s reaches the value corresponding to the low knee . Even if this is a small distance , it can take a long time since s is so slow . Thus , changing τs can have a strong effect on the silent phase and CSP determined from Eq . 12 becomes positive ( Figure 8C , left panel ) instead of close to -1 as computed using Eq . 13 ( Figure 8C , right panel ) . In other words , using a measure based on time indicates a strong contribution of s in that particular situation , while a measure based on geometry indicates a marginal contribution of s to episode initiation . This discrepancy between the two measures appears because θ does not vary uniformly . It slows down considerably as it approaches its asymptotic value , “waiting” for s to reach the low knee . Thus the dynamics of s now play a major role in terminating the silent phase . Note that θ still has a strong effect on the s dynamics during the silent phase ( it determines the location of the low knee of the a-nullcline in Figure 8A ) , but θ's dynamics do not affect the silent phase duration much , so the measure that relies on perturbing the time constants finds it has little contribution .
We have first attempted to use approaches inspired from experimental methodology to determine the relative contributions of the two feedback processes to rhythm generation . These included comparison of the time course of each process ( the correlative approach ) and blocking one of the processes . The correlative approach simply compares the amount of variation of each process , scaled by each process' strength or conductance . Since the two processes vary by the same amount during the active and silent phase , this approach does not distinguish between active and silent phase . According to this approach , the relative contribution depends only on the ratio of their time constants ( τθ/τs ) and on the ratio of their strength ( w/g ) . It predicts that if these two ratios are close to 1 then both feedback processes contribute similarly to the rhythm . In the example shown here this is a good approximation for the active phase . However , for the silent phase , this intuitive rule fails , because an additional scaling factor must be introduced to compare the contributions of the two different negative feedback types . This scaling factor is significantly different from unity for the silent phase; it reflects the fact that the divisive feedback process , being a multiplicative factor to the activity , has very little effect at low activity ( i . e . , during the silent phase ) . The blockade approach suggests that the subtractive process might be more important in setting the silent phase duration , since blocking this process affected the silent phase duration more often than the active phase duration . In this way it provides a piece of information that is missed by the correlative approach . However , similar effects of the blockade on AP and SP durations were found in cases where the ratio of time constants was different ( and different effects when that ratio was identical ) , contradicting the correlative approach and , as shown in Figure 7 , contradicting our measure of the relative contributions of each process . Furthermore , unlike the correlative approach , the blockade experiment suggests a strong effect of θ0 ( which biases the input/output relationship of the system ) . In general , however , this parameter has little effect on the contribution of each process ( cf . Figure 6 ) . These disappointing results from the two experimental approaches are due to their well known pitfalls: passive observation only establishes an association without proving a causal relationship , while perturbations to the system , such as blockade experiments , can qualitatively change the system being studied . The use of total blockade may be considered extreme . A partial block can potentially be more informative than a complete block because a small enough perturbation may indicate a trend in a component's influence and preclude switching the system to a different mode of operation ( see e . g . , [17] , [18] , [19] ) . In other words , if the perturbation is small enough the effect on the activity may be close to linear so the effect of the partial block can be quantified and provide information on the role of the process that is partially blocked . However , partial blockade cannot provide a quantitative measure with the properties ( summation to 1 ) of the C values developed here . Our approach , instead , is to use small perturbations to the time constants of the feedback processes and look at the effect immediately following the perturbations . This minimizes the perturbation to the system , while quantifying the relative contribution of the two slow processes to the rhythmic behavior . This method could be applied to many oscillatory systems that rely on the interplay between positive feedback and several negative feedback processes . However , for most known experimental conditions , this method seems impossible to implement . To apply the method requires 1 ) the ability to change the time constants of the variables of interest one by one , 2 ) these changes must remain small but have measurable effects and 3 ) the system's behavior immediately after the changes must be measured , without waiting for transients to die out . For example , in the context of a neural network , there is currently no technique available to change the time constant of synaptic depression by a small amount , quickly and without affecting other network parameters . Thus , in many cases , the question of determining the contributions of different negative feedback processes in rhythm generation ( using our approach ) may only be addressed with computational models . One example in which our approach could be used in an experimental setting is the electrical oscillatory activity of single cells . The mathematical formalism used to describe the mean activity of an excitatory network is similar to the Hodgkin-Huxley formalism commonly used to describe the electrical activity of excitable cells [8] , [20] , [21] , [22] . In excitable cells , the sodium or calcium channels generate voltage-dependent inward current , providing fast positive feedback that increases membrane potential , while the delayed activation of outward potassium currents and inactivation of the inward currents provide negative feedback . An outward current has an opposite influence to the excitatory inward current and therefore provides subtractive feedback; on the other hand the inactivation of an inward current is a multiplicative term reducing the amount of positive feedback and therefore is a divisive feedback process . Preliminary results with the Hodgkin-Huxley model of nerve excitability [20] in a repetitive spiking mode suggest that while both sodium current inactivation and potassium ( K+ ) current activation contribute to terminating an action potential , it is mostly the de-activation of the K+ current that initiates the next spike ( J . Tabak , unpublished results ) . This could be verified experimentally for electrically compact cells using the dynamic clamp technique , which allows one to introduce a model-generated ionic current into a cell [23] , [24] . For example , one could pharmacologically block the Na+ current , then re-introduce it into the cell using the dynamic clamp . Because the added current is computed from a model , it would be possible to change its inactivation time constant by a desired amount and measure the effect of this perturbation on the duration of the spike or interspike interval . To our knowledge , a similar experiment has been done only once , to show that increasing the inactivation time constant of a low-voltage-activated calcium current would result in longer bursts in invertebrate neurons [25] . While both divisive and subtractive feedback can in principle terminate bursts in neurons [26] it is usually the latter that is considered to regulate bursting , in the form of slow , calcium-activated K+ currents . The experiment described in [25] provided strong support for a role of low-voltage-activated calcium current inactivation ( divisive feedback ) in burst termination . Modeling is being established as an essential tool for understanding complex biological systems [27] , complementing experimental approaches . But more than mere simulations of systems of differential equations , which are akin to experiments , it is the qualitative analysis of the models that provides new insights into a system's dynamics . Qualitative model analysis techniques include phase plane and bifurcation analysis , but these techniques become more difficult to apply as the number of variables increases . The commonly used fast-slow analysis , which simplifies model analysis by formally separating the equations into fast and slow subsystems , may have limited usefulness when many variables operate on the same time scale . An extension of fast-slow analysis that can deal with many variables operating on the same time scale is the Dominant Scale Method ( DMS ) [28] . This method follows one variable of interest along an oscillatory trajectory ( for instance , voltage in a cellular oscillator model ) and determines the sensitivity of this variable at each point on its trajectory to other variables that are present in its differential equation . During different epochs of time , only a few variables may significantly affect the primary variable , so the model can be reduced to a few variables during each epoch . Thus , a complex model is transformed into a sequence of simpler models using only the dominant variables , and qualitative analysis of the dynamics is possible for each successive epoch [29] . The DMS can evaluate the relative contributions of variables that have different roles , unlike the measure presented here . However , our approach uses the sensitivity of observable features of the system behavior ( AP and SP ) , not the sensitivity of a variable to other variables . For this reason , one may use our approach to identify cases where a variable has very little effect on the primary variable but nevertheless controls the duration of a given phase of the activity ( as discussed in last section of Results ) . Our approach to measure the contribution of feedback processes to rhythmic behavior is to compute the sensitivity of the AP and SP to the time constants for these processes . Other techniques that use sensitivities of observables of a system to control parameters are Metabolic Control Analysis and Biochemical Systems Theory [30] , [31] , which have been used to analyze metabolic and gene regulatory networks . Important features of these approaches include summation theorems , for instance the sum of the sensitivities of the level of a metabolite to control coefficients is equal to 1 . A similar summation theorem holds in our analysis , where the contributions of the two slow variables to the AP or SP duration sum to 1 . These techniques are usually applied to the control of steady states , but they have also been used to describe how observables such as the period and amplitude of an oscillatory system are regulated by control parameters [32] , [33] . The control of these observables is usually distributed across control parameters [33] . Here , we found that the control of the active phase is distributed across the divisive and subtractive feedback processes , but control of the silent phase is mostly operated by the subtractive process , θ . That is , θ is the “rate limiting factor” in the termination of the silent phase . Finally we mention parameter search techniques , which are usually developed to find parameter sets that lead to a target behavior . These techniques can also be used to determine what parameter changes must be done to qualitatively affect a system's activity and provide information about the robustness of such activity [34] . Furthermore , by finding different parameter sets that produce similar system behavior , it is possible to determine the relationships between parameters that allow a behavior to be maintained [35] or to evaluate how each model parameter influence a given characteristic of the behavior using nonlinear regression [36] . This “database approach” indirectly provides information about the role played by some variables of the system and how a variable can take over when another variable is eliminated . It can be used to explore the behavior of a model in different regions of parameter space [37] . An intriguing observation is that different parameter combinations in a wide area of parameter space may produce similar oscillatory patterns [38] . If two distinct parameter sets produce the same system behavior , does this mean that a variable might have different roles in different networks that produce similar activity ? This question could be answered with a combination of the database approach and the analysis technique developed here . We have developed a computational method to quantify the relative contributions of feedback processes to active and silent phases of episodic activity . We have considered a case involving both subtractive and divisive processes . If both processes have similar strength and time scales , they contribute equally to terminate the active phase . This is consistent with our intuition and predicted by the correlative approach . Interestingly , it is the recovery from the subtractive process that sets the duration of the silent phase . This is because the divisive feedback is a multiplicative factor to the system's activity and therefore plays little role during the silent phase . Thus , different phases of the activity are controlled differently by the negative feedback processes . Experimental methodologies do not in general provide this type of information , so the determination of the relative contributions of different variables to a biological system's activity will usually require the development of a computational model . The method presented here can be applied to a wide array of oscillatory systems .
|
As modern experimental techniques uncover new components in biological systems and describe their mutual interactions , the problem of determining the contribution of each component becomes critical . The many feedback loops created by these interactions can lead to oscillatory behavior . Examples of oscillations in biology include the cell cycle , circadian rhythms , the electrical activity of excitable cells , and predator-prey systems . While we understand how negative feedback loops can cause oscillations , when multiple feedback loops are present it becomes difficult to identify the dominant mechanism ( s ) , if any . We address the problem of establishing the relative contribution of a feedback process using a biological oscillator model for which oscillations are controlled by two types of slow negative feedback . To determine which is the dominant process , we first use standard experimental methodologies: either passive observation to correlate a variable's behavior to system activity , or deletion of a component to establish whether that component is critical for the system . We find that these methods have limited applicability to the determination of the dominant process . We then develop a new quantitative measure of the contribution of each process to the oscillations . This computational method can be extended to a wide variety of oscillatory systems .
|
[
"Abstract",
"Introduction",
"Model",
"Results",
"Discussion"
] |
[
"neuroscience/theoretical",
"neuroscience",
"diabetes",
"and",
"endocrinology/neuroendocrinology",
"and",
"pituitary",
"biophysics/theory",
"and",
"simulation",
"computational",
"biology/computational",
"neuroscience",
"computational",
"biology/metabolic",
"networks",
"computational",
"biology/signaling",
"networks",
"computational",
"biology/systems",
"biology"
] |
2011
|
Quantifying the Relative Contributions of Divisive and Subtractive Feedback to Rhythm Generation
|
Simple spatial interaction models of human mobility based on physical laws have been used extensively in the social , biological , and physical sciences , and in the study of the human dynamics underlying the spread of disease . Recent analyses of commuting patterns and travel behavior in high-income countries have led to the suggestion that these models are highly generalizable , and as a result , gravity and radiation models have become standard tools for describing population mobility dynamics for infectious disease epidemiology . Communities in Sub-Saharan Africa may not conform to these models , however; physical accessibility , availability of transport , and cost of travel between locations may be variable and severely constrained compared to high-income settings , informal labor movements rather than regular commuting patterns are often the norm , and the rise of mega-cities across the continent has important implications for travel between rural and urban areas . Here , we first review how infectious disease frameworks incorporate human mobility on different spatial scales and use anonymous mobile phone data from nearly 15 million individuals to analyze the spatiotemporal dynamics of the Kenyan population . We find that gravity and radiation models fail in systematic ways to capture human mobility measured by mobile phones; both severely overestimate the spatial spread of travel and perform poorly in rural areas , but each exhibits different characteristic patterns of failure with respect to routes and volumes of travel . Thus , infectious disease frameworks that rely on spatial interaction models are likely to misrepresent population dynamics important for the spread of disease in many African populations .
Human mobility patterns underlie the spread of infectious diseases across spatial scales . Theoretical models of human mobility have been used to understand the spatial spread of influenza , cholera , and malaria , for example [1–20] as well as to design targeted interventions [1 , 5 , 20–22] . These models rely almost exclusively on two frameworks , the gravity model and the more recent radiation model , both of which were developed to describe regular commuting patterns in high-income settings [23–26] . In the absence of easily available data on travel behavior , these models are increasingly also being applied to models of infectious disease dynamics in low and middle-income settings . Despite the need for robust epidemiological models in places like Sub-Saharan Africa , it remains unclear if gravity and radiation models adequately describe mobility in these populations . Geographic constraints and economic drivers of travel may be substantially different in Sub-Saharan Africa than in high-income countries . Many African countries are experiencing rapid demographic changes and may have poor transportation infrastructure . Many populations remain subsistence farmers living in rural areas with limited economic opportunities , public resources , and infrastructure [27 , 28] . Kenya exhibits many of these attributes , for example , including highly variable population density and substantial geographic diversity , ranging from the major urban commercial center of Nairobi ( population density ~4 , 510/km2 ) to the pastoral communities in the northern part of the country ( see Fig 1A ) . Only 7% of Kenyan roads are paved , often those in and out of the capital , as is common in many African countries . Despite these constraints , mobility in many parts of the continent has increased dramatically over the last decade [29] , with rural-to-urban migration , seasonal travel , and extensive travel for agricultural and casual laboring jobs forming important components of the emerging ecology of African populations [30] . Data sources describing these travel patterns are rare , however [31 , 32] , so gravity ( parameterized ) and radiation ( parameter-free ) models offer intuitive and tractable analytical frameworks for describing human mobility patterns ( Fig 1B and 1C ) . In their simplest forms both models rely on spatial population data as a proxy for the economic attractiveness of a place and assume a decay in the amount of travel with distance [23 , 26 , 33] . In the standard gravity model , Euclidean distance is often used to inform this decay rate , whereas in the radiation model , an individual is likely to travel to the nearest location that offers an improvement in current working conditions ( measured via population size ) , with decay described as a function of the populations and distance between locations . Extensions have been proposed to improve the standard gravity model to include more relevant driving factors of travel such as the percentage of the population that is male , economic activity measures , and land cover [33] . Other formulations of the gravity model constrain the origin and destination travel and has been shown to outperform the standard gravity model [25] . By definition , neither encompasses different types of journeys or different trip durations , which are often important aspects of travel for the spread of infectious disease . Validating these frameworks , in low and middle-income settings in particular , remains challenging . Mobile phone data sets that are routinely collected by mobile operators provide an important new source of information about the dynamics of populations on an unprecedented scale , and provide an opportunity to measure human mobility directly for entire populations [23 , 25 , 34–38] . The adoption of mobile phone technologies in Africa in particular has been rapid , providing the opportunity to study population dynamics of countries for the first time [31 , 35] . Given the difficulties of obtaining and sharing mobile call data records ( CDRs ) , however , it will be important to assess whether measured travel patterns in different regions support the use of gravity and radiation models in places without mobility data . Here , we first review previous infectious disease models that have explicitly included a model of human mobility , and highlight the disparity between models and types of mobility quantified that are used for simulation versus those including epidemiological data . Next , we analyze CDRs from nearly 15 million subscribers in Kenya over the course of a year to test gravity and radiation models in this East African context . We test both gravity and radiation models in the context of Kenya , and show that both models fail to capture important aspects of mobility measured using CDRs , but in different ways . We then test their utility to describe travel over various trip durations and show differences in travel patterns between shorter and longer journeys . Finally , we highlight situations when each model outperforms the other and discuss a method to choose between models using the amount of travel .
We first reviewed infectious disease models that explicitly include human mobility ( Fig 2 ) . Here , we focused only on models that represent the first time a particular formulation was used , and not subsequent versions of the same framework ( see Supporting Information for the inclusion criteria and overview of papers included , S1 Table ) . We also included only papers that explicitly modeled both the disease dynamics and mobility patterns and have excluded papers that have not modeled both components ( for example see [4 , 10–17] ) . We found nineteen studies , eleven of which were purely simulated epidemiological models [10–20] and eight of which included fits to epidemiological data [1–9] . Although these studies analyzed a range of infectious diseases , nearly all simulation studies analyzed the spread of influenza in high-income countries using commuting as the relevant type of mobility ( 8 out of 11 ) . The majority of examples used a gravity model ( 10 papers ) [2–8 , 10 , 13 , 17 , 18] and nearly all of the examples using a radiation model were for simulated disease dynamics only ( 2 papers ) [11 , 12] . The examples that were fit to disease data were more varied although the majority were from low-income countries ( 5 ) [1 , 2 , 4 , 5 , 39] and described regional movement patterns ( see Fig 2 ) [1–5] . Thus , simple gravity model frameworks are very commonly used to understand the regional spread of infectious disease in low-income settings , highlighting the importance of testing their validity and generalizability . To test the performance of gravity and radiation models in an African setting , we analyzed regional travel across Kenya from de-identified call detail records ( CDRs ) at the cell tower level from 14 , 816 , 521 individual subscribers between June 2008 and June 2009 , representing 92% of mobile market share ( data previously described in [36] ) . We have previously used these data to quantify general mobility patterns as well as travel between locations of interest , and compared to census and travel survey data [23 , 34 , 36] . Here we focused on regional movement patterns since this is the most common spatial resolution of mobility models used in conjunction with epidemiological data in low-income settings , and regional travel represents a major source of uncertainty in disease models currently . We calculated all journeys between 69 Kenyan districts over the course of one year , ignoring travel within districts . On this spatial scale , movements between districts within the timespan of one day are almost nonexistent ( see Supporting Information ) , so we used the most commonly used tower each day to approximate each subscriber’s location on a daily basis . We fit both an unconstrained gravity model and a radiation model to data , representing the total number of journeys of the course of the year between districts over the course of the data set ( one year , see Materials and Methods ) . We fit a number of constrained gravity models , although these did not perform as well as the standard gravity model ( see Supporting Information ) . Here , we assume that travel measured by CDRs reflects “true” travel behavior , although it is likely to suffer from different types of bias , like any data on human mobility . The models varied widely in their ability to capture observed travel patterns in and out of rural versus urban districts , as illustrated by travel from Nairobi and Garissa ( Fig 3 ) . Nairobi is densely populated ( total population of district 3 . 4 million , 10% of the country’s population ) encompassing the capital and major population and economic center in the country . Located in the middle of the country , this district is well connected by paved roads to the second largest city ( Mombasa 1 . 2 million ) as well as to western Kenya , where nearly half of the population resides . In this setting , both models were able to identify the primary destination locations accurately , although the radiation model predicted travel to a wider range of locations than observed in the CDRs ( Fig 3A and 3B and 3C ) . Garissa , on the other hand , is a sparsely-populated low-income district bordering Somalia , and likely to be more similar to other rural areas in Africa than to high-income countries . For travel originating from Garissa , the predicted volumes and routes of travel were very different from empirical estimates ( Fig 3D and 3E and 3F ) . Most strikingly , the gravity model predicted travel to a much wider range of destinations than observed , and the radiation model failed to identify the primary travel destination . These errors would be likely to lead models to over-estimate the spread of disease in the first case , and under-estimate disease importation into the capital city in the second . The models diverged systematically in their predictions with regard to travel volume ( Fig 4A and 4B ) with the gravity model consistently over-predicting travel and the radiation model under-predicting travel ( mean ratio of data to predicted results was 0 . 83 and 35 . 03 , respectively , see S1 Fig ) . Although the gravity model using Euclidean distance gave a better overall fit to the data than the radiation model ( gravity model adjusted R2: 0 . 786 , radiation model adjusted R2: 0 . 014 , see S2 Fig ) , this was due to the radiation model’s consistent failure to capture large volumes of human travel between major population centers . We hypothesized that one reason for the poor performance of both models in rural areas may be the impact of physical accessibility and road infrastructure on travel . This is likely to be particularly important in Sub-Saharan Africa , and adjusted measures of distance based on estimated travel times , as well as road distance , have been developed for these regions [40] . We re-fit the parameters of the gravity model using road distance and travel times and found that Euclidean distance between district centroids provided the most accurate overall predictions of travel volume across a range of scenarios including the full dataset , travel to and from the capital , and large urban centers ( reduction in deviance: 63%-87% ) . Interestingly , in rural areas road distance noticeably outperformed all other distance measures , suggesting that travel time estimates may not accurately reflect human behavior in these regions ( see Fig 4C and S2–S4 Tables ) . We compared the distribution of errors from both models to identify “rules of thumb” for using gravity and radiation models to estimate volumes of travel ( see Materials and Methods ) . We assumed the empirical error from each model should be normally distributed and categorized the travel routes that fall more than 2 standard deviations away from the mean ( 10% of routes , see Fig 5A , KS-statistic = 0 . 2481 , p<0 . 001 ) . In general , both models failed to adequately capture travel from rural areas of intermediate population density over shorter distances , especially in the western part of Kenya in the Rift Valley and Western provinces ( Fig 5B and see S5 Table for further analysis ) . Importantly , these rural regions of intermediate population density are likely to represent sizeable fractions of African populations; in Kenya these provinces where mobility models are systematically failing account for nearly 40% of the population ( 14 million individuals ) . Neither the gravity nor the radiation model was consistently a superior choice , exhibiting different spatial patterns of performance ( see Fig 5B ) , however in general the radiation model outperformed the gravity model for low amounts of travel and vice a versa . We calculated a naïve gravity factor , i . e . a gravity model without any parameters fit ( pop_i * pop_j /d ( i , j ) ) and performed a logistic regression to determine which flows were better predicted using each model ( see Fig 5C , Supporting Information for regression results using just populations or distance as covariates , S6 Table–adjusted R2 = 0 . 5703 , p<0 . 001 ) . We observed a strong positive correlation between the gravity factor , which is proportional to the total amount of travel , and the odds of using a gravity model ( Fig 5C ) . These results imply that a gravity model is more likely to capture the spread of disease between major urban centers , but a radiation model may be more appropriate for modeling rural-to-urban migration . In both cases , model performance varied substantially in different locations . An important consideration for spatial models of infectious disease dynamics is the length of journeys , since it will help determine both the number of onward infections generated by an imported case and the risk of exposure to infection of a traveling individual . Gravity and radiation models do not make explicit assumptions about trip durations , but since they were primarily developed to model commuting patterns they may not be appropriate for understanding journeys of varying length . We therefore analyzed the spatial dimensions of human travel for trips of varying duration ( see Table 1 , Fig 6A ) [19] and the ability of each model to describe these different trips . As expected , the total number of trips between districts decreased as journey duration increased ( see Figs 6 and S3 and S4 ) . For example , the number of trips lasting between one and two weeks was on average two orders of magnitude greater than the number of trips lasting at least four months ( see Supporting Information ) . The major routes of travel also varied with the trip duration , with longer journeys being associated with increasing distances and larger population sizes at the destination , with Nairobi in particular becoming an increasingly important longer-term destination ( see Figs 6B and S5 and S6 ) . We refit a separate gravity model for each duration of travel ( note that we do not refit the radiation model since it is parameter free ) ( see Materials and Methods , Supporting Information ) . This analysis highlights the difference in the major routes of travel , where the destination population parameter increased as the trip duration increased and the importance of distance in the model decreased ( see Table 1 ) .
Our analysis suggests that gravity and radiation models do not adequately capture movements measured by mobile phones in rural and intermediate population density areas in Kenya , areas that are characteristic of many settings in Sub-Saharan Africa . These findings bring into question the universal applicability of these frameworks , and have important implications for estimating the risk of infectious disease importation , for example . Given the ubiquity of gravity and radiation models in epidemiological frameworks , we focused on validating these fundamental frameworks as opposed to examining more recent modifications [24 , 41] . One important caveat is that we have compared these theoretical models to travel measured via mobile phones , which may be affected by variable ownership and usage patterns , particularly in poor or rural areas [37 , 42] . Nevertheless , mobile phone data currently represent one of the most direct ways to measure regional population dynamics , especially in low-income settings where commuting and travel survey data may be patchy [42 , 43] . Here we have focused on the regional and inter-settlement spatial scales that can be measured using CDRs , but an important next step–particularly for infectious disease prediction–is to find appropriate data to examine the performance of gravity and radiation models on extremely local spatial and short temporal scales . Future work devoted to developing a generalizable model that can accurately capture travel in Sub-Saharan Africa , particularly in rural areas with intermediate population densities , will be an important priority for the development of appropriate frameworks for a description of African population dynamics . As more mobile phone data sets become available , the generalizability of our results can be confirmed in other countries assuming mobile phone data provides a reasonable sample of the underlying population [35] . Spatial interaction models can provide researchers with the ability to model population dynamics in low-income and data sparse settings , such as Sub-Saharan Africa . However the universality of these models is questionable , especially when describing rural travel in geographically and economically heterogeneous settings . Applications reliant on the underlying population dynamics derived from either model , such as understanding the spread of an infectious disease or the role of travel on economic activity , are likely to miss important routes and types of travel commonly found in Sub-Saharan Africa .
We analyzed anonymized mobile phone call data records ( CDR ) aggregated to the routing mobile phone tower level . These data were provided by the incumbent mobile phone ( 92% market share at the time of data acquisition ) provider in Kenya and included the timings of calls and SMS from 14 , 816 , 512 subscribers from June 2008—June 2009 ( with February 2009 missing from the data set ) . As in previous studies [23 , 34–36] , subscribers represented in the CDRs as unique hashed IDs to protect their privacy . Twelve billion mobile phone communications were analyzed , recording activity at a total of 11 , 920 routing towers . All subscriber data was aggregated to the district level to further preserve anonymity . In the interest of protecting privacy , limited access to the anonymized data was made available to a select set of researchers . Each entry in a CDR contains an anonymized caller ID , anonymized receiver ID , date , duration , and tower routing number for both the caller and receiver . From the CDRs , the geographic location of the caller and receiver could be approximated based on the unique longitude and latitude coordinates for each mobile phone tower . Using the CDRs , a location for each subscriber every time they either made/received a call ( or SMS ) could be obtained . For each day in the data set , subscribers were assigned a single tower location [35 , 36] . If the subscriber made at least one call on that day , then the location of the majority routing tower was assigned [35 , 36] . If there was no majority routing tower , then for the most likely set of towers , a single tower was randomly chosen . If the subscriber had not made a call on that day , then the location of their most recent routing tower was assigned . This provided a time series of tower location for each subscriber on each day . As done in previous studies , trips are calculated by observing when a subscriber’s tower location has changed from the previous day for the entire data set ( 12 months of data ) [35 , 36] . We aggregated towers to the district-level based on the tower’s location and only trips between towers in different districts were considered to quantify regional movement patterns . In comparison to a number of other studies analyzing spatial interaction models and infectious disease dynamics , we did not focus on commuting patterns since we are describing regional movement patterns , e . g . movement within a country as opposed to within a single city , and few subscribers change districts between daytime and nighttime . We investigated the ability of these models to describe travel over various durations . Using the CDR , we were able to quantify both the number of trips between districts as well as the duration of those journeys ( in days based on the daily location of each subscriber ) . For each trip between districts , we counted the number of days the subscriber spent in the visited district . Using the mobile phone data , we compared all travel ( every trip between all pairs of districts over the entire data set ) to journeys lasting various durations where trips were stratified into six separate groups ( see Table 1 ) . The category , All travel includes every trip taken between districts , regardless of trip duration . We grouped all trips lasting at least four months into a single category due to the length of the data set ( in total 12 months of CDR data ) . The gravity model is the most common spatial interaction model where the amount of travel ( Nij ) between two locations ( i , j ) is dependent on their populations ( popi , popj ) and the physical distance separating them ( d ( i , j ) ) [26 , 35 , 36]: Nij=popiαpopjβd ( i , j ) γk where the parameters α , β , γ , k are fit based on a Poisson distribution [35 , 44] . We choose the fitting method based on Flowerdew [44] where the amount of travel estimated using regression assuming a Poisson family . The gravity model has been extensively used to model mobility in conjunctions with models of the spatial spread of infectious diseases [2–8 , 10 , 13 , 17 , 18] . There have been a number of proposed additions and modifications to the gravity model including adding covariates such as the percentage of the population that is male [33] or putting constraints on the number of trips [25] such as the singly or doubly constrained model . Here , we fit the simplified since the model without covariates is the most commonly used for disease modeling [1 , 19–25 , 29 , 33 , 34] . We also fit the origin singly constrained model , production singly constrained model , and doubly constrained model ( see Supporting Information ) . However , these non-constrained simplified gravity models outperformed these three models ( increase in sum of square errors: non-constrained – 37 . 9% , origin constraint – 39 . 5% , destination constraint – 39 . 5% , and doubly constrained – 39 . 5% ) . We also fit separate gravity models to each set of data describing various trip durations ( see Table 1 and Supporting Information ) . Recently , the radiation model has been proposed as an improvement on the gravity model [23] . It draws its original inspiration from a gravity model , but is a stochastic process that only requires information on the population distribution and is parameter free . In this model , the average amount of travel ( Nij ) between two locations ( i , j ) is dependent upon their populations and the total population in the circle of radius rij centered at i where rij = d ( i , j ) ( the circle population is sij ) : < Nij > = Ni ( popipopj / ( popi + sij ) ( popi + popj + sij ) . Ni=popi ( TcT ) where Tc/T is the proportion of the population who travels . If no data is available to fit the radiation model , then Tc/T is fixed and not fit . Here we fit this percentage to the actual data ( see Supporting Information ) and the optimum value is Tc/T = 1 . Recently , extensions to this model have been proposed to reflect human behavior in employment choice using various functional forms [24] , however we have focused on the most commonly used model formulation . We analyzed three separate distance measures ( Euclidean , road , and travel time between both district polygon centroids and district population weighted centroids ) [40] . Euclidean distance was measured as the straight-line distance between centroids . Road distance was measured using the road network data from the Kenya National Bureau of Statistics . These data with land cover data ( www . africover . org ) and topography data ( http://srtm . csi . cgiar . org/ ) were used to construct a ‘friction surface’ that was used to estimate travel time distances , following previously outlined methods [40] . The travel time is based on a measure of friction between one location and another that takes into account land cover types , transport network and gradient . In general , this measure is thought to be more representative of the ease of human travel access across a landscape since it takes into account impedances to travel . Similar to previous methods , water bodies , land cover , slope and the road network datasets were combined on a 1km spatial resolution grid to empirically derive travel speeds [40] . These travel speeds were assigned to each land use type and modified based on the topography to create a ‘friction surface’ . This surface was used to estimate travel times between locations using least cost methods [40] , with those locations defined by population weighted centroids ( defined using high resolution population maps provided by the WorldPop Project: www . worldpop . org . uk ) , where these centroids were automatically adjusted to be located to the nearest road . Correlations between the measures can be found in the Supporting Information . We calculated the error of each model as the difference between the data and estimated value ( error = log ( data ) –log ( predicted ) ) . We took the standard assumption that these errors were normally distributed with mean 0 and standard deviation of 1 . For any value not in the confidence interval , we suggest that caution should be taken when utilizing these estimates ( see S5 Table ) ( about 10% of the pairs of locations were eliminated ) . Of routes between districts that were well described by either model , we calculated a gravity factor , gm = pop_i * pop_j / dist ( i , j ) which is a equivalent to the gravity model without fitting any parameters , as a proxy for the amount of travel between locations . Using this covariate , we then performed a logistic regression to determine when the radiation model or gravity model produced lower errors compared to the actual data for these routes . logitgm ( p ) = b0 , gm + b1 , gm * Xgm where p is the probability of choosing a gravity model over a radiation model , Xgm is the gravity factor we calculated . As this value increases , i . e . the amount of travel increases , the probability of choosing a gravity model over a radiation model increases ( see Fig 5C ) .
|
Human mobility underlies many social , biological , and physical phenomena , including the spread of infectious diseases . Analyses in high-income countries have led to the notion that populations obey universal rules of mobility that are effectively captured by spatial interaction models . However , communities in Africa may not conform to these rules since the availability of transport and geographic barriers may impose different constraints compared to high-income settings . We use anonymous mobile phone data from ~15 million subscribers to quantify different spatial and temporal scales of mobility within Kenya and test their performance with respect to this measurement of human travel . We find that standard models systematically fail to describe regional mobility in Kenya , with poor performance in rural areas . Epidemiological models that rely on these frameworks may therefore fail to capture important aspects of population dynamics driving disease spread in many African populations .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Evaluating Spatial Interaction Models for Regional Mobility in Sub-Saharan Africa
|
Our ability to parse our acoustic environment relies on the brain’s capacity to extract statistical regularities from surrounding sounds . Previous work in regularity extraction has predominantly focused on the brain’s sensitivity to predictable patterns in sound sequences . However , natural sound environments are rarely completely predictable , often containing some level of randomness , yet the brain is able to effectively interpret its surroundings by extracting useful information from stochastic sounds . It has been previously shown that the brain is sensitive to the marginal lower-order statistics of sound sequences ( i . e . , mean and variance ) . In this work , we investigate the brain’s sensitivity to higher-order statistics describing temporal dependencies between sound events through a series of change detection experiments , where listeners are asked to detect changes in randomness in the pitch of tone sequences . Behavioral data indicate listeners collect statistical estimates to process incoming sounds , and a perceptual model based on Bayesian inference shows a capacity in the brain to track higher-order statistics . Further analysis of individual subjects’ behavior indicates an important role of perceptual constraints in listeners’ ability to track these sensory statistics with high fidelity . In addition , the inference model facilitates analysis of neural electroencephalography ( EEG ) responses , anchoring the analysis relative to the statistics of each stochastic stimulus . This reveals both a deviance response and a change-related disruption in phase of the stimulus-locked response that follow the higher-order statistics . These results shed light on the brain’s ability to process stochastic sound sequences .
To understand soundscapes , the brain parses incoming sounds into distinct sources and tracks these sources through time . This process relies on the brain’s ability to sequentially collect information from sounds as they evolve over time , building representations of the underlying sources that are invariant to the randomness present in real-world sounds , while being flexible to adapt to changes in the acoustic scene . Extracting these representations from ongoing sounds is automatic and effortless for the average listener , but the underlying computations in the brain are largely unknown . To better understand how the brain processes real-world sounds , we investigate how the brain builds invariant representations from sounds containing randomness . Invariant representations of sound sources are referred to in the literature as regularities , where regularity extraction is the brain’s ability to access these representations for use in auditory scene analysis [1 , 2] . We differentiate between two types of regularities: deterministic regularities that describe a repeating or predictable pattern , and stochastic regularities that contain some randomness and are not fully predictable . Deterministic regularities can be as simple as a repeating tone or sequence , or they can be quite complex , for example: two interleaved deterministic patterns [3] , an abstract pattern within a single acoustic feature ( “falling pitch within tone-pairs” [4] ) or one spanning multiple features ( “the higher the pitch , the louder the intensity” [5] ) . The signature trait of deterministic regularities is the absence of ambiguity: a new sound can immediately be interpreted as a continuation of or a deviation from the regularity with certainty . Stochastic regularities , on the other hand , are characterized by the lack of certainty , as their inherent randomness leaves room for multiple possible interpretations of a sequence of sounds . A new sound belongs to a stochastic regularity probabilistically according to how well it fits relative to other possible interpretations . For example , consider a sequence of tones with frequencies drawn from an arbitrary distribution , such as in [6] . Each tone could be drawn from the same distribution as the preceding tones or it could be drawn from a new distribution . Given a new tone , deciding between these two alternatives ( i . e . , “same or different ? ” ) cannot be done with certainty , but rather proportionally to how likely the new tone is given its preceding context . Implicit in this example is that the brain is able to extract meaningful contextual information from previously heard sounds to characterize the stochastic regularity , and represent this abstracted information for interpreting new sounds . One possible mechanism for how the brain represents stochastic regularities is through statistical estimates , which entails extracting representative parameters from observed sensory cues [7] . The nature and extent of statistics collected by the brain remains unknown . Previous studies have focused on the marginal statistics of tones within a sequence , showing that the brain is sensitive to changes in mean and variance [8 , 9] . We refer to these as lower-order statistics , describing sounds independent of their context . In the present work , we investigate whether the brain collects higher-order statistics about the dependencies between sounds over time; namely , we examine how the brain gathers information about the temporal covariance structure in a stochastic sequence of sounds . We use melody stimuli with pitches based on random fractals , which exhibit long-range dependencies and cannot be described solely by lower-order statistics . We specifically use random fractals because of their ecological relevance: previous work has demonstrated the presence of random fractals in music [10] , speech [11] , and natural sounds [12] and shown the brain is sensitive to the amount of randomness , or entropy , in random fractal melodies [13 , 14] . Change detection experiments are well-suited for investigating regularity extraction , where the task is to detect deviation from an established regularity in a sequence of sounds . A detection can be reported behaviorally or recorded in the brain’s response ( e . g . , the mismatch negativity , MMN ) . A correct detection indicates the brain is sensitive to the tested regularity , for a change response is necessarily preceded by knowledge of what is being changed . Change detection experiments in the auditory domain using electroencephalography ( EEG ) and magnetoencephalography ( MEG ) have shown the brain is sensitive to a wide range of deterministic regularities [15–17] . Stochastic regularities , however , have mostly been studied using discrimination experiments , where the task is to differentiate between different regularities , with both behavioral [12] and brain imaging results [8 , 13 , 14 , 18 , 19] showing the brain is sensitive to various stochastic regularities . Compared to discrimination , the change detection paradigm more closely mirrors how the brain processes sounds in the real world , where boundaries between sound sources are not known a priori , but must be inferred from changes in ongoing sound . The mechanisms needed for change detection may differ depending on the type of regularity . With deterministic regularities , the brain can explicitly test whether each incoming sound deviates from the extracted pattern or not with near certainty . Deviation from a stochastic regularity , on the other hand , emerges gradually as evidence is accumulated over time , causing a delay in the perceived moment of change proportional to the amount of evidence needed to detect the change . This uncertainty unavoidably introduces variability in perception across trials and across subjects , which is particularly problematic for time-locked analyses such as in EEG , where low SNR necessitates many repetitions and precise temporal alignment across trials and subjects to get meaningful results . To account for this variability and facilitate the study of stochastic regularities in change detection , we need a suitable perceptual model of the mechanisms for extracting and using regularities in a changing scene to guide our analysis . While there have been several theoretical accounts of regularity extraction in the brain [2 , 20–23] , there are very few mathematical implementations of these concepts into concrete models for tracking regularities in sound inputs . One popular model is the CHAINS model , which examines pattern discovery and competition between alternate partitions of a sequence into concurrent , interleaved patterns [24] . This model has been very insightful in shedding light on principles of bistable perception in stream segregation; yet , its limitation to deterministic patterns impedes its applicability to stochastic regularities in the signal . Another model , IDyOM , initially formulated for application to music perception , uses information-theoretic principles to model auditory expectation , collecting occurrences of previously seen events to build predictions , similar to the n-grams used in language models for speech recognition or text processing [25] . While the IDyOM model is able to capture the statistical structure of both stochastic and deterministic regularities , it is formulated to operate only on a discrete , unordered , small set of possible events , and therefore does not generalize well to sounds that vary on a continuum like pitch or loudness . In this work , we employ a Bayesian framework to model the tracking of sensory statistics by the auditory system [26] . One of the advantages of Bayesian theory is that it is agnostic to priors and underlying distributions , optimally integrating priors and sensory evidence in the inference process . In particular , this framework makes minimal assumptions on the stationarity of the observed sequence and offers an ideal scheme for tracking statistics and detecting change in underlying probability distributions . Bayesian frameworks have been widely used in various incarnations to model data ranging from financial markets to human behavior in reading-inference , change detection , and reinforcement learning tasks [26–31] . In the present application , this mathematical platform allows us to directly probe the degree of optimality in brain processes observed and test alternative hypotheses for the computations involved . Here , we adapt this Bayesian framework for perceptual processing to investigate the extent to which auditory statistical information is represented in memory . We introduce perceptual parameters to the model that represent resource limitations ( i . e . , finite working memory and observation noise ) and provide constraints on performance that are valuable to interpret sub-optimal detection performance and variability across listener behaviors . By fitting the model to human behavior from a series of change detection experiments , we can explore questions regarding auditory stochastic regularity extraction: Which statistics are sufficient to explain human behavior ? How do the perceptual parameters of the model account for differences in behavior across subjects ? Finally , we use the model to guide analysis of EEG data , revealing effects that would be otherwise hidden using conventional EEG analyses . Results are presented in three parts: the first section presents psychophysics results from a series of change detection experiments , the second section introduces the model and presents results from fitting the model to human behavior , and the third section presents neural results obtained by using the model to guide EEG analysis . We believe this model opens up new avenues into investigating how the brain collects information from stochastic sounds that are more relevant to everyday perception .
A series of experiments probed listener’s ability to detect changes in fractal melodies . Stimuli were constructed from melodies at four levels of randomness or entropy in pitch ( both terms used interchangeably ) . Melody entropy is parameterized by β , where β = 0 corresponds to the highest entropy ( white noise ) , and entropy decreases as β increases ( see Fig 1a for examples of fractal melodies at different levels of β ) . Lower-order statistics ( mean and variance ) were normalized across the melody . Half-way through the melody , only the higher-order statistics change ( see Fig 1b for examples of change stimuli ) . The task in all experiments was the same: detect a change in entropy of the melody . To model brain processes involved in extracting information from stochastic sequences , we adapted a Bayesian sequential prediction model [26] , incorporating perceptually plausible constraints to the model’s resources . Fig 3 shows a schematic of the model and its outputs . The input to the model is a sequence of observations {xt}; in our case , the observations are the pitches from the melody stimulus . The model sequentially builds a predictive distribution of the next observation at time t + 1 given the previous observations: P ( xt+1|x1:t ) . Observations are assumed to be distributed according to some probability distribution with unknown parameters θ . At unknown changepoint times , the parameters θ change , and all following observations are drawn from this new distribution , independent of observations before the change . Observations between changepoints drawn from the same distribution form a run , and the time between changepoints is referred to as the run-length . If the most recent changepoint ( or equivalently , the current run-length ) were known , the independence of observations across changepoints could be used to simplify the prediction equation: given the current run-length rt , P ( xt+1|rt , x1:t ) = P ( xt+1|rt , xt − rt+1:t ) . Because changepoints must rather be inferred from the observations , the model maintains multiple hypotheses across all possible run-lengths and integrates them to predict the next observation: P ( x t + 1 | x 1 : t ) = ∑ r t P ( x t + 1 | r t , x t - r t + 1 : t ) P ( r t | x 1 : t ) In the sum , the prediction given run-length rt ( the first term ) is weighted by the model belief that the current run-length is rt ( the second term ) . With each incoming observation , these run-length beliefs are incrementally updated and a new belief is added with length zero and weight π , the change-prior , re-weighting the predictions in the sum . The change-prior is a parameter of the model that represents the prior belief that a change will occur at any time before evidence for a change is observed ( see S1 Text ) . Maintaining multiple run-length hypotheses is a key aspect of the model . Rather than making a hard decision about when a changepoint occurs and “resetting” the statistics , the model uses the observations as evidence to weight different interpretations of the sequence . In the present application of the model , the generating distribution is assumed to be a D-dimensional multivariate Gaussian with unknown mean and covariance structure , where the dimensionality D specifies the amount of temporal dependence in the model . As new observations come in , the model incrementally collects sufficient statistics whose form depends on D ( see Methods ) . Here , we ask whether human behavior from Experiments 1–2 can be captured by a model that collects marginal lower-order statistics ( D = 1 , i . e . , mean and variance ) or if higher-order statistics ( D = 2 , i . e . , mean , variance , and covariance ) are needed; we refer to these two versions of the model as the LOS model and HOS model , respectively . Next , we examined neural underpinnings of higher-order stochastic regularities in the brain . In an experiment structured similarly to Experiments 1 and 2 above , listeners were asked to detect changes in stochastic melodies while EEG was simultaneously recorded from central and frontal locations on the scalp . Stimuli were generated at two levels of entropy ( i . e . , one change degree ) with both INCR and DECR change direction .
We introduced a perceptual model for stochastic regularity extraction and applied this model to the same change detection experiments as our human listeners . We used different sets of statistics in the model to determine which best replicate human behavior: a lower-order statistics ( LOS ) model that collects the marginal mean and variance of tone pitches or a higher-order statistics ( HOS ) model that additionally collects the covariance between successive tone pitches . Comparing the performance range for LOS and HOS models to human performance , we showed that higher-order statistics are necessary to capture all human behaviors , while lower-order statistics are insufficient to capture the full range of subject behaviors . This disparity strongly suggests the brain is collecting and using higher-order statistics about the temporal dependencies between incoming sounds . Furthermore , the model revealed effects in EEG that are only discernible using higher-order statistics: ERP evidence showed an MMN response elicited by tones that are surprising according to the higher-order statistics of the preceding melody , and cortical phase-locking was disrupted at the changepoints specified by the HOS model . Interestingly , both LOS and HOS models were able to replicate behavior from poorer performing subjects ( d′ < 1 . 5 ) , but the LOS model is unable to mirror behaviors with high hit-rates without also increasing the FA-rate ( Fig 4a ) . Intuition states that marginal statistics within the local context ( i . e . , short memory or small m ) might be effective for detecting changes in local variance in the fractal sequences; this notion is supported by the model , where m = 10 tones yields the best LOS model performance ( Fig 4b ) . Yet this local LOS model , with limited sampling in the statistics collected , is unable to match the performance exhibited by better performing subjects . In other words: if listeners ( or the LOS model ) rely solely on marginal statistics , then their ability to accurately flag changes in random fractal structure is highly constrained . Furthermore , relying on low-order statistics should elicit an effect of the direction of change ( from low to high entropy or vice versa ) on the hit-rates . Behavioral data shows no such effect of change direction on behavioral hit-rates ( Experiments 1 and 1b ) , which further corroborates that listeners cannot be solely relying on lower-order statistics . While these results strongly argue for the brain’s ability to track higher-order statistics in sound sequences , they do not disagree with previous work demonstrating sensitivity to lower-order statistics [8 , 9] . Rather , by designing a task in which higher-order statistics are beneficial , we show that listeners are additionally sensitive to the temporal covariance structure of stochastic sequences . We also do not argue that the statistics collected by the brain are limited to these , but could include longer-range covariances . We performed the same analysis using a D = 3 model that collects covariance between non-adjacent sounds , but it did not provide any improvement over the D = 2 ( HOS ) model . This merely means that for our stimuli , there was no additional information to aid in change detection beyond the adjacent covariances . Additional experiments with stimuli that specifically control for this are needed to determine the extent of the temporal range of statistics collected by the brain . By their very nature , the stimuli used here exhibit a high degree of irregularity and randomness across individual instances of sequences . For the listener , deciding where the actual change in regularity occurs in a particular stimulus is a noisy process that arises with some level of uncertainty . Perceptually , most trials do not contain an obvious “aha moment” when change is detected; rather , the accumulation of evidence for statistical change emerges as a gradual process . Similarly from a data analysis point of view , determining the exact point of time when the statistical structure undergoes a notable change is a nontrivial problem , given that the perception of statistical change is not binary but continuous and varies both between trials and between listeners . As such , the study of stochastic processing hinges on the use of a model that is well-matched to the computations occurring in the brain , combining the right granularity of statistics with the right scheme for cue integration and decision making . And with the introduction of perceptual parameters to the model , we gain flexibility in the behaviors that can be reproduced by the model with clear interpretation as to the computational constraints leading to these behaviors . Taking a close look at individual differences through the lens of the model , we were able to inspect underlying roots of this variability . Rather than simply a difference in decision threshold ( i . e . , “trigger-happiness” ) , we argue the variability across listeners was due to individual differences in the limitations of the perceptual system . We incorporated these limitations into the model via perceptual parameters . The memory parameter represents differences in working memory capacity [37 , 38] , and the observation noise parameter represents individual differences in pitch perception fidelity [39] . We should note that these parameters may also be capturing other factors that affect listener performance like task engagement , neural noise , or task understanding , which could be contributing noise to these results . By fitting the model to individual listeners through their behavior , we showed correlates between human performance and the perceptual parameters of the model , and we found that neither perceptual parameter alone was adequate to fit all subjects . Rather than a nuisance , we see the inter-subject variability in these results as a consequence of individual differences in the perceptual system that are amplified by the uncertainty present in stochastic processing . We found effects of the statistical context on the neural response . First , examining ERP responses to individual tones , we found an enhanced P2 response to large frequency deviations in low-entropy melodies compared to high-entropy melodies and a frontal distribution of this difference consistent with sources in the auditory cortex . This result corresponds with previous work where large frequency deviations that were less likely given the previous context showed an enhanced P2 amplitude [32] . Similarly , we interpret this result reflecting a release from adaptation , where the low-entropy melody has a narrow local frequency range . Importantly , we do not see an MMN effect , arguably because frequency deviation alone is too crude to provide an adequate definition of “deviant” with our stochastic stimuli: large frequency deviations do not always violate the regularities in our stimuli , which may explain the lack of an observable MMN in the average differential response . Using the fitted model , we were able to tease out distinct surprisal effects on the tone ERP that differ both in statistics and in temporal integration window: the LOS surprisal measured how well each tone was predicted by the lower-order statistics of the local context , while the HOS surprisal measured how well each tone was predicted by the higher-order statistics of the longer context , as fit by the model to individual behavior . Because LOS and HOS surprisal are partially ( and unavoidably ) correlated , both LOS and HOS surprisal were included in a single regression in order to find components in the ERP that correlate with each independent of the other [34] . We found an enhanced P2 amplitude with increasing LOS surprisal that is similar in amplitude and latency to the P2 difference discussed above; indeed , LOS surprisal provides a similar definition of regularity to the ERP analysis based on melody entropy above , for large frequency deviations are always “deviants” according to the lower-order statistics . We again attribute this increased P2 to a release from adaptation . Consequently , we can then attribute the MMN response to HOS surprisal as a deviance response according to higher-order statistics independent from lower-order adaptation effects . There has been much discussion on whether the MMN response is truly a deviance response or merely due to adaptation [40 , 41] . Many experiments suffer from confounding frequency deviance with regularity deviance , making it difficult to definitively attribute MMN to one or the other . With our stochastic stimuli differing in higher-order statistics , we were able to disentangle the two interpretations . We again stress that this result is not in conflict with previous results showing effects of lower-order statistics on the MMN [8 , 42] , because deviants in these studies could also be considered deviants according to their higher-order statistics ( i . e . , the HOS model reduces to the LOS model when the covariance between sounds is zero ) . Finally , we found a disruption in the brain’s phase-locked response to tone onsets that coincides with HOS model changepoints , where the model detects a change in the higher-order statistics of each stimulus . Contrasting various controls using different estimates of when the change point occurs , we observed a notable phase disruption with changes in higher-order statistics only . The change in phase synchrony across trials could be due to the combined modulation of multiple ERPs to tones following the changepoint , or it could reflect a change in the oscillatory activity of the brain , which has been shown to correspond with both changes in predictive processing and attentional effects [43 , 44] . Further experimentation is needed to determine the source of this disruption . Importantly , this analysis takes into account the stochastic nature of the stimuli by interpreting the statistical structure of each stimulus through the model , rather than with the changepoint used to generate the stimuli ( i . e . , the “nominal” changepoint ) . The model presented here fits in nicely with existing theoretical formulations for predictive processing and object formation in perception [2 , 45] , as well as Bayesian descriptions of the perceiving brain [46–48] . Importantly , the model does not assume stationarity in the sound environment , and it can adapt to changes in regularity at any time . To achieve this , the model hypothesizes two mechanisms in the brain: first , the brain builds representations by collecting statistical estimates from sounds over time; second , the brain maintains multiple hypotheses for how to interpret the previously heard sound sequence . These hypotheses are represented explicitly in the model by statistical estimates collected over different time-windows , each of which gives a prediction for future sounds . Prediction errors are then used to update the beliefs in each hypothesis , weighting hypotheses proportional to the amount of evidence relative to alternative hypotheses . This competition between concurrent hypotheses is crucial for robust interpretation in the presence of uncertainty . As new sounds deviate from the prediction of the current best hypothesis , beliefs shift to a new dominant hypothesis ( and set of statistical estimates ) that better explains the previous sounds; the beliefs therefore reflect the dynamics of a changing environment . While in this work we used the model to investigate processing of regularities in pitch , we believe the same machinery can be applied to other auditory dimensions ( e . g . , loudness , timbre , spatial location ) and extended to other sufficient statistics to test different representations of regularities in sound . Additionally , the model is not limited to detecting changes , as was demonstrated here using a simple decision rule . Rather , it is a perceptual model of stochastic predictive processing that can operate in the presence of changes , as the brain does while perceiving real-world , dynamic sound environments .
The perceptual model is an extension of the Bayesian Online Changepoint Detection model described in [26] , which was designed to predict incoming observations sequentially given previous observations in the presence of unknown changepoints . Model code is available at https://engineering . jhu . edu/lcap/ . Because the model assumes observations are generated from a D-dimensional Gaussian distribution , there exists a closed-form solution for the predictive distribution that depends only on the sufficient statistics θ ^ t ( r t ) = { μ ^ t ( r t ) , Σ ^ t ( r t ) } , i . e . , the D-dimensional sample mean and sample covariance at time t collected over the previous rt observations [52] . We modify these sufficient statistics with perceptual parameters to add perceptually plausible constraints to the model . Observation noise ( n ) adds a constant variance to the predictive distribution , and the memory parameter ( m ) puts a limit on the number of past observations used in the prediction , effectively “forgetting” observations outside of this window . We then have a modified expression for the sufficient statistics for run-length rt at time t that incorporates the two perceptual parameters: θ ^ t ( r t ) = { { μ ^ t ( r t ) , Σ ˜ t ( r t ) } , r t < m { μ ^ t ( m ) , Σ ˜ t ( m ) } , r t ≥ m where Σ ˜ t ( r t ) = Σ ^ t ( r t ) + n 2 I D is the sample covariance with added observation noise n , and ID is the D-dimensional identity matrix .
|
To understand our auditory surroundings , the brain extracts invariant representations from sounds over time that are robust to the randomness inherent in real-world sound sources , while staying flexible to adapt to a dynamic environment . The computational mechanisms used to achieve this in auditory perception are not well understood . Typically , this ability is investigated using predictable patterns in a sequence of sounds , asking: “How does the brain detect the pattern embedded in this sequence ? ” , which does not generalize well to natural listening . Here , we examine processing of stochastic sounds that contain uncertainty in their interpretation , asking: “How does the brain detect the statistical structure instantiated by this sequence ? ” . We present human experimental evidence employing a perceptual model for predictive processing to show that the brain collects higher-order statistics about the temporal dependencies between sounds . In addition , the model reveals correlates between task performance and individual differences in perception , as well as deviance effects in the neural response that would be otherwise hidden with conventional , stimulus-driven analyses . This model guides our interpretation of both behavioral and neural responses in the presence of stimulus uncertainty , allowing for the study of perception of more natural stimuli in the laboratory .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"brain",
"electrophysiology",
"social",
"sciences",
"electrophysiology",
"neuroscience",
"geometry",
"perception",
"clinical",
"medicine",
"cognitive",
"psychology",
"mathematics",
"human",
"performance",
"statistics",
"(mathematics)",
"brain",
"mapping",
"bioassays",
"and",
"physiological",
"analysis",
"event-related",
"potentials",
"thermodynamics",
"electroencephalography",
"neuroimaging",
"research",
"and",
"analysis",
"methods",
"change",
"detection",
"entropy",
"imaging",
"techniques",
"fractals",
"clinical",
"neurophysiology",
"behavior",
"mathematical",
"and",
"statistical",
"techniques",
"electrophysiological",
"techniques",
"physics",
"psychology",
"neurophysiology",
"physiology",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"cognitive",
"science",
"statistical",
"methods"
] |
2018
|
Detecting change in stochastic sound sequences
|
The antisaccade task is a classic paradigm used to study the voluntary control of eye movements . It requires participants to suppress a reactive eye movement to a visual target and to concurrently initiate a saccade in the opposite direction . Although several models have been proposed to explain error rates and reaction times in this task , no formal model comparison has yet been performed . Here , we describe a Bayesian modeling approach to the antisaccade task that allows us to formally compare different models on the basis of their evidence . First , we provide a formal likelihood function of actions ( pro- and antisaccades ) and reaction times based on previously published models . Second , we introduce the Stochastic Early Reaction , Inhibition , and late Action model ( SERIA ) , a novel model postulating two different mechanisms that interact in the antisaccade task: an early GO/NO-GO race decision process and a late GO/GO decision process . Third , we apply these models to a data set from an experiment with three mixed blocks of pro- and antisaccade trials . Bayesian model comparison demonstrates that the SERIA model explains the data better than competing models that do not incorporate a late decision process . Moreover , we show that the early decision process postulated by the SERIA model is , to a large extent , insensitive to the cue presented in a single trial . Finally , we use parameter estimates to demonstrate that changes in reaction time and error rate due to the probability of a trial type ( pro- or antisaccade ) are best explained by faster or slower inhibition and the probability of generating late voluntary prosaccades .
In the antisaccade task ( [1]; for reviews , see [2 , 3] ) , participants are required to saccade in the contralateral direction of a visual cue . This behavior is thought to require both the inhibition of a reflexive saccadic response towards the cue and the initiation of a voluntary eye movement in the opposite direction . A failure to inhibit the reflexive response leads to an erroneous saccade towards the cue ( i . e . , a prosaccade ) , which is often followed by a corrective eye movement in the opposite direction ( i . e . , an antisaccade ) . As a probe of inhibitory capacity , the antisaccade task has been widely used to study psychiatric and neurological diseases [3] . Notably , since the initial report [4] , studies have consistently found an increased number of errors in patients with schizophrenia when compared to healthy controls , independent of medication and clinical status [5–8] . Moreover , there is evidence that an increased error rate constitutes an endophenotype of schizophrenia , as antisaccade deficits are also present in non-affected , first-degree relatives of diagnosed individuals ( for example [5 , 7]; but for negative findings see for example [9 , 10] ) . Unfortunately , the exact nature of the antisaccade deficits and their biological origin in schizophrenia remain unclear . One path to improve our understanding of these experimental findings is to develop generative models of their putative computational and/or neurophysiological causes [11] . Generative models that capture the entire distribution of responses can reveal features of the data that are not apparent when only considering summary statistics such as mean error rate ( ER ) and reaction time ( RT ) [12–15] . Additionally , this type of model can potentially relate behavioral findings in humans to their biological substrate . Here , we apply a generative modeling approach to the antisaccade task . First , we introduce a novel model of this paradigm based on previous proposals [16–20] . For this , we formalize the ideas introduced by Noorani and Carpenter [17] and extend them into what we refer to as the Stochastic Early Reaction , Inhibition and late Action ( SERIA ) model . Second , we apply both models to an experimental data set of three mixed blocks of pro- and antisaccades trials with different trial type probability . More specifically , we compare several models using Bayesian model comparison . Third , we use the parameter estimates from the best model to investigate the effects of our experimental manipulation . We found that there was positive evidence in favor of the SERIA model when compared to our formalization of the model proposed in [17] . Moreover , the parameters estimated through model inversion revealed the complexity of the decision processes underlying the antisaccade task that is not obvious from mean RT and ER . This paper is organized as follows . First , we formalize the model developed in [17] and introduce the SERIA model . Second , we describe our experimental setup . Third , we present our behavioral findings in terms of summary statistics ( mean RT and ER ) , the comparison between different models , and the parameter estimates . Finally , we review our findings , discuss other recent models , potential future developments , and translational applications .
All participants gave written informed consent before the study . All experimental procedures were approved by the local ethics board ( Kantonale Ethikkomission Zürich , KEK-ZH-Nr . 2014-0246 ) . In this section , we derive a formal description of the models evaluated in this paper . We start with a formalized version of the model proposed by Noorani and Carpenter in [17] and proceed to extend it . Their approach resembles the model originally proposed by Camalier and colleagues [21] to explain RT and ER in the double step and search step tasks , in which participants are either asked to saccade to successively presented targets or to saccade to a target after a distractor was shown . Common to all these tasks is that subjects are required to inhibit a prepotent reaction to an initial stimulus and then to generate an action towards a secondary goal . Briefly , Camalier and colleagues [21] extended the original ‘horse-race’ model [16] by including a secondary action in countermanding tasks . In [17] , Noorani and Carpenter used a similar model in combination with the LATER model [22] in the context of the antisaccade task by postulating an endogenously generated inhibitory signal . Note that this model , or variants of it , have been used in several experimental paradigms ( reviewed in [20] ) . Here , we limit our discussion to the antisaccade task . Following [17] , we assume that the RT and the type of saccade generated in a given trial are caused by the interaction of three competing processes or units . The first unit up represents a command to perform a prosaccade , the second unit us represents an inhibitory command to stop a prosaccade , and the third unit ua represents a command to perform an antisaccade . The time t required for unit ui to arrive at threshold si is given by: si=rit , ( 1 ) siri=t , ( 2 ) where ri represents the slope or increase rate of unit ui , si represents the height of the threshold , and t represents time . We assume that , on each trial , the increase rates are stochastic and independent of each other . The time and order in which the units reach their thresholds si determines the action and RT in a trial . If the prosaccade unit up reaches threshold before any other unit at time t , a prosaccade is elicited at t . If the antisaccade unit arrives first , an antisaccade is elicited at t . Finally , if the stop unit arrives before the prosaccade unit , an antisaccade is elicited at the time when the antisaccade unit reaches threshold . It is worth mentioning that , although this model is motivated as a race-to-threshold model , actions and RTs depend only on the arrival times of each of the units and ultimately no explicit model of increase rates or thresholds is required . Thus , for the sake of clarity , we refer to this approach as a ‘race’ model , in contrast to ‘race-to-threshold’ models that explicitly describe increase rates and thresholds . Formally ( but in a slight abuse of language ) , the two random variables of interest , the reaction time T ∈ [0 , ∞[ and the type of action performed A ∈ {pro , anti} , depend only on three further random variables: the arrival times Up , Us , Ua ∈ [0 , ∞[ of each of the units . The probability of performing a prosaccade at time t is given by the probability of the prosaccade unit arriving at time t , and the stop and antisaccade unit arriving afterwards: p ( A=pro , T=t ) =p ( Up=t ) p ( Ua>t ) p ( Us>t ) . ( 3 ) The probability of performing an antisaccade at time t is given by p ( A=anti , T=t ) =p ( Ua=t ) p ( Up>t ) p ( Us>t ) +p ( Ua=t ) ∫0tp ( Us=τ ) p ( Up>τ ) dτ . ( 4 ) The first term on the right side of Eq 4 corresponds to the unlikely case that the antisaccade unit arrives before the prosaccade and the stop units . The second term describes trials in which the stop unit arrives before the prosaccade unit . It can be decomposed into two terms: p ( Ua=t ) ∫0tp ( Us=τ ) p ( Up>τ ) dτ=p ( Ua=t ) ( p ( Us<t ) p ( Up>t ) +∫0tp ( Us=τ ) p ( τ<Up<t ) dτ ) ( 5 ) =p ( Ua=t ) ( p ( Us<t ) p ( Up>t ) +∫0tp ( Us<τ ) p ( Up=τ ) dτ ) ( 6 ) The term p ( Ua=t ) ∫0tp ( Us<τ ) p ( Up=τ ) dτ describes the condition in which the prosaccade unit is inhibited by the stop unit allowing for an antisaccade . Note that if the prosaccade unit arrives later than the antisaccade unit , the arrival time of the stop unit is irrelevant . That means that we can simplify Eq 4 to p ( A=anti , T=t ) =p ( Ua=t ) ( p ( Up>t ) +∫0tp ( Us<τ ) p ( Up=τ ) dτ ) . ( 7 ) Eqs 3 and 7 constitute the likelihood function of a single trial , and define the joint probability of an action and the corresponding RT . We refer to this likelihood function as the PRO-Stop-Antisaccade ( PROSA ) model . It shares the central assumptions of [17] , namely: ( i ) the time to reach threshold of each of the units is assumed to depend linearly on the rate r , ( ii ) it includes a stop unit whose function is to inhibit prosaccades and ( iii ) there is no lateral inhibition between the different units . Finally , ( iv ) RTs are assumed to be equal to the arrive-at-threshold times . Note that the RT distributions are different from the arrival time distributions because of the interactions between the units described above . The main difference of this model compared to [17] is that we do not exclude a priori the possibility of the antisaccade unit arriving earlier than the other units . Aside from this , both models are conceptually equivalent . The PROSA model is characterized by a strict association between units and action types . In other words , the unit up leads unequivocally to a prosaccade , whereas the unit ua always triggers an antisaccade . This implies that if the distribution of the arrival times of the units is unimodal and strictly positive , the PROSA model cannot predict voluntary slow prosaccades with a late peak , or in simple words , the PROSA model cannot account for slow , voluntary prosaccades that have been postulated in the antisaccade task [23] . Similarly , it has been argued that prosaccade RT can be described by the mixture of two distributions ( for example [2 , 22] ) . To account for this , we introduce the Stochastic Early Reaction , Inhibition and Late Action ( SERIA ) model . According to this model , and in analogy to the PROSA model , an early reaction takes place at time t if the early unit ue arrives before the late and inhibitory units , ul and ui , respectively . If the inhibitory or late unit arrives before the early unit , a late response is triggered at the time the late unit reaches threshold . Crucially , both early and late responses can trigger pro- and antisaccades with a certain probability . Thus , in parallel to the race processes which determine RTs , an independent , secondary decision process is responsible for which reaction is generated . Fig 1 shows the structure of the SERIA model . To formalize the concept of early and late responses , we introduce a new unobservable random variable that represents the type of response R ∈ {early , late} . The distribution of the RTs is analogous to the PROSA-model , such that , for instance , the probability of an early response at time t is given by p ( R=early , T=t ) =p ( Ue=t ) p ( Ui>t ) p ( Ul>t ) ( 8 ) where Ue , Ui , and Ul represent the arrival times of the early , inhibitory , and late units , respectively . The fundamental assumption of the SERIA model is that a secondary decision process , beyond the race between early , inhibitory , and late units , decides the action generated in a single trial . An initial approach to model this secondary decision process is to assume that the action type ( pro- or antisaccade ) is conditionally independent of the RT given the response type ( early or late ) . Hence , the distribution of RTs is not a priori coupled to the saccade type anymore; RT distributions for both pro- and antisaccades could in principle be bimodal , consisting of both fast reactive and slow voluntary saccades . Formally , the conditional independency assumption can be written down as p ( A , T|R ) =p ( A|R ) p ( T|R ) , ( 9 ) p ( A , T|R ) p ( R ) =p ( A|R ) p ( T|R ) p ( R ) , ( 10 ) p ( A , T , R ) =p ( A|R ) p ( T , R ) . ( 11 ) The term p ( A|R ) is simply the probability of an action , given a response type . We denote it as p ( A=pro|R=early ) =πe∈[0 , 1] , ( 12 ) p ( A=anti|R=early ) =1−πe , ( 13 ) p ( A=pro|R=late ) =πl∈[0 , 1] , ( 14 ) p ( A=anti|R=late ) =1−πl . ( 15 ) Since the type of response R is not observable , it is necessary to marginalize it out in Eq 11 to obtain the likelihood of the SERIA model: p ( A , T ) =p ( A , T , R=early ) +p ( A , T , R=late ) . ( 16 ) The complete likelihood of the model is given by substituting the terms in Eq 16 p ( A=pro , T=t ) =πep ( Ue=t ) p ( Ui>t ) p ( Ul>t ) +πlp ( Ul=t ) ( p ( Ue>t ) +∫0tp ( Ue=τ ) p ( Ui<τ ) dτ ) , ( 17 ) p ( A=anti , T=t ) = ( 1−πe ) p ( Ue=t ) p ( Ui>t ) p ( Ul>t ) + ( 1−πl ) p ( Ul=t ) ( p ( Ue>t ) +∫0t ( Ue=τ ) p ( Ui<τ ) dτ ) . ( 18 ) It is worth noting here that the PROSA model is a special case of the SERIA model , namely , it corresponds to the assumption that πe = 1 and πl = 0 . The SERIA model allows for bimodal distributions , as both early and late responses can be pro- and antisaccades . Importantly , one prediction of the model is that late prosaccades have the same distribution as late antisaccades . Until now , we have assumed that the competition that leads to late pro- and antisaccades does not depend on time in the sense that late actions are conditionally independent of RT . This assumption can be weakened by postulating a secondary race between late responses; this leads us to a modified version of the SERIA model , that we refer to as the late race SERIA model ( SERIAlr ) . The derivation proceeds similarly to the SERIA model , except that we postulate a fourth unit that generates late prosaccades instead of assuming that the late decision process is time insensitive . This version of the SERIA model includes an early unit ue that , for simplicity , we assume produces only prosaccades , an inhibitory unit that stops early responses ui , a late unit that triggers antisaccades ua , and a further unit that triggers late prosaccades up . As before , if the early unit reaches threshold before any other unit , a prosaccade is generated with probability p ( Ue=t ) p ( Ui>t ) p ( Ua>t ) p ( Up>t ) . ( 19 ) If any of the late units arrive first , the respective action is generated with probability: Antisaccade:p ( Ua=t ) p ( Up>t ) p ( Ue>t ) p ( Ui>t ) . ( 20 ) Prosaccade:p ( Up=t ) p ( Ua>t ) p ( Ue>t ) p ( Ui>t ) . ( 21 ) Finally , if the inhibitory unit arrives first , either a late pro- or antisaccade is generated with probability Antisaccades:p ( Ua=t ) p ( Up>t ) ( ∫0tp ( Ui=τ ) p ( Ue>τ ) dτ ) , ( 22 ) Prosaccades:p ( Up=t ) p ( Ua>t ) ( ∫0tp ( Ui=τ ) p ( Ue>τ ) dτ ) . ( 23 ) Implicit in the last two terms is the competition between the late units , which are assumed again to be independent of each other . Formally , this competition is expressed as the probability of , for example , the late antisaccade unit arriving before a late prosaccade p ( Ua = t ) p ( Up > t ) . A schematic representation of the model is shown in Fig 2 . This late race is similar to the Linear Ballistic Accumulation model proposed by [24] , although in that model decisions are seen as the result of a race of ballistic accumulation processes with fixed threshold but stochastic base line and increase rate . Here we only assume that the late decision process is a GO-GO race [21] . The likelihood of an action is given by summing over all possible outcomes that lead to that action: p ( A=pro , T=t ) =p ( Ue=t ) p ( Ui>t ) p ( Ua>t ) p ( Up>t ) +p ( Up=t ) p ( Ua>t ) p ( Ui>t ) p ( Ue>t ) +p ( Up=t ) p ( Ua>t ) ( ∫0tp ( Ui=τ ) p ( Ue>τ ) dτ ) , ( 24 ) p ( A=anti , T=t ) =p ( Ua=t ) p ( Up>t ) p ( Ui>t ) p ( Ue>t ) +p ( Ua=t ) p ( Up>t ) ( ∫0tp ( Ui=τ ) p ( Ue>τ ) dτ ) . ( 25 ) We have left out some possible simplifications in Eqs 24 and 25 for the sake of clarity . The conditional probability of a late antisaccade is given by the interaction between the late units , such that p ( Ua<Up ) =∫0∞p ( Ua=t ) p ( Up>t ) dt=1−p ( Up<Ua ) , ( 26 ) is analogous to the probability of a late antisaccade 1−πl in the SERIA model . This observation shows that the main difference between the SERIA and SERIAlr model is that the former postulates that the distribution of late pro- and antisaccades are equal and conditionally independent of the action performed , whereas the latter constrains the probability of a late antisaccade to be a function of the arrival times of the late units . The expected response time of late pro- and antisaccade actions is given by 1p ( Up<Ua ) ∫0∞tp ( Up=t ) p ( Ua>t ) dt , ( 27 ) 1p ( Ua<Up ) ∫0∞tp ( Ua=t ) p ( Up>t ) dt . ( 28 ) We will refer to these terms as the mean response time of pro- and antisaccade actions , in contrast to the mean arrival times , which are the expected value of any single unit . The models above can be further finessed to account for non-decision times δ by transforming the RT t to tδ = t−δ . The delay δ might be caused by , for example , conductance delays from the retina to the cortex . In addition , the antisaccade or late units might include a constant delay δa , which is often referred to as the antisaccade cost [1] . Note that the model is highly sensitive to δ because any RT below it has zero probability . In order to relax this condition and to account for early outliers , we assumed that saccades could be generated before δ at a rate η ∈ [0 , 1] such that the marginal likelihood of an outlier is p ( T<δ ) =p ( Tδ<0 ) =η . ( 29 ) For simplicity , we assume that outliers are generated with uniform probability in the interval [0 , δ]: p ( T=t ) =ηδift<δ . ( 30 ) Furthermore , we assume that the probability of an early outlier being a prosaccade was approximately 100 times higher than being an antisaccade . Because of the new parameter η , the distribution of saccades with a RT larger than δ needs to be renormalized by the factor 1−η . In the case of the PROSA model , for example , this means that the joint distribution of action and RT is given by the conditional probability p ( A=pro , T=tδ|tδ>0 ) =p ( Up=tδ ) p ( Ua>tδ−δa ) p ( Us>tδ ) , ( 31 ) p ( Ua<0 ) =0 , ( 32 ) p ( A=anti , T=tδ|tδ>0 ) =p ( Ua=tδ−δa ) ( p ( Up>tδ ) +∫0tδp ( Up=τ ) p ( Us<τ ) dτ ) . ( 33 ) A similar expression holds for the SERIA models . However , in the PROSA model a unit-specific delay is equal to an action-specific delay . By contrast , in the SERIA model both early and late responses can generate pro- and antisaccades . Thus , δa represents a delay in the late actions that affects both late pro- and antisaccades . The models discussed in the previous sections can be defined independently of the distribution of the rate of each of the units . In order to fit experimental data , we considered four parametric distributions with positive support for the rates: gamma [13] , inverse gamma , lognormal [25] and the truncated normal distribution ( similarly to [22] and [24] ) . Table 1 and Fig 3 summarize these distributions , their parameters , and the corresponding arrival time densities . We considered five different configurations: 1 ) all units were assigned inverse gamma distributed rates , 2 ) all units were assigned gamma distributed rates , 3 ) the increase rate of the prosaccade and stop units ( or early and the inhibitory units ) was gamma distributed but the antisaccade ( late ) unit’s increase rate was inverse gamma distributed , 4 ) all the units were assigned lognormal distributed rates or 5 ) all units were assigned truncated normal distributed rates . All the parametric distributions considered here can be fully characterized by two parameters which we generically refer to as k and θ . Hence , the PROSA model is characterized by the parameters for each unit kp , ka , ks , θp , θa , θs . The SERIA model can be characterized by analogous parameters ke , kl , ki , θe , θl , θi and the probabilities of early and late prosaccades πe and πl . In the case of the SERIAlr model , the probability of a late prosaccade is replaced by the parameters of a late prosaccade unit kp , θp . In addition to the unit parameters , all models included the non-decision time δ , the antisaccade ( or late unit ) cost δa , and the marginal rate of early outliers η . In this section , we describe the experimental procedures , statistical methods , and inference scheme used to invert the models above . The data is from the placebo condition of a larger pharmacological study that will be reported elsewhere . We aimed to answer three questions with the models considered here . First , we investigated which model ( i . e . PROSA , SERIA or SERIAlr ) explained the experimental data best , and whether all important qualitative features of the data were captured by this model . We did not have a strong hypothesis regarding the parametric distribution of the data and hence , comparisons of parametric distributions were only of secondary interest in our analysis . Second , we investigated whether reduced models that kept certain parameters fixed across trial types were sufficient to model the data . Third , we investigated how the probability of a trial type in a block affected the parameters of the model . Inference on the model parameters was performed using the Metropolis-Hastings algorithm [31] . To increase the efficiency of our sampling scheme , we iteratively modified the proposal distribution during an initial ‘burn-in’ phase as proposed by [32] . Moreover , we extended this method by drawing from a set of chains at different temperatures and swapping samples across chains . This method , called population MCMC or parallel tempering , increases the statistical efficiency of the Metropolis-Hasting algorithm [33] and has been used in similar contexts before [34] . We simulated 16 chains with a 5-th order temperature schedule [35] . For all but the models including a truncated normal distribution , we drew 4 . 1 × 104 samples per chain , from which the first 1 . 6 × 104 samples were discarded as part of the burn-in phase . When a truncated normal distribution was included ( models m5 , m10 , and m15 ) , the total number of samples was increased to 6 × 104 , from which 2 × 104 were discarded . The convergence of the algorithm was assessed using the Gelman-Rubin criterion [33 , 36] such that the R˜ statistic of the parameters of the model was aimed to be below 1 . 1 . When a simulation did not satisfy this criterion , it was repeated until 99 . 5 percent of all simulations satisfied it . Models were scored using their log marginal likelihood or log model evidence ( LME ) . This is defined as the log probability of the data given a model after marginalizing out all its parameters . When comparing different models , the LME corresponds to the log posterior probability of a model under a uniform prior on model identity . Thus , for a single subject with data y , the posterior probability of model k , given models 1 to n is p ( mk|y ) =p ( y|mk ) p ( mk ) ∑i=1np ( y|mi ) p ( mi ) =p ( y|mk ) ∑i=1np ( y|mi ) . ( 35 ) Importantly , this method takes into account not only the accuracy of the model but also its complexity , such that overparameterized models are penalized [37] . A widely used approximation to the LME is the Bayesian Information Criterion ( BIC ) which , although easy to compute , has limitations ( for discussion , see [38] ) . Here , we computed the LME through thermodynamic integration [33 , 39] . This method provides robust estimates and can be easily computed using samples obtained through population MCMC . One important observation here is that the LME is sensitive to the prior distribution , and thus can be strongly influenced by it [40] . We addressed this issue in two ways: On one hand and as mentioned above , we defined the prior distribution of the increase rates of all models in terms of the same mean and variance . This implies that the priors were equal up to their first two moments , and hence all models were similarly calibrated . On the other hand , we complemented our quantitative analysis with qualitative posterior checks [33] as shown in the results section . Besides comparing the evidence of each model , we also performed a hierarchical or random effects analysis described in [38 , 41] . This method can be understood as a form of soft clustering in which each subject is assigned to a model using the LME as assignment criterion . Here , we report the expected probability of the model ri , which represents the percentage of subjects that is assigned to the cluster representing model i . This hierarchical approach is robust to population heterogeneity and outliers , and complements reporting the group-level LME . Finally , we compared families of models [42] based on the evidence of each model for each subject summed across conditions . In addition to a Bayesian analysis of the data , we used classical statistics to investigate the effect of our experimental manipulation on behavioral variables ( mean RT and ER ) and the parameters of the models . We have suggested previously [11 , 43 , 44] that generative models can be used to extract hidden features from experimental data that might not be directly captured by , for example , standard linear methods or purely data driven machine learning techniques . In this sense , classical statistical inference can be boosted by extracting interpretable data features through Bayesian techniques . Frequentist analyses of RT , ER , and parameter estimates were performed using a mixed effects generalized linear model with independent variables subject ( SUBJECT ) , prosaccade probability ( PP ) with levels PP20 , PP50 and PP80 , and when pro- and antisaccade trials were analyzed together , trial type ( TT ) . The factor SUBJECT was always entered as a random effect , whereas PP and TT were treated as categorical fixed effects . In the case of ER , we used the probit function as link function . Analyses were conducted with the function fitglme . m in MATLAB 9 . 0 . The significance threshold α was set to 0 . 05 . All likelihood functions were implemented in the C programming language using the GSL numerical package ( v . 1 . 16 ) . Integrals without an analytical form or well-known approximations were computed through numerical integration using the Gauss-Kronrod-Patterson algorithm [45] implemented in the function gsl_integration_qng . The sampling routine was implemented in MATLAB ( v . 8 . 1 ) and is available as a module of the open source software package TAPAS ( www . translationalneuromodeling . org/tapas ) .
Forty-seven subjects ( age: 23 . 8 ± 2 . 9 ) completed all blocks and were included in further analyses . A total of 27072 trials were recorded , from which 569 trials ( 2% ) were excluded ( see Table 4 ) . Both ER and RT showed a strong dependence on PP ( Fig 5 and Table 5 ) . Individual data is included in the S1 Dataset and is displayed in S1 Fig . The mean RT of correct pro- and antisaccade trials was analyzed independently with two ANOVA tests with factors SUBJECT and PP . We found that in both pro- ( F2 , 138 = 46 . 9 , p < 10−5 ) and antisaccade trials ( F2 , 138 = 37 . 3 , p < 10−5 ) the effect of PP was significant; with higher PP , prosaccade RT decreased , whereas the RT of correct antisaccades increased . On a subject-by-subject basis , we found that between the PP20 and PP80 conditions , 91% of the participants showed increased RT in correct antisaccade trials , while 81% demonstrated the opposite effect ( a decrease in RT ) in correct prosaccade trials . Similarly , there was a significant effect of PP on ER in both prosaccade ( F2 , 138 = 376 . 1 , p < 10−5 ) as well as in antisaccade ( F2 , 138 = 347 . 0 , p < 10−5 ) trials . This effect was present in all but one participant in antisaccade trials and in all subjects in prosaccade trials . Exemplary RT data of one subject in the PP50 condition is displayed in Fig 6 . Finally , we investigated how some of the parameters of the model were related to each other across subjects . Because it has been commonly reported that schizophrenia is related with higher ER , but also with increased antisaccade RT , an interesting question is whether higher late-action response times are correlated with the percentage of late errors and inhibition failures , i . e . , early saccades that are not stopped . We found that the response time of late pro ( F1 , 135 = 13 . 6 , p < 0 . 001 ) and antisaccades ( F1 , 135 = 7 . 1 , p < 0 . 01 ) was negatively correlated with the probability of a late error ( Fig 13 ) , but no significant interaction between PP and response time was found ( pro: F2 , 135 = 1 . 7 , p = 0 . 19; anti: F2 , 135 = 0 . 3 , p = 0 . 76 ) . Hence , late responders tended to make fewer late errors , suggesting a speed/accuracy trade-off in addition to the main effect of PP . We further considered the question whether the percentage of inhibition failures was correlated with the expected arrival time of the late antisaccade unit in antisaccade trials ( Fig 13 right ) . Note that the number of inhibition failures is the same in both trial types in a constrained model , but inhibition failures are errors in antisaccade trials and correct early reactions in prosaccade trials . We found that these parameters were not significantly correlated ( F2 , 135 = 1 . 2 , p = 0 . 26 ) . This was also the case when we considered the expected response time of late prosaccades in prosaccade trials ( not displayed; F2 , 135 = 0 . 0 , p = 0 . 98 ) . Fig 14 illustrates the posterior distribution of late errors and inhibition failures of two representative subjects as estimated using MCMC . Clearly , PP induced strong differences in the percentage of inhibition failures and late errors in prosaccade trials in both subjects . The effect of PP is less pronounced in late errors in antisaccade trials . The posterior distributions also illustrate how the SERIAlr model can capture individual differences: For example , the percentage of late prosaccade errors in the PP80 condition and the percentage of inhibition failures across all conditions are clearly different in each subject .
Our results show that both RT and ER depend on PP . While this was a highly significant factor in our study , there are mixed findings in previous reports . ER in antisaccade trials was found to be correlated with TT probability in several studies [29 , 46 , 47] . However , this effect might depend on the exact implementation of the task [47 , 48] . Changes in prosaccade ER similar to our study have been reported by [29] and [48] . Studies in which the type of saccade was signaled at fixation prior to the presentation of the peripheral cue do not always show this effect [47] . The results on RTs are less consistent in the literature . Our findings of increased anti- and decreased prosaccade RTs with higher PP are in line with the overall trend in [29] , and with studies in which the cue was presented centrally [47] . Often , there is an additional increase in RT in the PP50 condition [29 , 47] , which was visible in our data as a slight increase in RT in the PP50 condition on top of the linear effect of PP . Overall , RTs in our study were relatively slow compared to studies in which the TT cue was separated from the spatial cue [46 , 47] . However , a study with a similar design and added visual search reported even slower RTs in both pro- and antisaccades [29] . Formal comparison of generative models can offer insight into the mechanisms underlying eye movement behavior [11] and might be relevant in translational neuromodeling applications , such as computational psychiatry [49–53] . Here , we have presented what is , to our knowledge , the first formal statistical comparison of models of the antisaccade task . For this , we formalized the model introduced in [17] and proceeded to develop a novel model that relaxes the one-to-one association of early and late responses with pro- and antisaccades , respectively . All models and estimation techniques presented here are openly available under the GPLv3 . 0 license as part of the open source package TAPAS ( www . translationalneuromodeling . org/tapas ) . Bayesian model comparison yielded four conclusions at the family level . First , the SERIA models were clearly favored when compared to the PROSA models . Second , including a late race between actions representing late pro- and antisaccades ( SERIAlr ) resulted in an increase in model evidence , compared to a model not including a late race ( SERIA ) . Third , models in which the race parameters of the early and inhibitory unit were constrained to be equal across TT had a higher LME than models in which all parameters were free . Hence , the effect of the cue in a single trial was limited to the late action , and did not affect the race between an early and inhibitory process . This constitutes an important external validation , as it means that model comparison does favor a model which respects the temporal order of the experiment: Information about TT is only available after the stimulus was presented and , thus , it is unlikely to have an impact on fast reactive responses . Fourth , early responses were nearly always prosaccades . Crucially , these four conclusions are based on family-wise comparison across all parametric distribution of the increase rate of the units . A further consequence of our findings is that two independent and qualitatively different decision processes lead to an antisaccade: the race process between early and inhibitory units , and the secondary decision process that generates late responses . A separation of decisions into a ‘where’ and a ‘when’ component has been proposed by [54] , but mainly in conceptual terms . However , model comparison showed that these two components ( ‘where’ and ‘when’ ) cannot be completely dissociated and that time plays a role in late decisions . Nevertheless , the assumption that action type and arrival time of late responses were independent yielded a good fit to this particular data set , suggesting that it is , in many cases , an acceptable approximation to assume a time-independent late decision process . The most obvious difference between the SERIA and SERIAlr can be observed in prosaccade trials in the PP20 condition ( left panel , upper half plane Fig 9 ) , in which late prosaccades are slower than antisaccades . We discuss this point in more detail below . One of the most salient results presented here is that models in which the parameters of the units were constrained to be equal across trial types had a larger LME than models in which all the parameters were free , suggesting that the early and inhibitory race units were not affected by the cue presented in a single trial . While visual inspection of the predicted likelihood under the posterior parameters showed that most of the prominent characteristics of the data were explained correctly , some more subtle effects were not captured accurately by the SERIA model . This is particularly clear in the PP20 condition , in which the SERIA model displays a large bias in prosaccades trials in the PP20 condition . One possible explanation is that restricting the parameters across trial types made the model too rigid to capture this effect . Fig 16 compares the fitted RT distributions for models m8 ( SERIA ) and m13 ( SERIAlr ) , in which no constraint on the parameters was imposed . Both models are qualitatively almost identical , although as shown in Fig 7 , the LME favored the SERIAlr model . Thereby , the qualitative similarity between both models indicates that , in our experiment , the RT of late decisions is only weakly dependent on time . In conclusion , although removing the constraint on the parameters did improve the fit , the differences are marginal and thus did not justify the additional model complexity . As mentioned above this is consistent with the notion that the information about trial type is only available to a subject once the peripheral stimulus ( green bar ) has been processed , presumably tens of milliseconds after the stimulus onset . In fact , this example illustrates the protection against overfitting provided by the LME , as this is a case in which simpler models were preferred over more complex models despite of slightly less accurate fits . Arguably , the constrained SERIA model fails to fully capture the RT of late prosaccade in the PP20 and PP50 conditions because of the assumption that late prosaccades have the same arrival time as late antisaccades . As shown in Fig 15 , although the response time of late pro- and antisaccades are strongly correlated , the average ratio of the response times changes across conditions . It is far from obvious why TT probability affects RT and ER in the antisaccade task . One possible explanation is that increased probability leads to higher preparedness for either pro- or antisaccades . Such a theory posits an intrinsic trade-off between preparations for one of the two action types that leads to higher RTs and ERs in low probability trials . Thus , a trade-off theory predicts that the arrival times of early and late responses should be negatively correlated . Although this hypothesis can explain our behavioral findings in terms of summary statistics , our model suggests a more complicated picture . The main explanation of our results is the effect of TT probability on the inhibitory unit and the probability of a late prosaccade . A higher probability of antisaccade trials leads to faster inhibition and to a higher number of late prosaccades . This resulted in higher mean RT in prosaccade trials when PP is low . In the case of antisaccades , although the mean arrival times of the late unit increased in the PP50 condition , the increased arrival time of the inhibitory unit on the PP80 condition skewed the antisaccade distribution towards higher RTs . Nevertheless , the SERIAlr implies the anticorrelation of late pro- and antisaccades in a single trial type , as these are the results of a GO-GO race . The biological implementation of action inhibition in the antisaccade and other countermanding tasks has received a lot of attention and is still debated [69–73] . Our work adds evidence to the theory that the antisaccade task requires a process that inhibits prepotent responses and is independent of the initiation of a late action [20] . Recent evidence from electrophysiological recordings in the rat brain ( [74] reviewed by [71] ) suggests that the hypothesized race between GO and inhibitory responses might be implemented by different pathways in the basal ganglia [68] . In addition to the basal ganglia , microstimulation of the supplementary eye fields tends to facilitate inhibition of saccades in the countermanding task [75] . Although not a primary goal of our model , we considered the question of predicting corrective antisaccades . This problem has received some attention recently [18 , 61 , 65 , 76] , as more sophisticated models of the antisaccade task have been developed . We speculated that corrective antisaccades are generated by the same mechanism as late responses . Thus , their RT distribution should follow a similar distribution . Our results strongly suggest that this is the case ( see Fig 11 ) . Moreover , the time delay of the corrective antisaccades indicates that , on average , these actions are not the result of the late unit being restarted at the end time of the erroneous prosaccade , as this would lead to much higher RTs . Rather , the planning of a corrective antisaccade might be started much before the end of the execution of an erroneous prosaccade , in accordance with the parallel planning model of the antisaccade task [46] and the ‘GO–STOP+GO’ model in [21] . Despite the large number of studies of clinical patients using the antisaccade task , an important question remains open: What are the causes of the errors in different neurological and psychiatric conditions ? For example [77 , 78] argued that errors in schizophrenia might be explained , at least partially , by a failure to generate a secondary late action based on several modifications of the antisaccade task . However , it was also proposed that the increased ER in schizophrenia is due to high tonic dopamine levels in the basal ganglia , that lead to decreased inhibition of early responses [68] . More generally , different neurological and psychiatric diseases , or even patients with the same condition , might be characterized by a different source of errors . For example , there is intriguing evidence [79] that patients with different diseases such as attention deficits disorders [80] , Parkinson’s disease [81] , and amyotrophic lateral sclerosis [82] might be characterized by different ratios of early and late errors . An interesting experimental finding in our study related to this is the considerable amount of erroneous antisaccades in prosaccade trials . An increased number of such errors could be caused by reduced cognitive flexibility leading to impaired shifting between tasks as observed for example in obsessive compulsive disorder [83] . The ability to quantify different types of errors through computational modeling might help to further characterize these diseases . Here we have presented a novel model of the antisaccade task . While the basic structure of the model follows the layout of a previous model [17] , we have introduced two crucial advancements . First , we postulated that late responses could trigger both pro- and antisaccades , which are selected by an independent decision process . Second , the generative nature of our model allows for Bayesian model inversion , which enables the comparison of different models and families of models on formal grounds . To our knowledge this has not been done for any of the previous models of the antisaccade task , which is of relevance for translational applications that aim at better understanding psychiatric diseases by means of computational modeling . The application of the model to a large data set yielded several novel results . First , the early and inhibitory race processes triggered by different cues are almost identical . Moreover , different PP had very different effects on the individual units , which was not obvious from the linear analysis of the mean RT and ER . Crucially , our modeling approach allowed us to look at a mechanistic explanation or the effects of PP by examining the individual units . In future work we aim to disentangle the mechanisms of behavioral differences caused by neuromodulatory drugs and psychiatric illnesses using formal Bayesian inference .
|
One widely replicated finding in schizophrenia research is that patients tend to make more errors than healthy controls in the antisaccade task , a psychometric paradigm in which participants are required to look in the opposite direction of a visual cue . This deficit has been suggested to be an endophenotype of schizophrenia , as first order relatives of patients tend to show similar but milder deficits . Currently , most models applied to experimental findings in this task are limited to fit average reaction times and error rates . Here , we propose a novel statistical model that fits experimental data from the antisaccade task , beyond summary statistics . The model is inspired by the hypothesis that antisaccades are the result of several competing decision processes that interact nonlinearly with each other . In applying this model to a relatively large experimental data set , we show that mean reaction times and error rates do not fully reflect the complexity of the processes that are likely to underlie experimental findings . In the future , our model could help to understand the nature of the deficits observed in schizophrenia by providing a statistical tool to study their biological underpinnings .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"ballistics",
"classical",
"mechanics",
"reaction",
"time",
"random",
"variables",
"neuroscience",
"cognitive",
"neuroscience",
"probability",
"distribution",
"mathematics",
"sensory",
"physiology",
"behavior",
"schizophrenia",
"mental",
"health",
"and",
"psychiatry",
"probability",
"theory",
"physics",
"visual",
"system",
"eye",
"movements",
"normal",
"distribution",
"physiology",
"biology",
"and",
"life",
"sciences",
"sensory",
"systems",
"physical",
"sciences",
"cognitive",
"science"
] |
2017
|
The Stochastic Early Reaction, Inhibition, and late Action (SERIA) model for antisaccades
|
ClinicalTrials . gov NCT03474198 , NCT01659437
Buruli ulcer ( BU ) , or Mycobacterium ulcerans disease , is a necrotizing skin disease driven by production of the immunosuppressive and cytotoxic macrolide-like toxin , mycolactone . Treatment for BU shifted from surgery and skin grafting to antibiotic therapy following pre-clinical and clinical evidence that combination , as opposed to single drug , therapy could be highly efficacious in killing M . ulcerans , stopping disease progression , reversing tissue damage and preventing relapse after treatment [1–3] . The original regimen of rifampin ( RIF , 10 mg/kg ) and streptomycin ( STR , 15 mg/kg ) given daily has both the benefit and the drawback of the inclusion of an injectable drug: patient and provider adherence is better assured but daily injections disrupt work , study , and recreation and burden the healthcare system . Although ototoxicity and other complications of STR treatment were not initially observed in West African patients based on self-report , more objective audiometric testing showed that hearing loss does indeed occur [4] . In Australia , which also has a large number of cases , all-oral antibiotic therapy combined with surgery has been in practice for many years [5] . In March of 2017 , the BU Technical Advisory Group for the World Health Organization Global BU Initiative , due to difficulties in obtaining STR and preliminary findings of non-inferiority in a clinical trial ( NCT01659437 ) in West Africa , recommended replacing STR with oral clarithromycin ( CLR , 15–30 mg/kg ) [6] . Compared to other chronic mycobacterial diseases such as leprosy and tuberculosis ( TB ) requiring antibiotic treatment for 6 to 24 months , depending on the form of the disease in each case , the treatment of BU with either regimen is relatively short at only 2 months . With the achievement of an all-oral regimen in hand , the next goal is to find ways to shorten treatment to one month or less . Previous studies in our laboratory established that clofazimine ( CFZ ) at a dose of 25 mg/kg , when used in combination with RIF , is as effective as either standard regimen ( i . e . , RIF+STR or RIF+CLR ) in reducing footpad swelling , production of mycolactone , and the M . ulcerans burden in a mouse footpad model of BU [7] . The regimen was comparable to RIF+STR and was significantly superior to RIF+CLR in preventing relapse after treatment for 6 weeks . Subsequently , research in our group with a mouse model of TB has shown that the dose of CFZ can be halved from 25 to 12 . 5 mg/kg with no loss of efficacy and a reduction in the transient skin discoloration associated with the drug [8 , 9] . The MIC for CFZ against both M . tuberculosis and M . ulcerans is approximately 0 . 25–0 . 5 μg/ml [7] . Recent studies in our group have shown that increasing the dose of RIF and the long half-life rifamycin , rifapentine ( RPT ) shortens the treatment duration needed to prevent relapse in mouse models of TB [10 , 11] . More recently , we found that replacing RIF with high-dose RIF or RPT resulted in greater bactericidal activity compared to RIF+CLR in a mouse footpad model of BU ( T . Omansen et al . , 2017 , P1621 , ECCMID , p . 191 , WHO Meeting on BU , p . 128 ) . Likewise , Chauffour et al . [12] showed that the regimen of RPT 10 mg/kg together with CLR achieved superior bactericidal effects and sterilizing efficacy at least comparable to that of RIF+STR in a mouse footpad model of BU . One complication in the treatment of BU in humans is a paradoxical worsening of the clinical appearance of lesions [13] . Given that CFZ has both antimicrobial and anti-inflammatory properties and treats erythema nodosum reactions in leprosy , it has the additional potential advantage over STR and , possibly , CLR to reduce the risk of such paradoxical worsening . Here , we evaluated the bactericidal and sterilizing efficacy of a rifamycin plus CFZ at the lower CFZ dose of 12 . 5 mg/kg and at escalating doses of RIF from 10 to 20 or 40 mg/kg and of RPT from 10 to 20 mg/kg . The results indicate that increasing the rifamycin dose can significantly shorten the treatment duration necessary to achieve culture negativity and prevent relapse when combined with CFZ , which may be a superior alternative to CLR or STR as a companion drug of the rifamycin .
M . ulcerans 1059 ( Mu1059 ) , originally obtained from a patient in Ghana , was generously provided by Dr . Pamela Small , University of Tennessee . Autoluminescent Mu1059 ( Mu1059AL ) was generated in our laboratory [14 , 15] . These strains both produce mycolactone A/B , and this toxin kills macrophages and fibroblasts in vitro [16 , 17] . The Mu1059AL strain was passaged in mouse footpads before use in these studies . The bacilli were harvested from footpads with grade 2 level swelling , i . e . , swelling with inflammation[18] . All animal procedures were conducted according to relevant national and international guidelines . The study was conducted adhering to the Johns Hopkins University guidelines for animal husbandry and was approved by the Johns Hopkins Animal Care and Use Committee , #MO17M13 . Johns Hopkins University is in compliance with the Animal Welfare Act regulations and Public Health Service Policy and also maintains accreditation of its program by the private Association for the Assessment and Accreditation of Laboratory Animal Care International . RIF , CFZ , and STR were purchased from Sigma ( St . Louis , MO ) . RPT and CLR were prepared from Priftin ( Sanofi ) and generic CLR ( Aurobindo Pharma , Hyderabad , India , Dayton , NJ , USA ) tablets , respectively , purchased at a pharmacy . Stock RIF and RPT ( with brief sonication ) suspensions were prepared every two weeks in distilled water; STR and CLR solutions were prepared weekly in water; and CFZ was suspended weekly in an 0 . 05% ( w/v ) agarose solution in distilled water . All drugs were given 5 days per week in 0 . 2 ml . RIF ( 10 , 20 , and 40 mg/kg ) , RPT ( 10 and 20 mg/kg ) , CFZ ( 25 mg/kg and 12 . 5 mg/kg ) , and CLR ( 100 mg/kg ) were administered by gavage . STR ( 150 mg/kg ) was administered by subcutaneous injection ( S1 Table ) . Doses for CLR and STR were chosen based on mean plasma exposures ( i . e . , area under the concentration-time curve over 24 hours post-dose ) compared to human doses . BALB/c mice ( N = 292 ) , age 4–6 weeks ( Charles River , Wilmington , MA ) , were inoculated in both hind footpads with approximately 4 . 42 log10 ( 2 . 65 x104 ) CFU of Mu1059AL in 0 . 03 ml PBS , resulting in a mean ( ±S . D . ) CFU count of 3 . 53±0 . 37 log10 M . ulcerans per footpad three days after infection . Treatment began 38 days after infection when footpad swelling increased to approximately grade 2 [18] , and there were 5 . 31±0 . 28 log10 CFU/footpad . Treatment with RIF+STR , RIF+CLR , RIF+CFZ , RPT+CFZ and RIF or RPT alone was administered for up to 6 weeks for the combination regimens and up to 4 weeks for the monotherapy regimens . Footpads were harvested before treatment initiation ( Day 0 ) and after 1 , 2 , and 4 weeks of treatment from mice ( 6 footpads from 3 mice ) for CFU and relative light unit RLU counts to assess luminescence and mycolactone detection . For relapse determinations , 10 mice ( 20 footpads ) were held without treatment for approximately 12 weeks after completing a 4- or 6-week combination regimen treatment ( See the overall experiment scheme , including each regimen evaluated , in S1 Table ) . Mice were euthanized if they reached grade 3 swelling on a scale of 0–4 , as described [18] . Footpad tissue was harvested , minced with fine scissors , suspended in 1 . 5 ml PBS , serially diluted , and plated on Middlebrook selective 7H11 plates ( Becton-Dickinson , Sparks , MD ) . Plates were incubated at 32°C and colonies were counted after 8–12 weeks of incubation . Autoluminescence was assessed using a Turner Designs ( TD 20/20 ) luminometer in both intact footpads and in suspensions of minced footpads in PBS . Values in the latter tended to be approximately 5 times higher than those obtained in intact footpads and only the suspension values are reported here . The values are reported as relative light units ( RLU ) . Samples of footpad tissue were stored in PBS at -20°C prior to mycolactone quantification . Mycolactone was extracted from 50 μl of tissue homogenate with 0 . 2 ml of acetonitrile containing 100 ng/ml of the internal standard , itraconazole . The standard curve and quality controls were prepared in blank mouse EDTA plasma . After centrifugation , the supernatant was transferred into an autosampler vial for LC-MS/MS analysis . Separation was achieved with a Thermo Betasil Phenyl ( 50 × 2 . 1 mm , 3 μm ) column at 40°C with a gradient . Mobile phase A was water containing 0 . 1% formic acid and mobile phase B was acetonitrile containing 0 . 1% formic acid . The gradient started with mobile phase B was held at 20% for 0 . 5 minutes and increased to 100% over 0 . 5 minutes; 100% mobile phase B was held for 2 minutes and then returned back to 20% mobile phase B and allowed to equilibrate for 2 minutes . Total run time was 5 minutes with a flow rate of 0 . 3 ml/min . The column effluent was monitored using a Sciex triple quadrupole 4500 mass spectrometry detector ( Sciex , Foster City , CA , USA ) using electrospray ionization operating in positive mode . The spectrometer was programmed to monitor the following Multiple Reaction Monitoring transitions: 765 . 4 → 429 . 3 for mycolactone and 705 . 3 → 392 . 0 for itraconazole . Calibration curves for mycolactone were computed using the area ratio peak of the analysis to the internal standard using a quadratic equation with a 1/x2 weighting function over the range of 0 . 5 to 100 ng/ml . DNA from each individual colony was extracted by boiling in 1X TE ( Tris-EDTA , pH 8 . 0 ) buffer for 5 minutes; 5 μl of the supernatant was then used for PCR . Specific primers , forward primer MU_rpoF 5’ CGACGACATCGACCACTTC 3’ and reverse primer MU_rpoR 5’ CGACAGTGAACCGATCAGAC 3’ , were used to amplify a 400 bp region encompassing the rifampin resistance-determining region . The PCR product was then sequenced to identify the presence of any mutation . Colonies from untreated control groups were used as a negative control . GraphPad Prism 6 was used to compare group means by student’s T test and analysis of variance and group proportions by Fisher’s exact test , and to determine Spearman’s correlation coefficients to evaluate RLU and CFU correlations .
Treatment was initiated five weeks after inoculation of M . ulcerans strain Mu1059AL , when the mean footpad swelling index was approximately 1 . 75 on a scale of 0–4 [21] . The mean CFU count on treatment initiation ( Day 0 ) was 5 . 31 ± 0 . 28 log10 , and the mean RLU count was 239 . 3 ± 84 ( Fig 1 ) .
Treatment for BU was revolutionized in the last 20 years after work in a mouse model like that employed here demonstrated that a combination of RIF10 plus either STR or amikacin could prevent the development of swelling and treat established lesions [18 , 20 , 21] . In humans , RIF+STR is efficacious but the inclusion of STR has the drawbacks of ototoxicity and a requirement for daily injections for 8 weeks [4] . In March , 2017 , a WHO technical advisory group recommended the adoption of an all-oral regimen of RIF+CLR on the basis of a series of clinical trials [6 , 22–24] . While better tolerated , the recommended regimen still requires 8 weeks of treatment . Therefore , shortening the duration of treatment required to safely eradicate M . ulcerans and its mycolactone toxin is now a major goal of research to improve BU treatment . Increasing the rifamycin exposure by using higher doses of RIF or RPT increase the sterilizing activity of combination therapy in mouse models of TB [10 , 11] . The treatment-shortening potential of anti-TB regimens containing daily RIF doses as high as 35 mg/kg [25] and RPT doses as high as 20 mg/kg [26] are now being evaluated in phase 2/3 trials ( NCT02581527 , NCT03474198 , NCT02410772 ) . We hypothesized that similar dose increases would shorten the duration of all-oral treatments for BU . Indeed , we found that , while monotherapy with the standard dose of RIF10 had a modest impact on swelling , mycolactone production , and bacterial burden , increasing the rifamycin dose had a significant impact on all three parameters , particularly on mycolactone levels and bacterial burden . Whereas RIF20 alone may have lagged behind the other high-dose rifamycins as monotherapy , they all had efficacy similar to that of the control regimens after 2–4 weeks of treatment . Combining high-dose RIF or RPT together with CFZ resulted in superior reduction of CFU burden and footpad swelling compared to rifamycin monotherapy , particularly after week 2 . More importantly , these combinations significantly reduced the proportion of mice relapsing after 4 weeks of treatment compared to both RIF10+STR and RIF10+CLR . Combining CFZ with RIF20 , RIF40 or RPT20 completely prevented relapse after 4 weeks of treatment , whereas RIF10+STR and RIF10+CLR treatment for 2 additional weeks was still associated with relapse in 15% and 65% of footpads , respectively . These results suggest that all-oral high-dose rifamycin and CFZ regimens have the potential to reduce the treatment duration from 8 weeks to 4 weeks without reducing efficacy . This would represent a substantial advance over the current standard of care . CFZ was recently repurposed for treatment of multidrug-resistant TB as a component of a short-course regimen now endorsed by WHO [27] . There may be additional significant advantages to replacing CLR with CFZ in all-oral regimens for BU . RIF significantly induces human metabolism of CLR , lowering average CLR concentrations by 73–87% and compromising its activity as a companion agent [28 , 29] . While CLR concentrations remain above the MIC when CLR is administered with standard doses of RIF to BU patients [30] , the inductive effect of high-dose rifamycins is expected to be even greater and more consistent from person-to-person . The inductive effect of high-dose rifamycins is expected to be even greater and more consistent from person-to-person . CFZ , on the other hand , has no known unfavorable interactions with RIF . At the 100 mg human dose providing plasma exposures similar to the 12 . 5 mg/kg dose in mice , CFZ has better gastrointestinal tolerability than CLR . Given the anti-inflammatory activity of CFZ , treatment with this drug may reduce the possibility of paradoxical reactions [5 , 13] . While the precise mechanisms underlying the induction of paradoxical reactions remain uncertain , accumulations of macrophages/giant cells have been observed . CFZ has been shown to accumulate in macrophages and to inhibit TNF production and boost anti-inflammatory IL-1RA production , including in dermal macrophages [31] . Giant cells form in response to both CD4-mediated and innate immune mechanisms [32] . Accordingly , we speculate that CFZ may modulate at least some of the inflammatory signals that may be involved in the induction of paradoxical reactions . Finally , CFZ is expected to become more readily available now due to its recommended use in the treatment of multi-drug resistant TB . Clinical trials are also underway ( NCT03474198 ) or in the planning stages for combining rifamycins with CFZ in treatment of drug-susceptible TB based on treatment-shortening effects in a mouse model [8 , 33 , 34] . The main side effect of CFZ is skin discoloration . However , it should be emphasized that the discoloration is dose- and duration-dependent , is less noticeable in pigmented skin , and resolves completely after treatment completion [35–38] . We believe it is unlikely that 4 weeks of treatment producing plasma exposures observed with the 12 . 5 mg/kg dose used here in mice would produce noticeable or disconcerting skin discoloration . Any skin discoloration is expected to be a minor , short-lasting effect , particularly in comparison to the gastrointestinal intolerance associated with CLR [5 , 39] and the toxicity and discomfort with STR [4] . CFZ may also be safer in BU patients with other co-morbidities . Caution in treating with CLR has been urged in patients with coronary heart disease in whom an increase in death has been observed after a two-week course of CLR . Deaths were apparent after patients were followed for one year or longer ( NCT00121550 ) [40] . O’Brien et al . [5] noted that severe antibiotic complications developed at a median time of 4 weeks in Australian patients treated with the currently used oral regimens for BU , either RIF+CLR or RIF+fluoroquinolone , and were associated with reduced renal function . Relapse of BU after antimicrobial treatment is thought to be rare , unlike in the preceding era when surgery without antibiotic treatment inevitably missed covert areas of infection . These observations suggest that there are new opportunities to shorten treatment durations with more potent drug combinations . To test the new regimens studied here , we used the stringent outcome measure of relapse prevention and found that high-dose rifamycins together with CFZ prevented relapse more effectively than RIF+STR and RIF+CLR despite shorter durations of treatment . Based on these promising results with drugs that are already in clinical use , these regimens warrant evaluation in clinical trials seeking to shorten the treatment of BU .
|
Buruli ulcer , a neglected tropical skin disease caused by Mycobacterium ulcerans , is treatable since 2004 with antibiotics instead of surgery . Treatment with either rifampin plus streptomycin or , more recently , rifampin plus clarithromycin requires taking the drugs daily for 8 weeks . Streptomycin is administered by injection and may result in hearing loss . Clarithromycin often causes gastrointestinal discomfort . Our goal is to identify a regimen that is both shorter and associated with fewer side effects . Rifampin , previously an expensive drug , is well tolerated not only at the standard dose of 10 mg/kg but at doses of 20 and 40 mg/kg . The related rifamycin , rifapentine , has a longer half-life and is also well tolerated . We tested in a mouse model of Buruli ulcer whether higher doses of these rifamycins together with clofazimine , a drug that has transient skin pigmentation side effects but no toxicities , could effectively reduce lesion size , the number of bacteria , and production of the mycolactone toxin , in a shorter time than that for the existing drug regimens . We found that treatment for 4 weeks with a high dose rifamycin plus clofazimine is as effective as 8 weeks of the current standard regimens of rifampin plus streptomycin or rifampin plus clarithromycin .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"antimicrobials",
"medicine",
"and",
"health",
"sciences",
"toxins",
"pathology",
"and",
"laboratory",
"medicine",
"drugs",
"tropical",
"diseases",
"microbiology",
"surgical",
"and",
"invasive",
"medical",
"procedures",
"toxicology",
"toxic",
"agents",
"animal",
"models",
"bacterial",
"diseases",
"streptomycin",
"model",
"organisms",
"pharmaceutics",
"antibiotics",
"experimental",
"organism",
"systems",
"neglected",
"tropical",
"diseases",
"pharmacology",
"bacteria",
"research",
"and",
"analysis",
"methods",
"infectious",
"diseases",
"buruli",
"ulcer",
"mouse",
"models",
"actinobacteria",
"mycobacterium",
"ulcerans",
"microbial",
"control",
"biology",
"and",
"life",
"sciences",
"drug",
"therapy",
"organisms"
] |
2018
|
Shorter-course treatment for Mycobacterium ulcerans disease with high-dose rifamycins and clofazimine in a mouse model of Buruli ulcer
|
Protein-protein interaction networks ( PINs ) are rich sources of information that enable the network properties of biological systems to be understood . A study of the topological and statistical properties of budding yeast and human PINs revealed that they are scale-rich and configured as highly optimized tolerance ( HOT ) networks that are similar to the router-level topology of the Internet . This is different from claims that such networks are scale-free and configured through simple preferential-attachment processes . Further analysis revealed that there are extensive interconnections among middle-degree nodes that form the backbone of the networks . Degree distributions of essential genes , synthetic lethal genes , synthetic sick genes , and human drug-target genes indicate that there are advantageous drug targets among nodes with middle- to low-degree nodes . Such network properties provide the rationale for combinatorial drugs that target less prominent nodes to increase synergetic efficacy and create fewer side effects .
There is a growing awareness that networks of protein interactions and gene regulations are the keys to understanding diseases and finding accurate drug targets [1] . With the increasing availability of genome-wide data including those on protein interactions and gene expressions , numbers of studies have been done on the structure and statistics of protein interactions and how diseased genes and drug targets are distributed over the network [2] , [3] . Understanding the topological and statistical properties of interaction networks and their relationships with lethal genes as well as currently identified drug targets should provide us with insights into robust and fragile properties of networks and possible drug targets for the future . We studied budding-yeast and human protein-protein interaction networks ( PINs ) to identify the architectural properties of network structures . PINs have often been argued to be “scale-free” [4] , [5] , which mostly means they have power-law frequency-degree distributions . However , this definition diverges from the original meaning of being scale-free in terms of the self-similarity of geometric properties of subject systems and there have been reports that claim such distributions are “more normal than normal”; thus , they are not considered to be particularly exotic by themselves [6] . In addition , there are different network topologies with different robustness and performance properties that maintain power-law distributions [7] . Therefore , it is very important to identify the architectural features of the network bearing the specific utilization of analysis results in mind . Our goal in this study was to identify the network topology of PINs and their relationship with lethal genes and possible drug targets so that the statistical likelihood of novel drug targets could be inferred . A particularly interesting issue in the field of systems engineering , physics , and systems biology is the trade-off between the properties of robustness , fragility , and efficiency . Highly optimized tolerance ( HOT ) theory is a conceptual framework that can be used to explain this issue . Although a system conforming to HOT theory is optimized for specific perturbations and has highly efficient properties , such a system is extremely fragile against unexpected perturbations [8] , [9] . Doyle et al . [8] demonstrated that the Abline Internet2 router-level topology network conformed to HOT theory . Nodes in the Abline network with extremely high-degree nodes connect to a large number of low-degree nodes , while links between these high-degree nodes are suppressed and thus they do not form a core backbone for the whole network . A network having similar structures to the Abline network is defined as a HOTnet [8] . It would be very interesting to clarify whether PINs are HOTnets or not . The two questions addressed in this paper are: ( 1 ) what is the global architecture of PINs ? Do they follow the possible architectural features of scale-free networks created by preferential attachments or conform to HOT theory , and ( 2 ) are there specific statistical features for proteins that are likely to be drug targets ? To answer these questions , budding yeast and human PINs were used to analyze their structural properties using a series of analysis methods .
Scale-free Network vs . Highly Optimized Tolerance Network: A series of analyses was carried out using budding yeast and human PIN data to identify the topological features of PINs . In this study , we defined low-degree nodes as nodes with degrees of less than 5 because Han et al . [10] and Partil and Nakamura [11] defined hubs as nodes with degrees of more than 6 . We then developed a method called moving stratification by degrees ( MSD ) to extract sub-networks consisting of hubs with specific degree distributions where indices such as average cluster coefficients would be computed ( see Materials and Methods for details ) . The analyses revealed that the average cluster coefficient was very high for sub-networks consisting of hubs with degrees from 6 to 38 , while it was very low for hubs with degrees of more than 39 in the yeast PIN ( see Figure S1 and Table S1 ) . Notably , for hubs with degrees of less than 38 , the difference in cluster coefficients was generally significant between the yeast PIN and random network , while there were no significant differences in cluster coefficients for hubs with degrees of more than 39 ( see Figure S1 ) . Therefore , we defined middle-degree nodes as those with degrees from 6 to 38 and those with degrees of more than 39 as high . In the same manner , we defined middle- ( from 6 to 30 ) and high-degree ( more than 31 ) nodes in the human PIN ( see Figure S2 and Table S2 ) . Note that , when we used more stringent thresholds for middle- ( from 10 to 50 ) and high-degree ( more than 51 ) nodes , the results did not change essentially , i . e . , the average cluster coefficient for middle-degree nodes was much higher than that for high-degree nodes ( see Tables S3 and S4 ) . The analyses revealed three findings: ( 1 ) the network structure for middle-degree nodes ( from 6 to 38 for yeast and from 6 to 30 for human PINs ) , and high-degree nodes ( more than 39 for yeast and more than 31 for human PINs ) has different structures , ( 2 ) middle-degree nodes are tightly connected and form a structure often called a “stratus” , and ( 3 ) high-degree nodes do not connect , but connect with low-degree nodes , and form an “altocumulus” structure ( Figures 1 and 2 ) . Notably , we used more stringent thresholds for middle- ( degrees from 10 to 50 ) and high-degree nodes ( degrees more than 51 ) , and found that changing the thresholds did not essentially affect the results ( see Figure S3 and S4 ) . These results suggests that PINs have an architecture where highly interconnected middle-degree nodes form a core backbone for the whole network and large numbers of low-degree nodes connect to high-degree nodes ( see Figure 2 ) . This architecture is a type of network that is suggested as a HOTnet , i . e . , a network with HOT properties , also seen in the Internet router-level topology [8] . To further confirm this observation , we calculated a graph-theoretic quantity , s ( g ) , that defines the likelihood high-degree nodes will be connected to one another ( see Materials and Methods for details ) . S ( g ) , a value normalized against smax , indicates that networks with tightly interconnected high-degree nodes tend to be closer to 1 . 0 , whereas networks with only sparsely interconnected high-degree nodes tend to be closer to 0 . 0 ( see Materials and Methods for details ) . Doyle et al . reported randomly generated preferential-attachment-type scale-free networks had relatively high values such as 0 . 61 , whereas a HOTnet exemplified by a network abstracted from an actual Abilene Internet2 router topology network had a value as low as 0 . 34 [8] . We found that the value of S ( g ) for the yeast PIN was 0 . 25 and that of the human PIN was 0 . 38 . Thus , we could conclude that PINs are HOTnets . PINs are networks with a modular structure [12]–[14] . Here , modularity is defined as characteristics where there are fewer links between nodes with similar degrees . This only means there are limited links between high-degree nodes ( hubs ) , whereas there are links between hubs and low-degree nodes . This is a feature that was also confirmed in this study ( see Figure 2 ) . Modularity in PINs implies that networks have two features [13]: First , functional units may be composed of many low-degree nodes that are directly connected to a hub node . Second , confusion between modules is avoided by avoiding direct connection between hubs . While there are arguments against this claim that hubs are tightly connected because they need to influence one another to achieve an integrated function for the whole system [15] , analysis results indicate that such integration is most likely to take place via middle-degree nodes instead of high-degree nodes ( see Figure 2 ) . The distribution of essential genes , synthetic genes , and other genes are shown in Figure 3 . It is interesting to note that both essential genes and synthetic lethal genes have similar distributions . The average degree of essential proteins is 4 . 95 and that of synthetic lethal proteins is 4 . 40 . However , the Wilcoxon rank sum test demonstrated that there is no statistical significance between them ( P = 0 . 334 ) . In either case , essential and synthetic lethal proteins are concentrated on middle-degree nodes and high-degree nodes . However , the average degree among synthetic sick genes is 4 . 07 and this is significantly lower than that among synthetic lethal genes ( P = 0 . 0015 ) . This means genes that have less severe impact are distributed toward regions with a lower-degree distribution . Scale-richness: The power law distribution often characterized for scale-free networks only means that local frequency-degree distributions are independent of location along the degree axis , rather than self-similarity of network structures . However , Tanaka demonstrated that bacterial metabolic networks are scale rich in the sense there are different categories of metabolites and enzymes depending on the degree of nodes [16] . A group of nodes with high degree tends to be composed of currency molecules such as ATP and a group of nodes with low degree mostly consists of enzymes involved in specific cellular functions . In this study , we investigated if the frequency-degree distribution of proteins for each functional category exhibited the scale-rich characteristics reported by Tanaka . Figures 4 and S5 correspond to frequency-degree plots for proteins in different functional categories in the yeast PIN and the human PIN . The functional categories were assigned based on the GO slim ontology . As shown in the figures , the degree distribution patterns differ among functional categories . Moreover , proteins with different GO slim annotations have different average degrees ( See Tables S5 and S6 ) . Note that many functional categories have significantly higher ( or lower ) average degrees than the whole PINs ( See Tables S5 and S6 ) . These results suggest that the yeast and human PINs are scale-rich . Drug Targets: Drug-target molecules are distributed over low- to middle-level degree nodes with higher probability on middle-degree nodes . Consistent with reports already published , the average degree among drug-target nodes ( 4 . 74 ) is higher than the average degree among all nodes ( 4 . 06 ) . The distribution of known drug targets is shown in Figure 5 and this is predominantly distributed to middle-degree nodes and mostly on backbone of the network . There are almost no drug targets for high-degree nodes . The distribution of drug targets for cancer and non-cancerous diseases are in sharp contrast . While the average degree of target nodes for cancer drugs was 7 . 82 , the targets for non-cancerous diseases scored only 4 . 24 ( P = 0 . 01 ) . Moreover , we found that the proportion of drug targets among low-degree proteins were similar to random expectation . Figure 6 shows distribution of drug targets marked on degree-rank plot . The drug target molecule that has highest degree is Src with 41 which is the target for drugs such as Dasatinib . Target molecules for anti-cancer drugs are shifted toward high degree nodes compare against average and non-anti-cancer drugs .
A series of analyses revealed that both the budding yeast and human PINs are scale-rich and have HOT networks . There are extensive interconnections among middle-degree nodes that form the backbone of the network ( see Figure 2 ) . Most drug-target genes concentrate on middle-degree nodes and parts of low-degree nodes , but not on high-degree nodes . Interestingly , Feldman et al . ( 2008 ) [17] reported that genes harboring inherited disease mutations also concentrated on middle-degree nodes . Because of the potential lethality observed in budding yeast ( Figure 3A ) and reported high lethality in mouse knockout [2] , high-degree nodes are unlikely to be preferred drug targets or genes with disease mutations . Since oncogenes tend to be high-degree nodes , they are less likely to be drug targets , or one has to accept major potential side effects . The fact that the degree distribution of cancer-drug targets is higher than that of non-cancer-drug targets is consistent with the report by Yao and Rzhetsky [18] . Since high-degree nodes are predominantly connected with low-degree nodes ( Figures 1 , 2 , S3 , and S4 ) , the elimination of high-degree nodes is likely to affect large numbers of low-degree nodes . This may result in unacceptable side effects since a group of genes that bear certain functions may be made collectively dysfunctional . Detailed case studies are warranted to test and verify this possible interpretation . However , the average degree distribution of synthetic sick genes ( 4 . 07 ) is less than that of essential genes ( 4 . 95 ) and synthetic lethal genes ( 4 . 40 ) . This implies that a drug design strategy to generate synergetic effects by targeting less important targets can be a reasonable option because each compound in such drugs can select targets that have less impact on the overall system alone . We found that middle-level degree nodes are the optimal targets for therapeutic drugs . A similar observation was reported by Yao and Rzhetsky [18] , although they measured the mean degree among drug targets . In this study , we investigated the degree distribution of drug targets in greater detail , because we measured a fraction of drug targets to all nodes with degree k as well as mapping drug targets on the network structure . It was clearly identified most of drug targets for drugs that are currently on the market are concentrated on middle degree nodes that are back bone of the network and low-degree nodes that tends to have specific function specific effects . One of novel findings here is that the distribution of drug targets for low-degree nodes is similar to random expectation , indicating that there are a certain number of low-degree drug targets . From these results , we can expect that the most advantageous targets for combinatorial drugs could be among low-degree nodes because these could have less severe impact on the overall system of the human body . This is consistent with the idea of “long-tail drugs”[19] . Are there any relationships between structures in molecular networks ( i . e . , scale-richness in PINs ) and the properties of their underlying genome ? Rzhetsky and Gomez [20] proposed a stochastic model describing the evolutionary growth of molecular networks . Their model predicts that , in a molecular network , the shape of the degree distribution will be similar to the shape of the distribution of domains in the genome . Actually , they showed that , in the case of the entire yeast PIN , both the degree distribution and the distribution of the domain followed a power law . Therefore , it might be interesting to see whether , for each functional category , the shape of the degree distribution was similar to that of the domain distribution , when the entire architecture of domains in genomes becomes available . In this study , we assumed that the PINs represented all functions of genes . However , the PINs are just composed of binary protein-protein binding and proteins have other types of functions , such as catalyzing reactions with non-protein substrates . Therefore , PINs reflect a subset of the entire cellular function . This indicates that , if the complete picture for cellular protein functions could be considered , our conclusions from the PINs may diverge from what we presented here . Moreover , at present , the yeast and human PINs represent incomplete pictures of the actual entire PINs of these organisms . When data on all the actual entire PINs become available , we intend to examine all the actual entire PINs to see whether similar observations to those in this study can be made or not . It is interesting to note that both PINs and the Internet topology are HOTnets . Many of the observed properties in Internet router topology may be applied to PINs as well . Such properties include robustness against node failure and optimized performance [21] . It has been reported that analysis using several possible router topologies found that a HOTnet configuration was most efficient , providing more maximum overall bandwidth to users than that with other network-configuration approaches such as random and preferential attachment [21] . The implication is that biological PINs have evolved to become efficient and error tolerant . The series of analyses presented in this report indicate that there are changes whereby we can rationally design drugs by taking into account network properties , and additional insights from engineering and physics may further extend our opportunities for exploring network-based biology .
|
Genome-wide data on interactions between proteins are now available , and networks of protein interactions are the keys to understanding diseases and finding accurate drug targets . This study revealed that the architectural properties of the backbones of protein interaction networks ( PINs ) were similar to those of the Internet router-level topology by using statistical analyses of genome-wide budding yeast and human PINs . This type of network is known as a highly optimized tolerance ( HOT ) network that is robust against failures in its components and that ensures high levels of communication . Moreover , we also found that a large number of the most successful drug-target proteins are on the backbone of the human PIN . We made a list of proteins on the backbone of the human PIN , which may help drug companies to search more efficiently for new drug targets .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion"
] |
[
"genetics",
"and",
"genomics/bioinformatics",
"computational",
"biology"
] |
2009
|
Structure of Protein Interaction Networks and Their Implications on Drug Design
|
Rotors are functional reentry sources identified in clinically relevant cardiac arrhythmias , such as ventricular and atrial fibrillation . Ablation targeting rotor sites has resulted in arrhythmia termination . Recent clinical , experimental and modelling studies demonstrate that rotors are often anchored around fibrotic scars or regions with increased fibrosis . However , the mechanisms leading to abundance of rotors at these locations are not clear . The current study explores the hypothesis whether fibrotic scars just serve as anchoring sites for the rotors or whether there are other active processes which drive the rotors to these fibrotic regions . Rotors were induced at different distances from fibrotic scars of various sizes and degree of fibrosis . Simulations were performed in a 2D model of human ventricular tissue and in a patient-specific model of the left ventricle of a patient with remote myocardial infarction . In both the 2D and the patient-specific model we found that without fibrotic scars , the rotors were stable at the site of their initiation . However , in the presence of a scar , rotors were eventually dynamically anchored from large distances by the fibrotic scar via a process of dynamical reorganization of the excitation pattern . This process coalesces with a change from polymorphic to monomorphic ventricular tachycardia .
Many clinically relevant cardiac arrhythmias are conjectured to be organized by rotors . A rotor is an extension of the concept of a reentrant source of excitation into two or three dimensions with an area of functional block in its center , referred to as the core . Rapid and complex reentry arrhythmias such as atrial fibrillation ( AF ) and ventricular fibrillation ( VF ) are thought to be driven by single or multiple rotors . A clinical study by Narayan et al . [1] indicated that localized rotors were present in 68% of cases of sustained AF . Rotors ( phase singularities ) were also found in VF induced by burst pacing in patients undergoing cardiac surgery [2 , 3] and in VF induced in patients undergoing ablation procedures for ventricular arrhythmias [4] . Intramural rotors were also reported in early phase of VF in the human Langendorff perfused hearts [5 , 6] . It was also demonstrated that in most cases rotors originate and stabilize in specific locations [4–8] . A main mechanism of rotor stabilization at a particular site in cardiac tissue was proposed in the seminal paper from the group of Jalife [9] . It was observed that rotors can anchor and exhibit a stable rotation around small arteries or bands of connective tissue . Later , it was experimentally demonstrated that rotors in atrial fibrillation in a sheep heart can anchor in regions of large spatial gradients in wall thickness [10] . A recent study of AF in the right atrium of the explanted human heart [11] revealed that rotors were anchored by 3D micro-anatomic tracks formed by atrial pectinate muscles and characterized by increased interstitial fibrosis . The relation of fibrosis and anchoring in atrial fibrillation was also demonstrated in several other experimental and numerical studies [8 , 11–14] . Initiation and anchoring of rotors in regions with increased intramural fibrosis and fibrotic scars was also observed in ventricles [5 , 7 , 15] . One of the reasons for rotors to be present at the fibrotic scar locations is that the rotors can be initiated at the scars ( see e . g . [7 , 15] ) and therefore they can easily anchor at the surrounding scar tissue . However , rotors can also be generated due to different mechanisms , such as triggered activity [16] , heterogeneity in the refractory period [16 , 17] , local neurotransmitter release [18 , 19] etc . What will be the effect of the presence of the scar on rotors in that situation , do fibrotic areas ( scars ) actively affect rotor dynamics even if they are initially located at some distance from them ? In view of the multiple observations on correlation of anchoring sites of the rotors with fibrotic tissue this question translates to the following: is this anchoring just a passive probabilistic process , or do fibrotic areas ( scars ) actively affect the rotor dynamics leading to this anchoring ? Answering these questions in experimental and clinical research is challenging as it requires systematic reproducible studies of rotors in a controlled environment with various types of anchoring sites . Therefore alternative methods , such as realistic computer modeling of the anchoring phenomenon , which has been extremely helpful in prior studies , are of great interest . The aim of this study is therefore to investigate the processes leading to anchoring of rotors to fibrotic areas . Our hypothesis is that a fibrotic scar actively affects the rotor dynamics leading to its anchoring . To show that , we first performed a generic in-silico study on rotor dynamics in conditions where the rotor was initiated at different distances from fibrotic scars with different properties . We found that in most cases , scars actively affect the rotor dynamics via a dynamical reorganization of the excitation pattern leading to the anchoring of rotors . This turned out to be a robust process working for rotors located even at distances more than 10 cm from the scar region . We then confirmed this phenomenon in a patient-specific model of the left ventricle from a patient with remote myocardial infarction ( MI ) and compared the properties of this process with clinical ECG recordings obtained during induction of a ventricular arrhythmia .
Our anatomical model is based on an individual heart of a post-MI patient reconstructed from late gadolinium enhanced ( LGE ) magnetic resonance imaging ( MRI ) was described in detail previously [20] . Briefly , a 1 . 5T Gyroscan ACS-NT/Intera MR system ( Philips Medical Systems , Best , the Netherlands ) system was used with standardized cardiac MR imaging protocol . The contrast –gadolinium ( Magnevist , Schering , Berlin , Germany ) ( 0 . 15 mmol/kg ) – was injected 15 min before acquisition of the LGE sequences . Images were acquired with 24 levels in short-axis view after 600–700 ms of the R-wave on the ECG within 1 or 2 breath holds . The in-plane image resolution is 1 mm and through-plane image resolution is 5 mm . Segmentation of the contours for the endocardium and the epicardium was performed semi-automatically on the short-axis views using the MASS software ( Research version 2014 , Leiden University Medical Centre , Leiden , the Netherlands ) . The myocardial scar was identified based on signal intensity ( SI ) values using a validated algorithm as described by Roes et al . [21] . In accordance with the algorithm , the core necrotic scar is defined as a region with SI >41% of the maximal SI . Regions with lower SI values were considered as border zone areas . In these regions , we assigned the fibrosis percentage as normalized values of the SI as in Vigmond et al . [22] . In the current paper , fibrosis was introduced by generating a random number between 0 and 1 for each grid point and if the random number was less than the normalized SI at the corresponding pixel the grid point was considered as fibroblast . Currently there is no consensus on how the SI values should be used for clinical assessment of myocardial fibrosis and various methods have been reported to produce significantly different results [23] . However , the method from Vigmond et al . properly describes the location of the necrotic scar region in our model as for the fibrosis percentage of more than 41% we observe a complete block of propagation inside the scar . This means that all tissue which has a fibrotic level higher than 41% behaves like necrotic scar . The approach and the 2D model was described in detail in previous work [24–26] . Briefly , for ventricular cardiomyocyte we used the ten Tusscher and Panfilov ( TP06 ) model [27 , 28] , and the cardiac tissue was modeled as a rectangular grid of 1024 × 512 nodes . Each node represented a cell that occupied an area of 250 × 250 μm2 . The equations for the transmembrane voltage are given by C m d V i k d t = ∑ α , β ∈ { - 1 , + 1 } η i k α β g gap ( V i + α , k + β - V i k ) - I ion ( V i k , … ) , ( 1 ) where Vik is the transmembrane voltage at the ( i , k ) computational node , Cm is membrane capacitance , ggap is the conductance of the gap junctions connecting two neighboring myocytes , Iion is the sum of all ionic currents and η i k α β is the connectivity tensor whose elements are either one or zero depending on whether neighboring cells are coupled or not . Conductance of the gap junctions ggap was taken to be 103 . 6 nS , which results in a maximum velocity planar wave propagation in the absence of fibrotic tissue of 72 cm/s at a stimulation frequency of 1 Hz . ggap was not modified in the fibrotic areas . A similar system of differential equations was used for the 3D computations where instead of the 2D connectivity tensor η i k α β we used a 3D weights tensor w i j k α β γ whose elements were in between 0 and 1 , depending both on coupling of the neighbor cells and anisotropy due to fiber orientation . Each node in the 3D model represented a cell of the size of 250 × 250 × 250 μm3 . 20s of simulation in 3D took about 3 hours . Fibrosis was modeled by the introduction of electrically uncoupled unexcitable nodes [29] . The local percentage of fibrosis determined the probability for a node of the computational grid to become an unexcitable obstacle , meaning that for high percentages of fibrosis , there is a high chance for a node to be unexcitable . As previous research has demonstrated that LGE-MRI enhancement correlates with regions of fibrosis identified by histological examination [30] , we linearly interpolated the SI into the percentage of fibrosis for the 3D human models . In addition , the effect of ionic remodeling in fibrotic regions was taken into account for several results of the paper [31 , 32] . To describe ionic remodeling we decreased the conductance of INa , IKr , and IKs and depending on local fibrosis level as: G Na = ( 1 - 1 . 55 f 100 % ) G Na 0 , ( 2 ) G Kr = ( 1 - 1 . 75 f 100 % ) G Kr 0 , ( 3 ) G Ks = ( 1 - 2 f 100 % ) G Ks 0 , ( 4 ) where GX is the peak conductance of IX ionic current , G X 0 is the peak conductance of the current in the absence of remodeling , and f is the local fibrosis level in percent . These formulas yield a reduction of 62% for INa , of 70% for IKr , and of 80% for IKs if the local fibrosis f is 40% . These values of reduction are , therefore , in agreement with the values published in [33 , 34] . The normal conduction velocity at CL 1000 ms is 72 cm/s ( CL 1000 ms ) . However , as the compact scar is surrounded by fibrotic tissue , the velocity of propagation in that region gradually decreases with the increase in the fibrosis percentage . For example for fibrosis of 30% , the velocity decreases to 48 cm/s ( CL 1000 ms ) . We refer to Figure 1 in Ten Tusscher et al [25] for the planar conduction velocity as a function of the percentage fibrosis in 2D tissue and 3D tissue . The geometry and extent of fibrosis in the human left ventricles were determined using the LGE MRI data . The normalized signal intensity was used to determine the density of local fibrosis . The fiber orientation is presented in detail in the supplementary S1 Appendix . The model for cardiac tissue was solved by the forward Euler integration scheme with a time step of 0 . 02 ms . The numerical solver was implemented using the CUDA toolkit for performing the computations on graphics processing units . Simulations were performed on a GeForce GTX Titan Black graphics card using single precision calculations . The eikonal equations for anisotropy generation were solved by the fast marching Sethian’s method [35] . The eikonal solver and the 3D model generation pipeline were implemented in the OCaml programming language . Rotors were initiated by an S1S2 protocol , as shown in the supplementary S1 Fig . Similarly , in the whole heart simulations , spiral waves ( or scroll waves ) were created by an S1S2 protocol . For the compact scar geometry used in our simulations the rotation of the spiral wave was stationary , the period of rotation of the anchored rotor was always more than 280 ms , while the period of the spiral wave was close to 220 msec . Therefore , we determined anchoring as follows: if the period of the excitation pattern was larger than 280 ms over a measuring time interval of 320 ms we classified the excitation as anchored . When the type of anchoring pattern was important ( single or multi-armed spiral wave ) we determined it visually . If in all points of the tissue , the voltage was below -20 mV , the pattern was classified as terminated . We applied the classification algorithm at t = 40 s in the simulation . In the whole heart , the pseudo ECGs were calculated by assuming an infinite volume conductor and calculating the dipole source density of the membrane potential Vm in all voxel points of the ventricular myocardium , using the following equation [36] E C G ( t ) = ∫ ( r → , D ( r → ) ∇ → V ( t ) ) | r → | 3 d 3 r ( 5 ) whereby D is the diffusion tensor , V is the voltage , and r → is the vector from each point of the tissue to the recording electrode . The recording electrode was placed 10 cm from the center of the ventricles in the transverse plane . Twelve-lead ECGs of all induced ventricular tachycardia ( VT ) of patients with prior myocardial infarction who underwent radiofrequency catheter ablation ( RFCA ) for monomorphic VT at LUMC were reviewed . All patients provided informed consent and were treated according to the clinical protocol . Programmed electrical stimulation ( PES ) is routinely performed before RFCA to determine inducibility of the clinical/presumed clinical VT . All the patients underwent PES and ablation according to the standard clinical protocol , therefore no ethical approval was required . Ablation typically targets the substrate for scar-related reentry VT . After ablation PES is repeated to test for re-inducibility and evaluate morphology and cycle length of remaining VTs . The significance of non-clinical , fast VTs is unclear and these VTs are often not targeted by RFCA . PES consisted of three drive cycle lengths ( 600 , 500 and 400 ms ) , one to three ventricular extrastimuli ( ≥200 ms ) and burst pacing ( CL ≥200 ms ) from at least two right ventricular ( RV ) sites and one LV site . A positive endpoint for stimulation is the induction of any sustained monomorphic VT lasting 30 s or requiring termination . ECG and intracardiac electrograms ( EG ) during PES were displayed and recorded simultaneously on a 48-channel acquisition system ( Prucka CardioLab EP system , GE Healthcare , USA ) for off-line analysis .
Fibrotic scars can not only anchor the rotors but can dynamically anchor them from a large distance . In the first experiments we studied spiral wave dynamics with and without a fibrotic scar in a generic study . The diameter of the fibrotic region was 6 . 4 cm , based on the similar size of the scars from patients with documented and induced VT ( see the Methods section , Magnetic Resonance Imaging ) . The percentage of fibrosis changed linearly from 50% at the center of the scar to 0% at the scar boundary . We initiated a rotor at a distance of 15 . 5 cm from the scar ( Fig 1 , panel A ) which had a period of 222 ms and studied its dynamics . First , after several seconds the activation pattern became less regular and a few secondary wave breaks appeared at the fibrotic region ( Fig 1 , panel B ) . These irregularities started to propagate towards the tip of the initial rotor ( Fig 1 , panel C-D ) creating a complex activation picture in between the scar and the initial rotor . Next , one of the secondary sources reached the tip of the original rotor ( Fig 1 , panel E ) . Then , this secondary source merged with the initial rotor ( Fig 1 , panel F ) , which resulted in a deceleration of the activation pattern and promoted a chain reaction of annihilation of all the secondary wavebreaks in the vicinity of the original rotor . At this moment , a secondary source located more closely to the scar dominated the simulation ( Fig 1 , panel G ) . The whole process now started again ( Fig 1 , panels H-K ) , until finally only one source became the primary source anchored to the scar ( Fig 1L ) with a rotation period of 307 ms . For clarity , a movie of this process is provided as supplementary S1 Movie . Note that this process occurs only if a scar with surrounding fibrotic zone was present . In the simulation entitled as ‘No scar’ in Fig 1 , we show a control experiment when the same initial conditions were used in tissue without a scar . In the panel entitled as ‘Necrotic scar’ in Fig 1 , a simulation with only a compact region without the surrounding fibrotic tissue is shown . In both cases the rotor was stable and located at its initial position during the whole period of simulation . The important difference here from the processes shown in Fig 1 ( Fibrotic scar ) is that in cases of ‘No scar’ and ‘Necrotic scar’ no new wavebreaks occur and thus we do not have a complex dynamical process of re-arrangement of the excitation patterns . We refer to this complex dynamical process leading to anchoring of a distant rotor as dynamical anchoring . Although this process contains a phase of complex behaviour , overall it is extremely robust and reproducible in a very wide range of conditions . In the second series of simulations , the initial rotor was placed at different distances from the scar border , ranging from 1 . 8 to 14 . 3 cm , to define the possible outcomes , see Fig 2 . Here , in addition to a single anchored rotor shown in Fig 1H we could also obtain other final outcomes of dynamical anchoring: we obtained rotors rotating in the opposite direction ( Fig 2A , top ) , double armed anchored rotors which had 2 wavefronts rotating around the fibrotic regions ( Fig 2A , middle ) or annihilation of the rotors ( Fig 2A , bottom , which show shows no wave around the scar ) , which normally occurred as a result of annihilation of a figure-eight-reentrant pattern . To summarize , we therefore had the following possible outcomes: Termination of activity A rotor rotating either clockwise or counter-clockwise A two- or three-armed rotor rotating either clockwise or counter-clockwise Fig 2 , panel B presents the relative chance of the mentioned activation patterns to occur depending on the distance between the rotor and the border of the scar . We see , indeed , that for smaller initial distances the resulting activation pattern is always a single rotor rotating in the same direction . With increasing distance , other anchoring patterns are possible . If the distance was larger than about 9 cm , there is at least a 50% chance to obtain either a multi-armed rotor or termination of activity . Also note that such dynamical anchoring occurred from huge distances: we studied rotors located up to 14 cm from the scar . However , we observed that even for very large distances such as 25 cm or more such dynamical anchoring ( or termination of the activation pattern ) was always possible , provided enough time was given . We measured the time required for the anchoring of rotors as a function of the distance from the scar . For each distance , we performed about 60 computations using different seed values of the random number generator , both with and without taking ionic remodeling into account . The results of these simulations are shown in Fig 3 . We see that the time needed for dynamical anchoring depends linearly on the distance between the border of the scar and the initial rotor . The blue and yellow lines correspond to the scar model with and without ionic remodeling , respectively ( ionic remodeling was modelled by decreasing the conductance of INa , IKr , and IKs as explained in the Methods Section ) . We interpret these results as follows; The anchoring time is mainly determined by the propagation of the chaotic regime towards the core of the original rotor and this process has a clear linear dependency . For distant rotors , propagation of this chaotic regime mainly occurs outside the region of ionic remodelling , and thus both curves in Fig 3 have the same slope . However , in the presence of ionic remodelling , the APD in the scar region is prolonged . This creates a heterogeneity and as a consequence the initial breaks in the scar region are formed about 3 . 5 s earlier in the scar model with remodeling compared with the scar model without remodeling . To identify some properties of the substrate necessary for the dynamical anchoring we varied the size and the level of fibrosis within the scar and studied if the dynamical anchoring was present . Due to the stochastic nature of the fibrosis layout we performed about 300 computations with different textures of the fibrosis for each given combination of the scar size and the fibrosis level . The results of this experiment are shown in Fig 4 . Dynamical anchoring does not occur when the scar diameter was below 2 . 6 cm , see Fig 4 . For scars of such small size we observed the absence of both the breakup and dynamical anchoring . We explain this by the fact that if the initial separation of wavebreaks formed at the scar is small , the two secondary sources merge immediately , repairing the wavefront shape and preventing formation of secondary sources [37] . Also , we see that this effect requires an intermediate level of fibrosis density . For small fibrosis levels no secondary breaks are formed ( close to the boundary of the fibrotic tissue ) . Also , no breaks could be formed if the fibrosis level is larger than 41% in our 2D model ( i . e . closer to the core ) , as the tissue behaves like an inexcitable scar . For a fibrosis > 41% the scar effectively becomes a large obstacle that is incapable of breaking the waves of the original rotor [37] . Close to the threshold of 41% we have also observed another interesting pattern when the breaks are formed inside the core of the scar ( inside the > 41% region ) only and cannot exit to the surrounding tissue , see the supplementary S1 Movie . Finally , note that Fig 4 illustrates only a few factors important for the dynamical anchoring in a simple setup in an isotropic model of cardiac tissue . The particular values of the fibrosis level and the size of the scar can also depend on anisotropy , the texture of the fibrosis and its possible heterogeneous distribution . To verify that the dynamical anchoring takes place in a more realistic geometry , we developed and investigated this effect in a patient-specific model of the human left ventricle , see the Method section for details . The scar in this dataset has a complex geometry with several compact regions with size around 5-7 cm in which the percentage of fibrosis changes gradually from 0% to 41% at the core of the scar based on the imaging data , see Methods section . The remodeling of ionic channels at the whole scar region was also included to the model ( including borderzone as described the Fibrosis Model in the method section ) . We studied the phenomenon of dynamical anchoring for 16 different locations of cores of the rotor randomly distributed in a slice of the heart at about 4 cm from the apex ( see Fig 5 ) . Cardiac anisotropy was generated by a rule-based approach described in details in the Methods section ( Model of the Human Left Ventricle ) . Of the 16 initial locations , shown in Fig 5 , there was dynamical anchoring to the fibrotic tissue in all cases , with and without ionic remodeling . After the anchoring , in 4 cases the rotor annihilated . The effect of the attraction was augmented by the electrophysiolical remodelling , similar as in 2D . A representative example of our 3D simulations is shown in Fig 5 . We followed the same protocol as for the 2D simulations . The top 2 rows the modified anterior view and the modified posterior view in the case the scar was present . In column A , we see the original location of the spiral core ( 5 cm from the scar ) indicated with the black arrow in anterior view . In column B , breaks are formed due to the scar tissue , and the secondary source started to appear . After 3 . 7 s , the spiral is anchored around the scar , indicated with the black arrow in the posterior view , and persistently rotated around it . In the bottom row , we show the same simulation but the scar was not taken into account . In this case , the spiral does not change its original location ( only a slight movement , see the black arrows ) . To evaluate if this effect can potentially be registered in clinical practice we computed the ECG for our 3D simulations . The ECG that corresponds to the example in Fig 5 is shown in Fig 6 . During the first three seconds , the ECG shows QRS complexes varying in amplitude and shape and then more uniform beat-to-beat QRS morphology with a larger amplitude . This change in morphology is associated with anchoring of the rotor which occurs around three seconds after the start of the simulation . The initial irregularity is due to the presence of the secondary sources that have a slightly higher period than the original rotor . After the rotor is anchored , the pattern becomes relatively stable which corresponds to a regular saw-tooth ECG morphology . Additional ECGs for the cases of termination of the arrhythmia and anchoring are shown in supplementary S2 Fig . For the anchoring dynamics we see similar changes in the ECG morphology as in Fig 6 . The dynamical anchoring is accompanied by an increase of the cycle length ( 247 ± 16 ms versus 295 ± 30 ms ) . The reason for this effect is that the rotation of the rotor around an obstacle –anatomical reentry– is usually slower than the rotation of the rotor around its own tip—functional reentry , which is typically at the limit of cycle length permitted by the ERP . In the previous section , we showed that the described results on dynamical anchoring in an anatomical model of the LV of patients with post infarct scars correspond to the observations on ECGs during initiation of a ventricular arrhythmia . After initiation , in 18 out of 30 patients ( 60% ) a time dependent change of QRS morphology was observed . Precordial ECG leads V2 , V3 and V4 from two patients are depicted in Fig 7 . For both patients the QRS morphology following the extra stimuli gradually changed , but the degree of changes here was different . In patient A , this morphological change is small and both parts of the ECG may be interpreted as a transition from one to another monomorhpic ventricular tachycardia ( MVT ) morphology . However , for patient B the transition from polymorphic ventricular tachycardia ( PVT ) to MVT is more apparent . In the other 16 cases we observed different variations between the 2 cases presented in Fig 7 . Supplementary S3 Fig shows examples of ECGs of 4 other patients . Here , in patients 1 and 2 , we see substantial variations in the QRS complexes after the arrhythmia initiation and subsequently a transformation to MVT . The recording in patient 3 is less polymorphic and in patient 4 we observe an apparent shift of the ECG from one morphology to another . It may occur , for example , if due to underlying tissue heterogeneity additional sources of excitation are formed by the initial source . Overall , the morphology with clear change from PVT to MVT was observed in 5/18 or 29% of the cases . These different degrees of variation in QRS morphology may be due to many reasons , namely the proximity of the created source of arrhythmia to the anchoring region , the underlying degree of heterogeneity and fibrosis at the place of rotor initiation , complex shape of scar , etc . Although this finding is not a proof , it supports that the anchoring phenomenon may occur in clinical settings and serve as a possible mechanism of fast VT induced by programmed stimulation .
In this study , we investigated the dynamics of arrhythmia sources –rotors– in the presence of fibrotic regions using mathematical modeling . We showed that fibrotic scars not only anchor but also induce secondary sources and dynamical competition of these sources normally results their annihilation . As a result , if one just compares the initial excitation pattern in Fig 1A and final excitation pattern in Fig 1L , it may appear as if a distant spiral wave was attracted and anchored to the scar . However , this is not the case and the anchored spiral here is a result of normal anchoring and competition of secondary sources which we call dynamical anchoring . This process is different from the usual drift or meandering of rotors where the rotor gradually changes its spatial position . In dynamical anchoring , the break formation happens in the fibrotic scar region , then it spreads to the original rotor and merges with this rotor tip and reorganizes the excitation pattern . This process repeats itself until a rotor is anchored around the fibrotic scar region . Dynamical anchoring may explain the organization from fast polymorphic to monomorphic VT , also accompanied by prolongation in CL , observed in some patients during re-induction after radio frequency catheter ablation of post-infarct scar related VT . In our simulations the dynamics of rotors in 2D tissue were stable and for given parameter values they do not drift or meander . This type of dynamics was frequently observed in cardiac monolayers [38 , 39] which can be considered as a simplified experimental model for cardiac tissue . We expect that more complex rotor dynamics would not affect our main 2D results , as drift or meandering will potentate the disappearance of the initial rotor and thus promote anchoring of the secondary wavebreaks . In our 3D simulations in an anatomical model of the heart , the dynamics of rotors is not stationary and shows the ECG of a polymorphic VT ( Fig 6 ) . The dynamical anchoring combines several processes: generation of new breaks at the scar , spread of breaks toward the original rotor , rotor disappearance and anchoring or one of the wavebreaks at the scar . The mechanisms of the formation of new wavebreaks at the scar has been studied in several papers [15 , 37 , 40] and can occur due to ionic heterogeneity in the scar region or due to electrotonic effects [40] . However the process of spread of breaks toward the original rotors is a new type of dynamics and the mechanism of this phenomenon remains to be studied . To some extent it is similar to the global alternans instability reported in Vandersickel et al . [41] . Indeed in Vandersickel et al . [41] it was shown that an area of 1:2 propagation block can extend itself towards the original spiral wave and is related to the restitution properties of cardiac tissue . Although in our case we do not have a clear 1:2 block , wave propagation in the presence of breaks is disturbed resulting in spatially heterogeneous change of diastolic interval which via the restitution effects can result in breakup extension . This phenomenon needs to be further studied as it may provide new ways for controlling rotor anchoring processes and therefore can affect the dynamics of a cardiac arrhythmia . In this paper , we used the standard method of representing fibrosis by placement of electrically uncoupled unexcitable nodes with no-flux boundary conditions . Although such representation is a simplification based on the absence of detailed 3D data , it does reproduce the main physiological effects observed in fibrotic tissue , such as formation of wavebreaks , fractionated electrograms , etc [22] . The dynamical anchoring reported in this paper occurs as a result of the restructuring of the activation pattern and relies only on these basic properties of the fibrotic scar , i . e . the ability to generate wavebreaks and the ability to anchor rotors , which is reproduced by this representation . In addition , for each data point , we performed simulations with at least 60 different textures . Therefore , we expect that the effect observed in our paper is general and should exist for any possible representation of the fibrosis . The specific conditions , e . g . the size and degree of fibrosis necessary for dynamical anchoring may depend on the detailed fibrosis structure and it would be useful to perform simulations with detailed experimentally based 3D structures of the fibrotic scars , when they become available . Similar processes can not only occur at fibrotic scars , but also at ionic heterogeneities . In Defauw et al . [42] , it has been shown that rotors can be attracted by ionic heterogeneities of realistic size and shape , similar to those measured in the ventricles of the human heart [43] . These ionic heterogeneities had a prolonged APD and also caused wavebreaks , creating a similar dynamical process as described in Fig 1 . In this study however , we demonstrated that structural heterogeneity is sufficient to trigger this type of dynamical anchoring . It is important to note that in this study fibrosis was modeled as regions with many small inexcitable obstacles . However , the outcome can depend on how the cellular electrophysiology and regions of fibrosis have been represented . In modeling studies , regions of fibrosis can also be represented by coupled elements with a fixed resting potential or with detailed fibroblast models , or by smoothly varying but reduced diffusion [44] . However , different in-silico studies [14 , 45 , 46] on modeling fibrosis in AF demonstrated that in AF , the reentrant activities co-located at the borders of fibrotic regions , although different methodologies were used to model fibrosis . For example , in a reecent modeling study by Morgan et al [45] , rotors also stabilize in the border zones of patchy fibrosis in 3D atria , although fibrosis was modeled with myocyte-fibroblast coupling . These results agree with multiple experimental and clinical studies , which also showed co-localization of rotors and fibrosis [5 , 7 , 8 , 11–14] . Our results suggest that the possible mechanisms for the fact that such patterns are so abundant is due to dynamical anchoring . Therefore , we expect that different methodologies in modeling studies would give rise to similar results . In our simulations the dynamics of rotors in 2D tissue were stable and for given parameter values they do not drift or meander . Although this type of dynamics was observed in cardiac monolayers [39 , 47] , the size of the myocardial cell in cultured monolayer is usually smaller than myocytes in myocardium , and they do not have a cylindrical form . Moreover , the gap junctions in cell cultures are usually found circumferentially , whereas in vivo gap junctions are found mostly at intercalated disks [48] . As these differences may affect the stabilty of the rotors , i . e . cause more complex dynamics , and it may also affect the results of 2D studies . Another possible effect by which fibrotic scars can influence rotor dynamics is the electrotonic influence from the fibrotic scar region . Indeed , electrotonic effects are well known in cardiac electrophysiology and can strongly affect the heterogeneity of cardiac tissue and the susceptibility to arrhythmias [49] . It was also estimated that , in many models of cardiac cells , the spatial length of the electrotonic effects is of the order of 0 . 5-1cm [50 , 51] . In our case we see dynamical anchoring for spirals located as far as 10-12 cm , which is far beyond these values . In addition , we also observe the same effect in case if we do not have ionic remodelling in the scar region ( Fig 3 ) , and thus in that case AP of all cells are the same ( up to some possible boundary effects ) . Therefore , we think that the electrotonic influence from the scar is unlikely to be a main determinant of the dynamic anchoring . However , a heterogeneity around the scar has some effect on the anchoring process which can also be seen in Fig 3 . Although the dynamical anchoring reported in this paper will always bring the rotor to the scar region , the precise location of the rotor inside this region was not studied here . This question requires additional investigation , which is currently being performed in our research group . The first preliminary results indicate that in case of a scar with a complex structure multiple anchoring sites are possible . Their location depends on several factors , such as the location of the initial rotor , the presence and the extent of the ionic remodelling . In this work , to describe ionic remodeling we decreased the conductance of INa , IKr , and IKs as explained in the Methods Section . Identification of the specific features of the anchoring region and its delineation is of great importance as it may have implications for the treatment of the related arrhythmias . It would also be of value to quantify the excitation patterns in terms of number of re-entrant sources during the dynamic anchoring process , which can be done using the methodology in Panfilov et al [52] and or Vandersickel et al [53] . The most common mechanism of scar-related VT is due to slow conduction through a surviving channel in the scar . In this manuscript we did not model it explicitly . This is because we wanted to investigate a general phenomenon which can occur in cardiac tissue in the presence of fibrosis and did not try to reproduce specific geometries of the scar and slow conducting channels . It would be interesting to perform similar studies based on detailed reconstructions of infarction scars , such as [54] and see if the presence of the slow conduction channel ( s ) would affect the process of anchoring . However , note that complex patterns of fibrosis similar to those studied in our paper were observed for example in patients with non-ischaemic cardiomyopathy [55] . This type of substrate can also result in monomorphic ventricular tachycardia typical for anchored rotational activity . In this paper , we considered the case of a pre-existing rotor and focused on its interaction with the fibrotic scar . The formation of a rotor can occur via multiple possible mechanisms ( e . g . [15–18] ) which we did not take into account . This is because we wanted to address the process at a stage common for all mechanisms . This assumption is idealized , and it would be more natural to consider the complete sequence of transition from the sinus rhythm to rotor formation and then to its interaction with the scar . This is because such interaction process can potentially influence the process of dynamic anchoring . However , such interactions will add additional levels of complexity to the problem on top of the effects studied in our paper and may be specific for each particular mechanism . Therefore we decided to focus on this later stage of an already existing rotor , which is common for all these mechanisms . This is a limitation of our approach and it should be addressed in subsequent studies for each of the mechanisms of generation of the initial rotor . We have studied the dependency of dynamical anchoring to the scar on size and fibrotic content of the scar in a simplified situation: the scar was of circular shape , the fibrosis was modelled as diffuse fibrosis and had a constant level within the entire scar ( Fig 4 ) . It would be of value to extend such studies and consider different shapes of the scar and study the possible effects of this shape on the anchoring . Additionally , we could consider a non-homogeneous distribution of fibrosis inside the scar and study it with and without of ionic heterogeneity . Furthermore , it would be valuable to study the possible effects of different texture of fibrosis like interstitial and patchy patterns on the dynamical anchoring . Also , in this paper we have always considered an initial rotor rotating in a homogeneous part of the tissue . It would be important to study more realistic tissue setups , where fibrosis is not only present around the scar but also at distant locations where the rotor is present . We plan to address these shortcomings in subsequent research . Our 2D simulations are performed for rotors which have stationary rotation . As stationary rotation of free non-anchored rotors is unlikely to occur in the whole heart , these results account for an idealized situation and may change if the rotor rotation is not stationary . However , as we discussed only a qualitative relation between the data we think that our general interpretation is acceptable . Clinical ECG recordings used in our paper were taken after ablation . It would be important to compare pre-ablation ECGs showing arrhythmia dynamics in patients with collected LGE MRI images . Unfortunately such clinical information was not available to us . Most of our simulations were performed with a space step of 250 microns . Although such step is widely used in computational studies on cardiac propagation it is larger than the typical size of cardiac cells . Therefore , it would be of value to perform simulations with a smaller space step ( e . g . 100 microns ) . However , as 2D studies performed here use a generic representation of cardiac fibrosis we do not expect qualitative changes of the results obtained in this paper .
|
Rotors are waves of cardiac excitation like a tornado causing cardiac arrhythmia . Recent research shows that they are found in ventricular and atrial fibrillation . Burning ( via ablation ) the site of a rotor can result in the termination of the arrhythmia . Recent studies showed that rotors are often anchored to regions surrounding scar tissue , where part of the tissue still survived called fibrotic tissue . However , it is unclear why these rotors anchor to these locations . Therefore , in this work , we investigated why rotors are so abundant in fibrotic tissue with the help of computer simulations . We performed simulations in a 2D model of human ventricular tissue and in a patient-specific model of a patient with an infarction . We found that even when rotors are initially at large distances from the fibrotic region , they are attracted by this region , to finally end up at the fibrotic tissue . We called this process dynamical anchoring and explained how the process works .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
"dermatology",
"medicine",
"and",
"health",
"sciences",
"diagnostic",
"radiology",
"engineering",
"and",
"technology",
"cardiovascular",
"anatomy",
"cardiac",
"ventricles",
"fibrosis",
"magnetic",
"resonance",
"imaging",
"developmental",
"biology",
"electrocardiography",
"bioassays",
"and",
"physiological",
"analysis",
"cardiology",
"research",
"and",
"analysis",
"methods",
"scars",
"arrhythmia",
"imaging",
"techniques",
"atrial",
"fibrillation",
"electrophysiological",
"techniques",
"rotors",
"mechanical",
"engineering",
"radiology",
"and",
"imaging",
"diagnostic",
"medicine",
"cardiac",
"electrophysiology",
"anatomy",
"biology",
"and",
"life",
"sciences",
"heart"
] |
2018
|
Dynamical anchoring of distant arrhythmia sources by fibrotic regions via restructuring of the activation pattern
|
The critical stem cell transcription factor FoxD3 is expressed by the premigratory and migrating neural crest , an embryonic stem cell population that forms diverse derivatives . Despite its important role in development and stem cell biology , little is known about what mediates FoxD3 activity in these cells . We have uncovered two FoxD3 enhancers , NC1 and NC2 , that drive reporter expression in spatially and temporally distinct manners . Whereas NC1 activity recapitulates initial FoxD3 expression in the cranial neural crest , NC2 activity recapitulates initial FoxD3 expression at vagal/trunk levels while appearing only later in migrating cranial crest . Detailed mutational analysis , in vivo chromatin immunoprecipitation , and morpholino knock-downs reveal that transcription factors Pax7 and Msx1/2 cooperate with the neural crest specifier gene , Ets1 , to bind to the cranial NC1 regulatory element . However , at vagal/trunk levels , they function together with the neural plate border gene , Zic1 , which directly binds to the NC2 enhancer . These results reveal dynamic and differential regulation of FoxD3 in distinct neural crest subpopulations , suggesting that heterogeneity is encrypted at the regulatory level . Isolation of neural crest enhancers not only allows establishment of direct regulatory connections underlying neural crest formation , but also provides valuable tools for tissue specific manipulation and investigation of neural crest cell identity in amniotes .
The neural crest ( NC ) is a transient population of cells that migrates throughout the embryo and forms many different cell types , including neurons and glia of the peripheral and enteric nervous systems , bone and cartilage of the craniofacial skeleton and melanocytes [1] , [2] . Induction of the neural crest is thought to involve a number of growth factors , including Wnts and BMPs , that establish the neural plate border region that contains the prospective neural crest . This region is characterized by the collective expression of a number of transcription factors , including Msx1/2 , Pax3/7 and Zic1 , termed neural plate border genes [3] . Subsequently , as neurulation progresses , additional transcription factors are expressed by neural crest precursors residing within the neural folds and dorsal neural tube . These transcription factors , termed neural crest specifier genes , include Sox9 , FoxD3 , Ets1 , Snail1/2 and Sox10 , amongst others [2] . Regulatory interactions between neural plate border genes , neural crest specifier genes and signaling inputs generate a complex gene regulatory network ( GRN ) that orchestrates essential steps in neural crest ontogeny , including emigration from the neural tube , migration to appropriate locations and differentiation into many different cell types . An important challenge is to establish direct connections within the neural crest GRN . For example , the neural plate border marker , Pax7 , is essential for expression of a number of different neural crest specifier genes [4] such that its loss-of-function results in the subsequent loss of Sox10 and Snail2 in the cranial neural crest . Thus , these genes act downstream of Pax7 , either by direct or indirect interactions . For the case of Sox10 , regulatory analysis revealed direct inputs from Sox9 , Ets1 and Myb , but not Pax7 [5] , suggesting that effects of loss of Pax7 on Sox10 expression are likely to be indirect . This raised the question of what genes might be direct targets of neural plate border genes like Pax7 . Of the neural crest specifier genes , FoxD3 is one of the first markers of premigratory neural crest in many vertebrate species including mouse , chick , Xenopus and zebrafish [6]–[12] . Its initial expression in the neural tube precedes that of Sox10 and several pieces of evidence suggest that FoxD3 is critical for initiating a cascade of neural crest gene expression that controls their emigration from the neural tube . For example , ectopic expression of FoxD3 in the chick neural tube induces expression of neural crest markers and increases emigration from the neural tube [13] . Similarly in Xenopus ectopic expression at the 8-cell stage increases the expression of neural crest markers , while expression of dominant-negative FoxD3 reduces expression of genes like Snail2 , Twist and Ets1 [11] and depletes some neural crest derivatives [9] , [11] , [14] , [15] . Despite its important role both in stem and neural crest cells , no regulatory element ( s ) controlling the onset of FoxD3 expression are known . To define linkages and assess direct regulatory interactions in the neural crest gene regulatory network with particular interest in possible targets of Pax7 , we set out to dissect the cis-regulatory regions of the critical neural crest gene , FoxD3 . Taking advantage of the chick's compact genome and ability to assay putative regulatory regions by electroporation , we have identified two enhancers , NC1 and NC2 , that mediate reporter expression in spatially and temporally distinct manners in the chick embryo , and in combination closely recapitulate the endogenous expression of FoxD3 . Detailed regulatory analysis shows that initial expression of FoxD3 in both cranial and trunk neural crest requires direct input from neural plate border genes , Pax7 and Msx1/2 . These factors function in combination with the neural crest specifier gene , Ets1 , to bind to the cranial NC1 regulatory element . However , at vagal/trunk levels , they function together with the neural plate border gene Zic1 to activate the NC2 enhancer . These results not only reveal region-specific enhancer activity in the neural crest , but also expand the neural crest GRN and inform upon direct interactions therein . Conserved between mouse and chick , these enhancers further provide excellent tools for assaying gene regulation and manipulation of neural crest gene expression in amniotes .
In the cranial neural tube , expression of FoxD3 initiates in premigratory neural crest cells at HH8- ( Figure 1A ) , with strong and rapid onset of expression that precedes that of Sox10 or Snail2 . At this stage , the FoxD3 expression domain includes the neural folds of the forebrain and midbrain . Subsequently , at HH8+ , FoxD3 expression expands posteriorly to the hindbrain ( Figure 1B ) . As neural crest cells delaminate at HH9 and migrate at stage 10 , FoxD3 is maintained or expressed de novo at high levels by many migrating cranial crest cells ( Figure 1C , 1D ) . The domain of expression of FoxD3 at this stage includes premigratory and migratory crest extending from the midbrain to the trunk , with the exception of the neural folds at the level of rhombomere 3 ( dotted arrow in Figure 1C ) . Expression persists through subsequent stages as the neural crest advances to surround the optic vesicle and populate the first branchial arch . At vagal and trunk levels , FoxD3 is also expressed by premigratory and migrating neural crest cells . FoxD3 transcript expression initiates as the neural folds appose in the midline at stage HH9 . At HH12 , FoxD3 is detected in migrating vagal crest as the first wave of cells leaves the neural tube ( white arrow in Figure 1I ) and with time , FoxD3 expression initiates at progressively more caudal levels in the trunk neural tube and migrating neural crest . At later stages , FoxD3 expression is maintained in a subset of neural crest derivatives , including peripheral ganglia [16] . Double fluorescent in situ hybridization for FoxD3 and Sox10 reveals differences in the expression domains of these neural crest specifier genes . FoxD3 transcripts are detected in the premigratory population prior to Sox10 , which is expressed in cranial , vagal and trunk neural crest cells only as cells leave the neural tube ( arrows on Figure 1E and 1F ) . Therefore , even though FoxD3 and Sox10 both have been placed in the same hierarchical level in the neural crest GRN , they are recruited at different time points during neural crest specification . The genomic region of FoxD3 was examined for conservation across multiple vertebrate taxa including chick , mouse , human , opossum , Xenopus and zebrafish using the UCSC Genome Browser and ECR Browser . The region analyzed spanned 160 kb between the genes immediately up and downstream of FoxD3 , Atg4C and Alg6 respectively ( Figure 1O ) . To test putative enhancers for neural crest regulatory activity , eighteen conserved regions varying in size from 1 kb to 4 kb were cloned into an eGFP reporter vector [17] and electroporated into the entire epiblast of stage HH4 or dorsal neural tube of HH8–14 chick embryos , together with pCI-H2B-RFP as a ubiquitous tracer to verify efficacy of transfection . By testing conserved regions within the FoxD3 locus , we found two enhancers that drive specific expression of eGFP in the neural crest , in a manner that collectively closely recapitulates the endogenous pattern of FoxD3 expression . Enhancer NC1 directed expression of eGFP in the premigratory cranial neural crest analogous to the early endogenous expression of FoxD3 ( Figure 1G–1H ) . eGFP in the cranial neural folds was detected from stage HH8+ ( Figure 1G ) , in the dorsal neural tube and on a few neural crest cells during emigration ( Figure 1H , 1J , 1L ) , lasting until approximately stage HH14 , at which time only very weak eGFP expression could be detected . While migrating neural crest from the midbrain to rhombomere ( R ) 2 exhibited NC1 mediated eGFP expression ( Figure 1J ) , no eGFP expression was observed caudal to R3 . This contrasts with the endogenous FoxD3 pattern , which is observed in R4 , R6 and more posterior crest ( Figure 1I ) . Enhancer NC2 , in contrast to NC1 , mediated strong eGFP expression in the premigratory , delaminating and migrating neural crest at and caudal to R6 ( Figure 1K ) , beginning at HH9 . This expression pattern of eGFP in the vagal and trunk neural crest recapitulates endogenous expression of FoxD3 ( Figure 1I ) at this axial level . The expression of both eGFP and endogenous FoxD3 mRNA extends to the premigratory/delaminating crest at the level of the 4th most caudal somite . Interestingly NC2 activity also controlled eGFP expression in a large subpopulation of migrating cranial neural crest at the level of the midbrain , R1 and R2 , which was detectable only after stage HH9+ , and expression in premigratory and migratory NC from R4 . To examine the activity of NC2 enhancer at later stages and in neural crest derivatives , we electroporated stage HH8–14 embryos in ovo , and fixed the embryos after 24–48 h ( HH15–20 ) . FoxD3 is expressed in most premigratory and migratory vagal and trunk neural crest , but is down-regulated in melanoblasts ( Figure S1A ) , which migrate underneath the ectoderm and initiate emigration approximately 24 h after the emigration of ganglionic neural crest in the chick . Interestingly , we observed expression of eGFP in melanoblasts prior to and during migration ( Figure S1B ) , in addition to expression in the dorsal root and trigeminal ganglia . To confirm that this expression was due to activity of the enhancer and not stability of eGFP , we performed in situ hybridization for eGFP and detected mRNA for eGFP in melanoblasts and dorsal root ganglia ( Figure S1D–S1E ) , suggesting that eGFP is indeed ectopically expressed by melanoblasts , under control of the NC2 enhancer . Expression of eGFP was also seen in neural crest cells migrating along the enteric neural crest pathway ( Figure S1F ) . In contrast to NC2 , at HH14 very weak expression of NC1 activity was confined to the branchial arches whereas no expression was observed in cranial ganglia . We next examined overlap of endogenous FoxD3 expression with reporter expression driven by NC1 and NC2 by performing double labeling with eGFP and FoxD3 antibodies . The results show that NC1-driven eGFP expression completely overlapped with that of endogenous FoxD3 protein in stage HH9 embryos ( Figure 2A–2C ) . Similarly , NC2-driven eGFP expression in migrating cranial neural crest overlapped with endogenous FoxD3 protein expression at stage 11 ( Figure 2D–2F ) and with FoxD3 in delaminating and migrating crest at the trunk/vagal levels at stage 12 ( Figure 2G–2H ) . The complete overlap between enhancer activity and FoxD3 expression strongly suggests that NC1 and NC2 are the responsible regulatory modules for the control of endogenous FoxD3 in the neural crest . To determine if orientation of the enhancers affects their activity , NC1 and NC2 enhancers were cloned in reverse orientation into ptk-eGFP and electroporated in HH4 embryos . The results show that both have equivalent ability to drive reporter expression in reversed as in their endogenous orientation , without significant changes in pattern or levels of activity ( Figure 2J , 2K ) . Finally , we examined whether these enhancers were conserved across amniotes . To this end , we cloned the homologous conserved regions from the mouse genome and mouse ( m ) NC1 and mNC2 constructs were electroporated into chick embryos at gastrula stages . The results show that the patterns of eGFP expression driven by mNC1 and mNC2 were identical to those observed with chick NC1 and NC2 ( Figure 2L , 2M ) , suggesting that these enhancers are conserved between chick and mouse and likely throughout amniotes . To examine the dynamic nature and combined activity of the two enhancers in the migrating cranial neural crest , we co-electroporated NC1 ( green ) and NC2 ( blue ) enhancers in combination with a previously identified cranial neural crest Sox10E element ( red ) [5] that expresses in all emigrating and migrating neural crest cells . Reporter expression of multiple fluorophores was then visualized in transverse sections of slices through the midbrain region , using a novel slice culture protocol [18] . Time-lapse movies revealed differential temporal and spatial activity of NC1 and NC2 enhancers . While NC1 activity was present in the premigratory neural crest , the expression it drove in the dorsal neural tube appeared transient in most cells and preceded that driven by the Sox10E enhancer ( white arrow in Figure 3A ) . NC1 activity then recurred in a small subpopulation of actively migrating cranial crest cells that concomitantly displayed Sox10E activity ( black arrows in Figure 3B and 3C , Video S1 ) . In contrast , NC2 activity was observed in very few cells within the neural tube ( red arrow in Figure 3C ) , and only a few delaminating neural crest cells coincident with Sox10E activation . Thereafter , NC2 drove expression in a large subset of migrating cranial neural crest cells , which were also positive for Sox10E activity ( black arrows in Figure 3C and 3D ) . To further investigate neural crest heterogeneity with respect to enhancer-driven expression , we co-electroporated embryos with NC1 ( green ) and NC2 ( red ) enhancers and observed neural crest formation and migration by time lapse microscopy . Analysis of the movies suggested that there was little overlap between cells showing activity of NC1 and those with NC2 ( Figure 3E–3H , Video S2 ) . Only a few cells co-expressed eGFP and RFP and this may reflect perdurance of the reporter that may be more stable than the endogenous transcription factor . These results suggest that there is highly dynamic regulation of FoxD3 in migrating neural crest cells and suggest that there may be distinct subpopulations that reflect activity of NC2 but not NC1 , or vice versa . They further suggest that NC1 activity may be largely responsible for the transient early expression of FoxD3 in the neural tube ( arrow in Figure 3F ) , whereas NC2 activity at cranial levels may be primarily responsible for FoxD3 expression in migrating neural crest cells ( arrows in Figure 3G and 3H ) . The dynamic expression driven by the enhancer NC1 and its early activation , correlating with the onset of endogenous FoxD3 , led us to explore upstream regulators and their binding motifs within NC1 , responsible for early activation in the premigratory neural crest . To this end , conservation across vertebrates was used as a guide to identify putative core regions within the enhancer . The central region of NC1 was highly conserved with human , mouse and Xenopus , but showed no sequence conservation with zebrafish . Primers were designed to amplify fragments of NC1 , which were tested for activity at stages HH9–10 , corresponding to the time it drove strongest expression . Using this approach , NC1 was reduced to 553 bp ( NC1 . 1 ) without loss of activity ( Figure 4A , 4B ) . A further deletion to 303 bp ( NC1 . 2 ) resulted in weak eGFP expression specifically in the cranial neural crest ( Figure 4A , 4D ) , suggesting that the regions at the ends of NC1 . 1 amplify activity of the enhancer , although the critical regions are present within NC1 . 2 . The sequence of NC1 . 2 was further analyzed by substituting 100 bp regions of sequence with eGFP coding sequence within the NC1 . 1 fragment . eGFP coding sequence was chosen as a random sequence to substitute for enhancer regions , so that the size and spacing was maintained , but did not alter expression in control experiments . This analysis revealed that 200 bp was required for expression mediated by the enhancer ( Figure 4A ) . We then substituted 20 bp blocks of sequence with eGFP coding sequence across this region within NC1 . 1 . This analysis revealed a region of 80 bp that was critical for detectable expression of eGFP ( Figure 4A ) . An adjacent 92 bp region was required as a unit for eGFP expression; however substitutions of 20 bp blocks within this secondary region weakened but did not eliminate eGFP expression . None of the substitutions resulted in expansion of enhancer-driven expression . The 172 bp fragment ( NC1 . 3 ) containing the most critical and supportive regions was amplified and electroporated into embryos , and the 80 bp putative core region ( NC1 . 4 ) was tested by placing two copies in tandem into the ptkeGFP construct . NC1 . 3 alone drove very weak expression of eGFP in the neural crest . Interestingly , the NC1 . 4 cancatamer was sufficient to drive eGFP expression in the same pattern as the full-length NC1 enhancer , albeit slightly weaker , suggesting that the 80 bp NC1 . 4 fragment contains the core elements essential for activity of this enhancer . Potential transcription factor binding sites within the core region were identified using Rvista and Jaspar databases ( Figure S2A ) . Mutations were made to these sites by substituting 6–8 bp of the core binding site ( marked in red or blue in Figure S2A ) . Mutations to the Ikaros binding site or to the Ets/Zeb binding site did not affect expression of eGFP ( Figure S2B and S2C ) . In contrast mutation of the homeodomain site ( Figure S2D and S2E ) or Elk/Ets site resulted in loss of eGFP expression . Additionally , mutation of an Msx site ( Figure S2A ) reduced activity of the enhancer . Pax7 , Msx1 and Msx2 are neural plate border genes expressed in the neural folds prior to expression of FoxD3 , and candidates for direct activators of FoxD3 . All of these can potentially bind to the homeodomain sites . Furthermore , Ets1 is expressed specifically in the cranial neural crest concomitant with the onset of FoxD3 expression . Similar to the dissection of NC1 , we performed a series of deletions and substitutions to identify the core structure critical for activity of the NC2 enhancer ( Figure 4H ) . NC2 is highly conserved in mouse , human , Xenopus and zebrafish . Stepwise deletions revealed a fragment , termed NC2 . 9 , with similar albeit weakened activity to that seen with NC2 in the vagal and trunk neural crest , as well as weak activity in cranial migratory neural crest ( Figure 4H , 4I ) . Subsequently , 100 bp and 30 bp substitutions were made within NC2 . 9 , narrowing the essential regions of the enhancer to approximately 120 bp , encompassing a 90 bp core region surrounded by auxiliary regions required for strong expression ( Figure 4H ) . Several deletions of the NC2 enhancer resulted in eGFP expression in the developing retina ( NC2 . 6 , NC2 . 9 M20 ) and otic vesicle ( NC2 . 7 ) , and using the full-length NC2 enhancer to drive eGFP , occasional weak expression could also be seen in these structures . Importantly , deletion of the Zic site within the 90 bp critical core region resulted in complete loss of activity of the enhancer ( Table S1 ) . The auxillary ( amplifying ) region contains Pax , Ets and SoxE sites . Deletion of Pax or SoxE binding sites in the auxiliary region caused loss of NC2 activity in cranial neural crest , but did not affect vagal/trunk NC2 activity ( Table S1 ) . Similarly , deletion of an Ets1 site in the auxiliary region ( M20 ) , abolished activity in R1–R3 of the cranial neural crest , but did not affect vagal/trunk activity ( Table S1 ) . The results suggest that the NC2 enhancer itself is differentially regulated in the cranial neural crest versus trunk neural crest . We next tested whether the putative transcription factors implicated by enhancer dissections could regulate enhancer driven reporter expression . To this end , individual embryos were electroporated on one side with FITC-conjugated control morpholino plus enhancer directing Cherry expression and with FITC-conjugated blocking morpholino plus enhancer-Cherry on the contralateral side . For NC1 , morpholino-mediated loss of Pax7 ( Figure 5B , 5K ) protein resulted in significant loss of reporter expression on the target morpholino side ( right ) . Whereas Msx1 knock-down alone resulted in a mild loss of Cherry expression and Msx2 knock-down had almost no phenotype ( data not shown ) , the double MO knock-down exhibited a strong loss of reporter expression ( Figure 5C , 5K ) . Additionally , knock-down of the transcription factor Ets1 resulted in strong loss of NC1 enhancer activity ( Figure 5D , 5K ) . In contrast , morpholinos against other neural crest or neural plate specifiers like Zic1 ( Figure 1E ) , Sox9 or AP-2 failed to alter NC1 reporter expression . These findings support the possibility that Pax7 , Msx1/2 , and Ets1 are direct inputs into the NC1 enhancer . To confirm that the loss of Cherry positive cells was not due to loss of neural crest cells on the morpholino-treated side of the embryo , we examined other neural crest markers in embryos in which enhancer-driven Cherry expression was depleted ( Figure S3 ) . At the concentration of morpholinos used , we observed little change in Sox9 expression ( Figure S3G–S3I ) , demonstrating that the neural crest population was present in morpholino-treated embryos . Similarly , immunostaining with the HNK-1 antibody at stage HH10 confirmed the persistence of neural crest cells after morpholino treatment ( Figure S3J–S3L ) . We next examined the effects of knocking down putative regulators on expression driven by the NC2 in the vagal/trunk neural crest . Electroporation of both Pax7 and Msx1/2 morpholinos resulted in loss of NC2 activity in the trunk neural crest ( Figure 5G , 5H , 5L ) similar to the effects observed for NC1 activity in the cranial crest . In addition , electroporation of the Zic1 morpholino caused strong loss of NC2 activity specifically in the trunk ( Figure 5J , 5L ) , suggesting this transcription factor is a key player in the regulation of trunk expression of FoxD3 . On the other hand , Ets1 knock-down had no affect on trunk activity of NC2 , which is not surprising given that this transcription factor is not expressed in the posterior neural crest ( Figure 5I , 5L ) . Taken together , these results place Pax7 and Msx1/2 as general regulators of FoxD3 expression , while Ets1 and Zic1 seem to specifically regulate NC1 and NC2 , respectively . To examine the effects of these regulators on endogenous gene expression , we performed morpholino-mediated loss-of-function of Pax7 , Ets1 , Msx1/2 and Zic1 and examined endogenous FoxD3 expression in newly forming cranial and trunk neural crest cells . Detection of FoxD3 was assessed by hybridization chain reaction ( HCR ) , which reflects transcript levels more accurately than in situ hybridization and at subcellular resolution [19] . We found that morpholino mediated knock-down of Msx1/2 , Pax7 and Ets1 caused a significant loss of cranial FoxD3 expression ( Figure 6A–6C ) at stage HH9 , but not Sox9 or HNK-1 expression . At trunk levels , knock-down of Msx1/2 , Pax7 and Zic1 resulted in a significant reduction of endogenous FoxD3 expression ( Figure 6D–6F ) , whereas loss of Ets1 had no effect . The results show that Pax , Msx and Zic transcription factors are not only important for mediating enhancer activity but also for endogenous expression of FoxD3 in the vagal/trunk neural crest . To demonstrate in vivo association of Pax7 , Msx1 and Ets1 transcription factors with the NC1 enhancer , we performed quantitative chromatin immunoprecipitation ( ChIP ) experiments . Cross-linked chromatin isolated from the midbrain dorsal neural tube of HH8–9 embryos was immunoprecipitated using Pax7 , Msx1 and Ets1 antibodies and ChIP-enriched DNA was used in site-specific qPCR , with primers designed to amplify fragments within the NC1 region . For all three factors , we found significant enrichment of the NC1 region amplicon , expressed as a percent of the total input chromatin , compared to IgG controls ( Figure 6G–6I ) . No enrichment was detected in the negative control regions in chromosome 8 ( Figure 6G–6I ) , confirming they are direct inputs into NC1 . These data demonstrate that Pax7 , Msx1 and Ets1 bind in vivo to the NC1 enhancer element in the cranial neural crest . Given the striking effects of Zic1 knockdown on NC2 activity in the trunk region , we hypothesized that this transcription factor directly binds this enhancer in vivo . To examine this , we dissected dorsal trunk neural tubes of stage HH12 embryos , crosslinked and immunoprecipitated chromatin with a Zic1 antibody . The results show significant enrichment of the NC2 region amplicon , expressed as a percent of the total input chromatin , compared to IgG ( Figure 6J ) . The results confirm that Zic1 directly associates with the NC2 enhancer in trunk neural crest . Taken together , these results reveal direct transcriptional regulators of FoxD3 in the neural crest GRN , and highlight the differential regulation of FoxD3 in the cranial and trunk neural crest cells .
Our results suggest that expression of FoxD3 is regulated in the avian neural crest by at least two enhancers , which direct expression in largely distinct spatiotemporal domains ( head versus vagal/trunk regions ) , as well as in different subpopulations of the cranial neural crest . The enhancer NC1 is active in premigratory and some migratory cranial neural crest rostral to R3 , while enhancer NC2 activity initiates in a single continuous wave caudal to rhombomere 4 , including vagal and trunk regions , but also later in a subpopulation of migrating cranial neural crest . In our analysis of the conserved regions within the FoxD3 locus , only these two regions were able to mediate reporter expression in patterns reflecting the distribution of neural crest . The proximity of the NC1 and NC2 enhancers to the FoxD3 coding region , the recapitulation of endogenous FoxD3 expression by the combined activity of the enhancers , and the effect of manipulating upstream regulators on both enhancers and endogenous FoxD3 expression , strongly suggest that NC1 and NC2 act as enhancers regulating endogenous expression of FoxD3 in the neural crest . Comparison of the activity of these two enhancers with the cranial Sox10 enhancer Sox10E2 [5] using time-lapse imaging demonstrated for the first time that there is dynamic regulation of multiple enhancers within a population of cranial neural crest cells . We observed that the activity of the cranial NC1 enhancer is initially restricted to cells in the dorsal neural tube; only later is it activated de novo in actively migrating cranial neural crest cells , where its activity is preceded by that of Sox10E2 . NC2 is active in only a few delaminating/emigrating cranial neural crest cells but in a majority of the migrating neural crest population . Interestingly , there is little overlap of NC1 and NC2 activity in the cranial neural crest , whereas both overlap with Sox10E2 , which appears to be active in all of the migrating cranial crest population . The minimal overlap in activity of NC1 versus NC2 in cranial neural crest populations raises the interesting possibility that there may be a regulatory switch of enhancers from NC1 to NC2 at the endogenous promoter of FoxD3 when the cells reside within the dorsal neural tube and/or are emigrating . Such competition at the promoter could occur if only a single enhancer can be functional at any given time on the FoxD3 promoter . If this is the case , the very few double labeled cells expressing NC1- and NC2-driven reporter expression may represent perdurance of eGFP protein rather than the actual levels of enhancer activity . The finding that NC1 and NC2 enhancers are active in generally separate cranial neural crest populations further suggests that the cranial neural crest represents a heterogeneous population , even as the cells are delaminating from the neural tube , and that this heterogeneity may be encrypted at the regulatory level . It is intriguing to speculate that the differential activity of NC1 and NC2 in distinct subpopulations may reflect differential cell fate and commitment status of future neural crest derivatives . Consistent with the possibility that NC1 and NC2 activity may reflect commitment to different lineages , NC2 is later active in neural crest-derived dorsal root and trigeminal ganglia , whereas NC1 is active transiently in the branchial arches , but not in peripheral ganglia . The activity of NC2 in the vagal and trunk neural crest recapitulated expression of endogenous FoxD3 in premigratory and migratory neural crest cells . In addition , FoxD3 is retained by a subset of neural crest derivatives [16] . Consistent with this , conditional knockout of FoxD3 in neural crest cells using the Wnt1-Cre line suggests that FoxD3 is required to maintain neural crest progenitors and that its loss biases their derivatives toward a mesenchymal fate at the expense of neural derivatives [24] . Thus , it appears to regulate the switch between neural/glia and melanocyte lineages [16] . NC2 not only was active in neuronal derivatives , but also directed activity in neural crest cells migrating along the dorsolateral pathway , which are melanocyte precursors that migrate 24 h after the ventrolateral population migrate to the ganglia . Cells on the dorsolateral population do not normally express FoxD3 [7] . Thus , NC2 likely is missing a repressor region for the pigment lineage that is present in the endogenous regulatory region . In fact , ectopic expression of FoxD3 in melanoblasts inhibits their migration onto the dorsolateral pathway , while down-regulation of FoxD3 results in premature dorsolateral migration and increases melanocyte differentiation in cultured neural crest [7] . FoxD3 represses transcription of Mitf , a key transcription factor required for melanocyte development [16] , [25] . Our finding of an active enhancer in melanoblasts suggests that FoxD3 is normally repressed in melanoblasts , and this repression does not occur within the NC2 region . In the zebrafish histone deacetylase 1 ( hdac1 ) mutant , a severe loss of mitfa positive melanophores can be rescued by partial reduction of FoxD3; suggesting hdac1 is required to repress FoxD3 in melanophores [25] . It is not yet clear whether this repression is direct or indirect and if it is conserved across species . The current results establish for the first time a direct regulatory connection between the neural plate border genes , Pax7 and Msx1/2 , and FoxD3 , suggesting it is an immediate downstream target . This confirms and validates previous indirect evidence in Xenopus , lamprey and mouse , and provides further support for a conserved gene regulatory network in the neural crest . Pax7 and Pax3 are closely related paralogs which have overlapping expression and function [26] . Pax3 and Pax7 bind identical DNA binding domains , and while they show equal affinity for binding to the paired domain , Pax7 shows a higher affinity for the homeobox domain [27] . Both Pax3 and Pax7 are expressed in the developing neural crest , but in overlapping and distinct regions of the neural crest , and these patterns differ between species . In mouse and Xenopus , Pax3 is expressed in premigratory neural crest along the neural axis , and Pax7 is restricted to cranial levels ( and very weak in Xenopus ) [13] , [28] , [29] . In chick and zebrafish , Pax7 is expressed throughout the developing crest , and whereas Pax3 expression in neural crest is restricted to trunk levels in chick , in zebrafish it is also seen at cranial levels [4] , [30] , [31] . Evidence from Xenopus , mouse and lamprey suggests that Pax3 and/or Pax7 is required for FoxD3 expression and neural crest specification [13] , [22] . In chick , Pax7 but not Pax3 knock-down at gastrula stages depletes neural crest specifier expression [4] . Mouse Pax3 mutants have a neural crest phenotype , and lack expression of FoxD3 in the trunk neural crest . However at cranial levels , where Pax7 is expressed , FoxD3 also is expressed [13] . Pax7 mutant mice have some craniofacial abnormalities , but survive well [28] , and the impact of Pax3/Pax7 combined knockout on the neural crest has not been described . Substitution of Pax3 by Pax7 rescues the development of the neural crest [26] , suggesting that there is partial redundancy between Pax3 and Pax7 in the mouse neural crest . In Xenopus , Pax3 is necessary for expression of FoxD3 [22] , and in lamprey the Pax3/7 gene is similarly necessary for expression of the FoxD3 homolog FoxD-A [23] . Msx1 has been proposed to lie upstream of Pax3 , FoxD3 and Snail2 during neural crest induction in Xenopus [21] . Loss of Msx1 or Msx2 in mice causes craniofacial abnormalities [32] , [33] , while the combined loss resulted in major defects in cranial neural crest derivatives , including mispatterning or reduction in size of cranial ganglia , loss , hypoplasticity or malformation of cranial bones , and conotruncal abnormalities [34] . Ablation of FoxD3 in mice in neural crest using Wnt-cre causes a similar phenotype at cranial levels; loss or reduction of many craniofacial structures , reduction in the size of cranial ganglia , subtle cardiac neural crest defects and also reduction in dorsal root ganglia size , and loss of enteric neural crest [24] . Cranial neural crest cells are still capable of undergoing migration in the absence of FoxD3 or Msx1/2 , but many undergo apoptosis; in FoxD3 mutants apoptosis was seen in the neural tube or during migration [24] , and in Msx1/2 mutants in the trigeminal ganglia and branchial arches [34] . As yet , the expression pattern of FoxD3 in the Msx1/2 mouse mutants is not known; however the strong similarities between the FoxD3 and Msx1/2 mutants at cranial levels provide support to the idea that Msx1/2 are immediately upstream of FoxD3 in the cranial neural crest . Differences at cranial levels between the mutant mice may reflect other roles of Msx genes , such as in neural tube and bone development . Other differences between the phenotypes suggest that in mice , Msx1/2 is not critical for neural crest development at trunk levels , unlike FoxD3 . Although Msx transcription factors have been primarily described as transcriptional repressors [35] , there is growing evidence for their role as transcriptional activators as well [36] , [37] . Our results demonstrate that during avian cranial neural crest specification , Msx1/2 act as transcriptional co-activators of FoxD3 . Our data also show that Ets1 is necessary for initial FoxD3 expression since electroporation of Ets1 morpholinos during gastrulation ( at HH5 ) depletes FoxD3 expression at HH9 . In contrast , a dominant-negative Ets1 inhibited cranial neural crest migration but did not result in decreased FoxD3 expression [38] . Examination of the expression of FoxD3 and Ets1 by in situ hybridization suggests that Ets1 and FoxD3 are expressed concomitantly in the cranial neural crest . The difference in results between these two studies likely rests in the stages at which the knock-down reagents were effective , with the present results uncovering an earlier role for Ets1 . Recent work on the Sox10E2 enhancer showed that Sox10 expression in the cranial neural crest is regulated by Ets1 , Sox9 and cMyb [5] , [39] . The finding that Ets1 participates in activation of both FoxD3 and Sox10 at cranial level solidifies its potential crucial activation role in regulation of the cranial neural crest as a factor that initiates the specification module of the neural crest gene regulatory network . Interestingly , mis-expression of Ets1 in trunk levels confers cranial neural crest-like characteristics on trunk neural tube cells; namely increased delamination of neural crest independent of cell cycle phase [38] . This suggests that it plays a critical role in conferring head/trunk differences in the neural crest . However , conservation of this regulation across vertebrates remains to be determined . Ets1 is expressed by premigratory and migratory cranial neural crest in mice [40] and Xenopus [41] . Mice null for Ets1 have defects in cardiac neural crest , but none reported in cranial neural crest [40] , [42] . Whether there is compensation for Ets1 in the cranial neural crest by other family members remains to be determined . Ets1 expression in chick neural crest is restricted to cranial levels; R4 and more rostral regions [38] , [43] . Interestingly , the NC1 enhancer for FoxD3 is not active in R4 , whereas the Sox10E2 enhancer is active to R6 [5] , and Ets1 is active in R4 but not further caudally [38] . Although there is little published information regarding the molecular players are involved in the establishment of the more caudal neural crest populations , the present results implicate the neural plate border specifier , Zic1 , as a critical factor in the control of FoxD3 expression at vagal and trunk levels . Zic1 has been shown to be required for FoxD3 expression in Xenopus neural crest [21] , [22] where it is likely to partner with Pax3 in neural crest specification . Conversely , over-expression of Zic1 causes expansion of the FoxD3 and Snail2 expression domains [22] , albeit it is unclear whether this occurs via direct or secondary interactions . The role of Zic1 as a trunk specific activator of FoxD3 is corroborated by expression data ( Simões-Costa M . , unpublished observations ) suggesting much higher Zic1 transcript levels in the vagal/trunk avian neural folds than at cranial levels at the onset of FoxD3 expression . Our results are consistent with complementary functions of Zic1 in trunk and Ets1 in cranial neural crest specification in the avian embryo . The present study expands the number of known direct regulatory interactions in the cranial neural crest gene regulatory network , confirming a direct regulation of FoxD3 by Pax3/7 and Msx1/2 , and revealing a previously unknown regulation of FoxD3 by Ets1 . We have also identified Zic1 as a key player in setting up the FoxD3 expression domain in the trunk neural crest . Several other genes , like Hairy2 , Sox10 and Sox5 , have been suggested to regulate FoxD3 expression [44]–[46]; however it remains to be determined whether this regulation is direct or indirect . It is well known that the developmental potential of neural crest cells varies along different levels of the neural axis . Quail/chick chimeras have elegantly demonstrated that both the pathways of migration and derivatives differ depending upon the axial level from which neural crest cells emigrate [47] . For example , cranial but not trunk neural crest cells normally contribute to bone and cartilage . Similarly , vagal neural crest cells contribute to the enteric nervous system whereas other neural crest populations normally do not [48] . Our data show that the inputs to FoxD3 in the vagal/trunk region are distinct from those functioning at cranial levels , suggesting a model for region-specific expression of FoxD3 ( Figure 7 ) . Whereas the neural plate border specifier Zic1 appears to be a critical input for NC2 activity in the trunk , Ets1 is critical for activating NC1 at cranial levels . Both Zic1 and Ets1 transcription factors appear to act in concert with Pax7 and Msx1/2 which are expressed along the entire neural axis . To date , no transcription factors have been found to be selectively expressed in particular regions of the premigratory neural crest . However , the discovery of cranial-specific enhancers for FoxD3 ( this study ) and Sox10 [5] clearly suggest that these differences are inherent at the regulatory level . The existence of these enhancers supports the idea that both spatial and temporal information is encoded in the genome .
The genomic region of chicken FoxD3 was compared to other vertebrates using the ECR Browser ( http://ecrbrowser . dcode . org ) and comparative analysis tracks of UCSC Genome Browser ( http://genome . ucsc . edu/ ) . We analyzed a 160 kb genomic region encompassing the FoxD3 locus up to the first upstream ( Atg4C ) and downstream ( Alg6 ) of neighboring genes . Regions containing elements found to be highly conserved across most vertebrates including human , mouse and Xenopus were amplified using Expand High Fidelity Plus system ( Roche , Indianapolis , IN ) with CH261-166E22 and CH261-100C15 ( CHORI BAC Resources , http://bacpac . chori . org ) BAC clones as templates and directionally cloned into the ptkeGFP or ptkCherry vectors [5] , [17] . Mouse neural crest enhancers mNC1 and mNC2 were amplified using Expand High Fidelity Plus system from genomic cDNA . For use in multiple enhancer time-lapse experiments , ptkCitrine and ptkCerulean plasmids were constructed by swapping the eGFP coding region by Citrine and Cerulean sequences , respectively . Appropriate enhancer elements ( Sox10E from [5] and NC1 , NC2 – this study ) were cloned into ptkCherry , ptkCitrine and ptkCerulean , respectively . Chicken embryos were electroporated at HH4 using previously described techniques [1] , [49] . FITC-conjugated morpholinos ( against target factors or control morpholino ) at concentrations from 1–3 mM combined with 1 mg/ml of enhancer_reporter Cherry constructs were electroporated only on one half of the embryo . For morpholino knockdown of regulators , electroporations were performed at HH5 or HH5+ to avoid disruption of the neural plate border . Fifteen to twenty embryos were analyzed for each of the morpholinos used . To electroporate HH8–14 chicken embryos in ovo , previously described techniques [50] were used with minor modifications; both constructs were injected at a concentration of 2 mg/ml each , and embryos were electroporated with 5 30ms-square pulses of 22 V with 100 ms rest in between each pulse . After incubation , embryos were collected and fixed in 4% paraformaldehyde for 1 hour , then viewed using fluorescence microscopy . Images were captured using a Zeiss Axioskop 2 plus microscope with AxioVision 4 . 6 software , and compiled using Adobe Photoshop 7 . 0 and Adobe Illustrator 10 . For dynamic multiple enhancer analysis in slice culture , embryos were electroporated at stage HH4 with three constructs simultaneously ( Sox10E-Cherry/NC1-Citrine/NC2-Cerulean ) to allow for proper spectral separation of reporter signals from different enhancers . After roughly 16 hours of incubation , cranial midbrain regions were prepared and imaged as described previously [18] . Whole mount and section in situ hybridization for FoxD3 were performed using previously described procedures [51] . Whole mount in situ hybridization for eGFP was modified using the guidelines in [52] . Double fluorescence in situ hybridization was performed according to [53] , and hybridization chain reaction ( HCR ) to detect endogenous FoxD3 was conducted according to [19] . Some embryos expressing RFP and eGFP were processed and cryosectioned at 14 mm . Select sections were labeled using the HNK-1 antibody ( diluted 1/50 ) , secondarily detected using goat anti-mouse IgM Alexa 350 ( 1/200; Molecular Probes ) . For whole mount immunostaining we used the protocol described by [54] ( FoxD3 antibody generously provided by Patricia Labosky and Michelle Southard-Smith ) . Regions of NC1 and NC2 were replaced with eGFP coding sequence using fusion PCR protocol . For 100 bp substitutions , the region of eGFP used was tggagtacaactacaacagccacaacgtctatatcatggccgacaagcagaagaacgg catcaaggtgaacttcaagatccgccacaacatcgaggacgg , for 30 bp substitutions acaagcagaagaacggcatcaaggtgaact and for 20 bp substitutions tggagtacaactacaacagc . Fragments were amplified using primers detailed in Table S2 and fused using the method adapted from [55] . Amplified fusion fragments were cloned into ptkeGFP and sequenced to ensure no additional mutations were present . ECR browser ( http://rvista . dcode . org/ ) and Jaspar database ( http://jaspar . genereg . net/cgi-bin/jaspar_db . pl ) were used to predict and analyze binding motifs within highly conserved regions . Individual sites were mutated by substituting 6–8 adjacent critical base pairs with GFP coding sequence , using fusion PCR and sub-cloning into ptkeGFP . Primers used are listed in Table S3 . Mutated enhancer constructs were electroporated into stage HH4 embryos as described above and analyzed for expression of eGFP and RFP at stages HH8–12 . A minimum of five embryos was examined for each condition . ChIP was performed using chromatin prepared from dorsal neural tube regions of HH8–10 ( 4–10 somite ) chicken embryos using Ets1 ( sc-350;Santa Cruz ) , Pax7 ( ab34360 . Abcam ) and Msx1 antibodies ( Sigma M0944 ) with normal rabbit IgGs ( sc-2027 , Santa Cruz;ab27478 , Abcam ) as previously described [56] . For the Zic1 ChIP chromatin was isolated from the dorsal neural tube regions from the trunk of HH11 embryos . Immunoprecipitation was performed with a Zic1 antibody from Sigma ( HPA004098 ) .
|
FoxD3 is an important stem cell factor expressed in many types of embryonic cells including neural crest cells . In the embryo , neural crest cells are a type of stem cell that forms diverse derivatives , including nerve cells , pigment cells , and facial structures . To better understand neural crest development and differentiation , we have explored how FoxD3 expression is regulated in these cells . By examining non-coding DNA , we have identified distinct genomic regions that mediate expression of green fluorescent protein ( GFP ) in a pattern that recapitulates FoxD3 expression . Interestingly , we find two genomic “on–off” switches or enhancers , called NC1 and NC2 , that drive GFP expression in a pattern that recapitulates FoxD3 expression at different times and places during neural crest development . We find that Pax and Msx proteins turn on both NC1 and NC2 enhancers by directly binding to them . In addition , cranial expression driven by NC1 requires a protein called Ets1 , whereas trunk expression of NC2 requires a different protein called Zic1 . The results show that FoxD3 in differentially regulated in distinct neural crest cell populations in a manner that is specifically encoded in the genome . These enhancers provide valuable tools for understanding neural crest development in birds and mammals .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology",
"organism",
"development",
"gene",
"expression",
"genetics",
"biology",
"gene",
"networks",
"genetics",
"and",
"genomics",
"cell",
"differentiation",
"dna",
"transcription"
] |
2012
|
Dynamic and Differential Regulation of Stem Cell Factor FoxD3 in the Neural Crest Is Encrypted in the Genome
|
Atypical protein kinase C ( aPKC ) isoforms have been implicated in cell polarisation and migration through association with Cdc42 and Par6 . In distinct migratory models , the Exocyst complex has been shown to be involved in secretory events and migration . By RNA interference ( RNAi ) we show that the polarised delivery of the Exocyst to the leading edge of migrating NRK cells is dependent upon aPKCs . Reciprocally we demonstrate that aPKC localisation at the leading edge is dependent upon the Exocyst . The basis of this inter-dependence derives from two-hybrid , mass spectrometry , and co-immunoprecipitation studies , which demonstrate the existence of an aPKC–Exocyst interaction mediated by Kibra . Using RNAi and small molecule inhibitors , the aPKCs , Kibra , and the Exocyst are shown to be required for NRK cell migration and it is further demonstrated that they are necessary for the localized activation of JNK at the leading edge . The migration associated control of JNK by aPKCs determines JNK phosphorylation of the plasma membrane substrate Paxillin , but not the phosphorylation of the nuclear JNK substrate , c-jun . This plasma membrane localized JNK cascade serves to control the stability of focal adhesion complexes , regulating migration . The study integrates the polarising behaviour of aPKCs with the pro-migratory properties of the Exocyst complex , defining a higher order complex associated with the localised activation of JNK at the leading edge of migrating cells that determines migration rate .
Migration of cells is critical to the development and the normal physiology of organisms; it also plays a more sinister role in the dissemination of cancer towards metastatic disease , a process typically associated with poor prognosis . The process of migration involves a combination of cellular functions including those of altered attachment to surrounding contacts ( cells or matrix ) , protrusion of a leading edge , polarisation in the creation or recognition of that leading edge , and mechanical movement [1] . Understanding the details of these processes represents an important objective in defining the collection of candidate targets that may offer new opportunities in restricting disease spread . The atypical PKC isoforms ( aPKCζ and aPKCι ) comprise a branch of the serine/threonine protein kinase PKC superfamily with regulatory properties that distinguish them from the more typical diacylglycerol-regulated isoforms [2] . These kinases can be activated by acidic phospholipids such as the polyphosphoinositides [3] , however specificity appears to be driven by activation through Par6/cdc42 [4] . Indeed , interactions with the Par6/Par3 complex have implicated aPKC isoforms in a number of polarity [5] and more recently migratory models [6] . In migrating astrocytes , the activation of aPKC leads to phosphorylation and inactivation of GSK-3β , which causes the adenomatous polyposis coli ( APC ) tumor suppressor protein to associate with microtubule plus ends at the leading edge [7] . The Par6-PKCζ complex also regulates the spatially localized association of Dlg1 and APC to control cell polarization [8] . PKCζ is required for epidermal growth factor-induced chemotaxis of human breast cancer cells [9] , while PKCι has been shown to promote nicotine-induced migration and invasion of cancer cells via phosphorylation of m- and μ-calpains [10] . The Exocyst was first identified as a complex required for exocytosis in Saccharomyces cerevisiae [11] . In mammals , the Exocyst comprises a complex of eight proteins , which facilitates regulated exocytosis to regions of membrane activity [12] , [13] , [14] , [15] . Recently it has been shown that a Ral-Exocyst pathway is involved in cell migration , with RalB activation leading to Exocyst assembly and recruitment to the leading edge [16] . It is anticipated that the various complexes and pathways involved in polarized migration may be co-regulated/coordinated and that elucidation of these relationships will lead to a more integrated understanding of migratory behaviour . The present study was stimulated by the finding that the scaffold protein Kibra , previously shown to interact with aPKCζ [17] , was a binding partner of the Exocyst ( see below ) . This has led to the specific hypothesis that there is a molecular and functional interaction between the Par/CDC42/PKCζ/ι pathway and the Ral/Exocyst pro-migratory pathways . We demonstrate that indeed there is a mutual dependence of aPKC and Exocyst in their behaviour in migratory cells and this is associated with their mutually dependent regulation of the delivery of signals to the leading edge of migrating cells . We identify key regulatory processes under the control of these local aPKC/Exocyst-dependent signals and go on to demonstrate that the regulation of focal adhesion stability represents a critical migratory output of the aPKC/Exocyst pathway .
A cooperative functional relationship between the Exocyst complex and aPKC in cell migration should be reflected in a shared requirement in a model system . The Exocyst has been shown previously to play an essential role in NRK cell migration . So to determine the requirements for PKCι and PKCζ in NRK cell migration , two independent siRNAs for each protein were employed to knock-down expression ( Figure 1A ) . Depletion of either PKCι or PKCζ resulted in a cell migration defect as assessed in a monolayer wound healing model ( Figure 1B ) as well as in a Transwell migration assay ( unpublished data ) . The speed of NRK cell migration in the wound assay is 15 . 3 µm/h . Depletion of either aPKC reduces this by ∼40% , whilst depletion of both reduces migration further to ∼6 µm/h ( Figure 1C ) . To distinguish between a non-catalytic , scaffold-only requirement for aPKC and a catalytic activity requirement , PKC inhibitors were employed . Selected combinations of these inhibitors can be exploited to provide circumstantial evidence on the requirements of PKC isoforms based upon their relative potency such that an aPKC activity involvement would be sensitive to the pan-PKC inhibitor Gö6983 ( cPKC , nPKC , aPKC ) while showing little sensitivity to BIMI ( cPKC , nPKC ) or Gö6976 ( cPKC ) . Gö6983 had a profound effect on cell migration , while the cPKC inhibitor Gö6976 had no effect ( Figure 1C and 1D ) . BIMI had a weaker effect than the Gö6983 on migration ( Figure 1C and 1D ) . Although these inhibitors are not entirely PKC-specific , in combination with the siRNA data it can be concluded that aPKCs are required for efficient NRK cell migration , providing a model in which to probe an aPKC-Exocyst relationship in migration . To assess a connection between aPKC isoforms and the Exocyst , we sought to determine their distribution in migrating cells . Examination of the location of aPKCs ( combined PKCι and PKCζ ) demonstrated that aPKC was localised at the leading edge of migrating NRK cells ( Figure 2A ) . By contrast , in confluent cells , aPKCs are mainly cytosolic and sometimes partially at cell-cell contacts ( unpublished data ) . A monoclonal antibody specific for PKCι confirmed its presence at the leading edge as well as within a perinuclear compartment; PKCζ specific reagents were found to cross-react with recombinant PKCι precluding PKCζ-specific immunostaining ( Dr . M . Linch and PJP unpublished results ) . The Exocyst ( here visualized by the subunits Sec6 and Exo70 ) is also in part localized at the leading edge of migrating cells and the pattern of aPKC distribution matches that observed for the Exocyst complex . The localisation of aPKC and the Exocyst at the leading edge of migrating cells is not simply a function of membrane ruffling . In subconfluent monolayers extensive membrane ruffling is observed without enrichment of aPKC or the Exocyst ( Figure S1 ) , while the ruffles at the leading edge of migrating cells are enriched with aPKC and the Exocyst . To determine whether the Exocyst was responsible for aPKC accumulation at the leading edge , siRNA to Sec5 was employed [16] . Knock-down of Sec5 ( Figure 2A ) prevented aPKC localisation at the leading edge without disturbing the total amount of aPKC in the cells ( Figure 2B ) . Knock-down of another component of the Exocyst complex , Exo84 , also prevented aPKC localisation at the leading edge ( see below , Figure 3C ) . When one of the components of Exocyst complex ( either Sec5 or Exo84 ) is depleted , more than 50% of the cells have a total absence of PKCι at the leading edge . Consistent with this , migration was inhibited on knock-down of Sec5 and Exo84 by 60% . Reciprocally it was found that siRNA to aPKCs ( Figure 2C and Figure S2A , S2C ) or treatment with the inhibitor Gö6983 ( unpublished data ) suppressed Exo70 ( for more than 60% of the cells ) and Sec6 accumulation at the leading edge . This disruption of localisation is not due to a modification of the protein levels of Exo70 and Sec6 ( Figure 1A ) . The recruitment of aPKC and Exo70 at the leading edge were quantified ( Figure S2A , S2B , S2C ) . We also evaluated the specificity of the staining as well as the effect on the knock-down of the Exocyst ( Sec5 or Exo84 ) on the presence of aPKC at the leading edge and reciprocally the effect on the knock-down of aPKC on the presence of Exo70 at the leading edge ( Figure S2A , S2B ) . Notably , depletion of either PKCι or PKCζ partially inhibited the “tubularisation” of the Sec6 compartment seen in motile cells on methanol fixation ( Figure S3C ) , suggesting that aPKC is required for the localization of Sec6 on microtubules in response to cell migratory cues . Depletion of PKCι or PKCζ or Sec5 did not dramatically affect the stability of the microtubules ( unpublished data ) . To control for the possibility of global disruptive effects of aPKC on vesicle-staining patterns in migrating cells , β-COP proteins were monitored . The depletion of aPKC modified the localization of Sec6 but not β-COP , indicating the specificity of the aPKC effect ( Figure S3C ) . The Exocyst associates with microtubules [18] , [19] providing a potential basis for Exocyst and aPKC movement . As predicted , depolymerisation of the microtubule network with nocodazole was found to block the accumulation of Exo70 at the leading edge ( Figure 2D ) . Using methanol fixation , Sec6 is detected on tubular structures and these “tubules” of Sec6 are also dependent on the stability of the microtubules ( Figure 2E ) . Because aPKCs were described to control MTOC orientation via the Dynein-Dynactin complex [20] and the recruitment of the Exocyst complex is dependent on the microtubule network , it is possible the effect of the depletion of aPKC on the recruitment of the Exocyst at the front of the cells is due to an indirect effect of aPKC on motor proteins . However , the presence of dominant negative CDC42 , which is involved in aPKC effects on polarity , did not disturb the localization of Exo70 at the leading edge ( Figure S3D ) . As predicted from the observations above , nocodazole treatment also blocks aPKC delivery to the leading edge ( Figure 2F ) . The specificity of nocodazole treatment is reflected in the finding that treatment increases actin stress fibres showing that cells retained microfilament structures ( unpublished data ) . To investigate the basis of the mutual aPKC-Exocyst localisation relationship , assessment was made of the possible association of aPKC and the Exocyst in NRK cells . Antibodies to the Exocyst subunit Sec8 efficiently immunoprecipitated the native complex; probing this immunopurified complex for the presence of aPKC showed that aPKC was also associated . By contrast the related PKCε and PKCδ , which are also expressed in NRK cells , were not recovered in association with the immunopurified Exocyst complex ( Figure 3A ) . Sec8 interacts with both PKCι and PKCζ , based on immunoprecipitation from NRK cells expressing PKCι or a myc tagged PKCζ construct ( Figure S4A and S4B ) . Immunoprecipitation with anti-mycPKCζ or PKCι antibodies did not recover detectable Sec8 in the immunoprecipitate; it would seem that only a subfraction of PKCι or PKCζ is complexed to Sec8 . Direct demonstration of the interaction in cells employing FRET approaches has not proved possible because the GFP-fusions of Exocyst subunits have been found not to enter into mature Exocyst complexes ( unpublished data ) in contrast to the findings in yeast [21] . Given the predicted requirement for activity in the migratory behaviour described above , it was important to determine whether the Exocyst associated aPKC was catalytically active . MBP was selectively phosphorylated in Sec8 immunoprecipitates and this phosphorylation was inhibited by a peptide inhibitor of aPKC ( Figure 3B ) . The Sec8-associated aPKC thus appears to be active . To assess if the interaction between aPKCs and the Exocyst is regulated , we examined activity and migratory requirements . Sec8 was immunoprecipitated following treatment with the inhibitor Gö6983 . As shown in Figure S4C , the physical interaction between Sec8 and aPKC is not modulated in the absence of aPKC activity . To examine if cell migratory cues impact the stability of the interaction between the Exocyst and aPKC , a monolayer of confluent NRK cells was extensively scratched to maximize the number of “free edges” where cell-cell contacts are released . As shown in Figure 3C , cell migration promoted the interaction between aPKC and the Exocyst at 3 h and 6 h . The interaction with the Exocyst increased 1 . 5- to 2-fold ( PKCι or both aPKCs ) 6 h after monolayer wounding ( Figure 3D ) . This increase of interaction during cell migration does not appear to be a non-specific stress response triggered by the multi-scratch assay , since no such influence is exerted by osmotic shock ( unpublished data ) . These results sustain the idea that the function of these proteins requires their interaction during cell migration . The Sec3 subunit of the Exocyst was used as a bait in a two-hybrid screen of a highly complex human placenta cDNA library ( 10 million independent clones ) . A total of 120 million interactions were screened ( 12 times library coverage ) and four clones encoding human Kibra ( NP_056053 ) were identified; Kibra is a known aPKC binding partner [17] , [22] . Kibra's domain of interaction with Sec3 was defined by the smallest identified prey fragment and encompasses amino acids 129–526 . This region has been found only in six screens amongst 935 screens performed against the same cDNA library , indicating that the Sec3-Kibra interaction is highly specific ( Figure 3E ) . In the same screen with Sec3 as a bait , expected partners such as Sec5 ( 12 fragments , 4 different fusions; interacting domain is amino acids 96–252 ) and Sec8 ( 7 fragments , 2 different fusions; interacting domain is amino acids 28–167 ) were identified also . Confirmation of this Exocyst complex has come from independent studies as Kibra was identified as a partner of the Exocyst from an unbiased proteomic analysis of Exocyst-interacting proteins [23] . To determine the retention of this Kibra interaction in the context of the Exocyst complex , we submitted protein extracts from NRK cells expressing Myc-Kibra or Flag-Kibra constructs to immunoprecipitation with anti-Myc or Flag antibody and the immunoprecipitates were analyzed for the presence of Sec8 ( a component of the complex but not Sec3 itself ) . Figure S5A and S5B show that ectopically expressed Myc-Kibra or Flag-Kibra co-immunoprecipitate with Sec8 , whereas the beads alone or a myc antibody used as a negative control precipitated neither . For consistency , we tested if the interaction between Kibra and Exocyst ( Sec8 ) and also between Kibra and aPKC are dependent on cell migration . As shown in Figure 3F , cell migration promoted the interaction between aPKC and Myc-Kibra and also that between Myc-Kibra and Sec8 . To ensure these observations were not a function of ectopic expression , we investigated the behaviour of the endogenous proteins . Endogenous Kibra was found to interact with Sec8 and aPKC , and this complex also increased during cell migration as observed above for the overexpressed Kibra ( Figure 3F and 3G ) . This result sustains the idea that the function of these proteins requires their interaction during cell migration and is entirely consistent with the increased interaction between Sec8-aPKC during cell migration ( Figure 3C ) . Given that the region of interaction between Kibra and Sec3 defined by two-hybrid resides within the amino acid sequence 129–526 and the region of Kibra that binds PKCζ encompasses amino acids 953–996 , there is no apparent conflict for Kibra in binding both PKCζ and Sec3 . To assess whether the endogenous aPKCs are associated with the endogenous Exocyst via Kibra , we submitted protein extracts from scratch wounded NRK cells , depleted or not of endogenous Kibra , to immunoprecipitation with anti-Sec8 . The immunoprecipitates were analyzed for the presence of PKCζ/ι . The interaction between Sec8 and PKCζ/ι decreased on depletion of Kibra , demonstrating that Kibra contributes to complex formation ( Figure 3G and Figure S5D ) . Based upon this requirement for Kibra , it was predicted that Kibra knock-down by siRNA would inhibit migration and this was found to be the case ( Figure 4A , 4B , and 4E ) . The fact that there was consistently only a 25% decrease in cell migration in the absence of Kibra using different knock-down strategies suggests that an alternate protein ( s ) might also participate in the complex between the Exocyst and aPKC . We sought to assess the relevance of the aPKC-Kibra interaction in the context of the localisation of aPKC in migrating cells . As previously shown in podocytes [24] , endogenous Kibra was found to be recruited to the leading edge as well in NRK cells . Endogenous Kibra colocalised with aPKC in an Exocyst dependent manner ( Figure 4D ) . The depletion of aPKC decreased the recruitment of Kibra at the leading edge ( Figure 4C , 4D , and 4E ) consistent with the lack of recruitment of the Exocyst under these conditions ( see above ) . Interestingly , overexpression of Myc-Kibra or Flag-Kibra inhibits NRK cell migration ( Figure 4B ) , consistent with interference in the bivalent interaction observed here . To address this point , examination of the location of aPKCs ( combined PKCι and PKCζ ) demonstrated that aPKC was colocalised at the leading edge of migrating NRK cells when Myc-Kibra is weakly overexpressed but the recruitment of aPKC at the leading edge is disturbed when Myc-Kibra is strongly overexpressed ( Figure S5C ) . Finally , to determine whether Kibra is responsible for aPKC delivery to the leading edge , siRNA to Kibra was employed . Knock-down of Kibra inhibited aPKC localisation at the leading edge ( Figure 4C , 4E ) . The function of the leading edge aPKC-Kibra-Exocyst complex was assessed in relation to the activation of the JNK pathway , since there is evidence both for aPKC involvement in JNK control in other contexts ( e . g . in response to TNF or IL1 [25] , [26] ) , as well as for JNK involvement in migration [27] , [28] , [29] , [30] . In response to wound healing ( multiple scratch wounds ) , JNK1 and JNK2 were activated in a biphasic fashion ( Figure 5A ) . The effect of acute inhibition of aPKC was assessed using Gö6983 and for comparison the non-aPKC directed inhibitors , Gö6976 ( Figure 5A ) and BIMI ( unpublished data ) . The inhibition of atypical , novel , and classical PKCs suppressed the activation of JNK1 without affecting JNK2 . By contrast the PKC inhibitors not directed at the aPKCs , Gö6976 , and BIMI modestly increased JNK1 phosphorylation ( Gö6976 decreases JNK2 activation while BIMI had no such effect , indicative of a non-PKC dependent effect ) . On depletion of JNK1 by siRNA , the P-JNK1 immunoreactivity was also decreased indicating that the doublet identified by western is JNK1 and a splice variant/modified form of JNK1 ( Figure S7D ) . Inhibiting aPKC ( Gö6983 ) , but not non-aPKC family members ( Gö6976 nor BIM1 ) , also affected the activation of ERK1/2 during cell migration ( Figure S4D , S4E ) . None of these PKC inhibitors influenced the p38 pathway response in migrating NRK cells , indicative of specific pathways wherein aPKC activity is required for JNK1 and ERK1/2 activation during migration . The effects of aPKC on JNK1 were reflected in the altered localised activation of JNK1/2 in migrating cells; under control conditions , JNK was found to accumulate in a phosphorylated state at the leading edge and this localised activation was lost on depletion of PKCζ , PKCι , Sec5 , Exo84 , or Kibra ( Figure 5B–5D; see also Figure 6C , Figure S6A ) . Notably the depletion of these proteins did not influence the accumulation of active JNK in the nucleus , consistent with a lack of effect of Gö6983 on global JNK2 activation determined by Western . This Exocyst/Kibra/aPKC-dependence was also observed for the phosphorylation of ERK1/2 at the leading edge of migrating cells ( Figure 5B–5D; see also Figure S6A ) . Quantitation of these responses is detailed in Figure S8 . This confirms that there is a strong decrease of P-JNK and P-ERK at the leading edge without a significant disruption of the recruitment of the total ERK and JNK proteins at the leading edge ( see below ) . It is noted that this demonstrates that this polarised leading edge in the migrating cells is still present even when aPKC and the Exocyst are non-functional . We confirmed the specificity of the phospho-specific antibody against P-ERK1/2 by using the MEK inhibitor U0126 and found that it suppressed the P-ERK staining ( Figure S6B ) . We confirmed the specificity of the phospho-specific antibody against P-JNK1/2 by using a phospho-JNK ( Thr183/Tyr185 ) blocking peptide and found that it suppressed the P-JNK staining ( Figure S7F ) . Also , following depletion of JNK1 by siRNA , the staining of P-JNK at the leading edge decreased ( Figure S7G ) . The controls monitoring the reduced protein expression associated with siRNA treatment is illustrated in Figure S7A and S7B ( it is notable that Exo84 is reduced on Sec5 depletion suggesting that uncomplexed Exo84 may be subject to degradation ) . This aPKC/Exocyst-dependent localised phosphorylation of MAPKinases ( phosphorylated ERK1/2 , as well as phosphorylated JNK ) may be a consequence of their movement to the leading edge or of their activation at the leading edge . To distinguish between these two possibilities , the localisation of the kinases ( phosphorylated and non-phosphorylated ) was assessed in control cells or following aPKC , Kibra , Sec5 , or Exo84 knock-down ( protein depletion on knock-down is illustrated in Figure S7A and S7B ) . No effects on MAPkinase protein distribution were observed ( Figure S6C , S6D , and S6E ) . Figure S6E shows that when the Exocyst/Kibra/aPKC is disrupted , P-ERK1/2 decreased at the leading edge whereas the non-phosphorylated form remained at the leading edge ( note that the P-ERK1/2 staining appears a little different from that in Figure 5B–5D due to methanol fixation instead of paraformaldehyde fixation; the methanol fixation is better for the total ERK1/2 whereas the paraformaldehyde gave better staining for the P-ERK1/2 ) . This result showed that cells retain a leading edge allowing the recruitment/retention of JNK and ERK proteins independent of the Exocyst and aPKC . Hence it is the localised activation that is critically under aPKC-Kibra-Exocyst control . Analysis of the relevant upstream kinase ( s ) in the JNK pathway identified MKK4 and not MKK7 as showing increased phosphorylation after wounding ( Figure 5A ) . Linking to aPKC function , wound-associated activation of MKK4 during cell migration is sensitive to the a/n/cPKC inhibitor Gö6983 ( Figure 5A ) ; Gö6976 ( n/cPKC inhibitor ) has no such effect ( in fact it has the opposite effect to Gö6983 , increasing the phosphorylation of MKK4 ) . The constitutive phosphorylation of MKK7 , the other JNK-kinase , was insensitive to Gö6983 ( unpublished data ) . The above evidence is indicative of a localised MKK4-dependent activation of JNK1 during cell migration , requiring the localised action of the aPKC-Kibra-Exocyst complex . To assess the specificity of these plasma membrane effects in relation to other compartments , we compared the aPKC-dependency of the phosphorylation of nuclear ( c-Jun ) and plasma membrane ( Paxillin ) both substrates for JNK [31] . The aPKC inhibitor Gö6983 blocked the phosphorylation of Paxillin on serine 178 but not of c-jun serine 63 ( Figure 6A ) . Furthermore , siRNA directed against aPKCs or Sec5 does not decrease phospho-cJun in the nucleus ( Figure S6E ) . The lack of effect on Jun phosphorylation is consistent with the fact that phospho-JNK in the nucleus is not modified after depletion of aPKCs ( see Figure 5B ) . Demonstrating that these events are integral to the observed migratory response , it was shown that the depletion of ERK2 but not ERK1 , depletion of JNK1 but not JNK2 , and depletion of MKK4 ( and also MKK7 ) significantly decreased cell migration ( Figure 5E and 5F ) as did the JNK inhibitor , SP600125 ( Figure S7H and S7I; the controls for depletion are included in Figure S7C and S7F ) . To address the effect of the absence of aPKC and Exocyst on Paxillin in migrating NRK cells , cells were stained for Paxillin and Actin . More Paxillin patches at focal adhesion complexes and also more actin stress fibres appeared in the absence of aPKC and the Exocyst ( Figure 6B; quantified in Figure S9 ) . Figure 6C shows that P-JNK colocalised with Paxillin and if the aPKC/Exocyst pathway is disrupted , P-JNK at the leading edge is abolished whereas there is an increase of Paxillin patches . This increase of Paxillin patches was mirrored with siRNAs against ERK2 and JNK1 consistent with the requirement for Exocyst/aPKC in the localised activation of ERK1/2 and JNK1 and the consequent distribution of Paxillin in focal adhesion complexes as opposed to the more static focal adhesion complexes . ERK1 , ERK2 , and JNK1 were depleted and the effect of their depletion on Paxillin Patches were quantified . Depletion of ERK2 and JNK1 and not ERK1 elicited an increase of Paxillin patches .
It is established here that aPKC isoforms via the Exocyst complex can confer efficient migration through their ability to control the leading edge activation of a distinct subpopulation of MAPKinases , conferring increased speed on the serum-dependent migratory response of cells . This localised process is enabled through the traffic of an aPKC-Exocyst complex to the leading edge of migrating cells . Assembly of this complex is dependent upon Kibra , which appears to act as a scaffold linking aPKC [17] , [22] with Sec3 through non-overlapping binding domains . Whilst assembly is not dependent upon aPKC activity , it is promoted by migratory conditions and immuno-isolation of the Exocyst-aPKC complex demonstrates that the associated aPKC is catalytically active . The Exocyst complex is required for aPKC accumulation at the leading edge . Reciprocally , the association of the Exocyst with active aPKC correlates with a requirement for aPKC expression and activity for the traffic of the Exocyst to the leading edge of migrating cells . The Exocyst-Kibra-aPKC complex traffics in a microtubule-dependent fashion to the leading edge of migrating NRK cells and this is a necessary event to promote efficient/directed migration . The control of aPKCs on the Exocyst at the leading edge could be explained by control of microtubule dynamics by aPKC as suggested previously [32] . Consistent with this pattern of behaviour , knock-down of Exocyst subunits or aPKC isoforms , or inhibition of aPKC isoforms ( Gö6983 , a pan-PKC inhibitor ) inhibits migration . The migratory model itself requires the presence of serum and matrix interactions for migration . For NRK cells , migration occurs on fibronectin , laminin , and collagen with all three displaying a requirement for aPKC for optimum migration . By contrast in RPE1 cells , migration on fibronectin is aPKC-dependent but on laminin or collagen migration is relatively insensitive to knock-down of aPKC . As evident from the NRK cell model here , which is 60% dependent on aPKC , there are multiple modes of migration that display differential dependence on control mechanisms and these vary between cells . Mechanistically , the aPKC-Exocyst assembly in the NRK cell model confers JNK and ERK activation at the leading edge and furthermore JNK1 ( and not JNK2 ) as well as ERK2 ( and not ERK1 ) inhibition blocks cell migration . JNK's effects appear to be mediated in part through the phosphorylation of Paxillin on serine 178 , determining the dynamics of focal adhesions [31] , [33] . aPKCs also control the phosphorylation of Paxillin by ERK1/2 on serine 126 ( unpublished data ) . These phosphorylations were described to be important for the turnover of Paxillin at the focal adhesions [27] , [31] , [33] , [34] . Indeed , the knock-down of aPKCζ/ι or Sec5 ( or Exo84 ) causes Paxillin accumulation in large , static focal adhesions . Thus we have mapped a pathway from the assembly of the aPKC-Exocyst complex , through their mutual delivery to the leading edge of migrating cells , the activation there of ERK and JNK , and the consequent phosphorylation of Paxillin , influencing the dynamics of focal adhesion turnover and migration . Video microscopy experiments ( Videos S1 and S2 ) are compatible with the regulation of Paxillin dynamics being under aPKC control . Co-immunoprecipitation experiments showed also an interaction between Sec8 and Paxillin in NRK cells . Paxillin was shown recently as a partner of Sec5 [35] . This interaction between Sec8 and Paxillin increased during NRK cell migration ( Figure 6 ) . These data suggest that there is an acute regulation of Paxillin by aPKC . The action of aPKCs in conferring this promigratory behaviour reflects their role in directing the subcellular localisation of signals . Such spatially resolved behaviour of cellular regulators is increasingly recognised as a critical factor in determining the nature of their output . Here the evidence is for the localised action of the JNK pathway ( in particular JNK1 ) in migration . The depletion of JNK1 decreases the P-JNK staining at the leading edge . Only JNK1 and not JNK2 knock-down inhibits cell migration and Gö6983 inhibits only the phosphorylation of JNK1 and not JNK2 during cell migration . These three observations provide compelling evidence that JNK1 is a key player in the JNK pathway responsible for aPKC-dependent NRK cell migration . Activation of JNK1 at the leading edge , effected through the aPKC-Kibra-Exocyst complex , is necessary for the phosphorylation of Paxillin in this compartment , while the migration-associated , JNK-dependent phosphorylation of nuclear c-jun is immune to aPKC-Kibra−-Exocyst function . Conversely it is evident that the activated JNK engaged in c-jun phosphorylation ( probably JNK2 ) is not able to trigger Paxillin phosphorylation at the plasma membrane ( Figure 7 ) . The aPKC-dependent plasma membrane activation of both JNK and ERK is driven by delivery of upstream controls and not through the localisation of the JNK or ERK proteins themselves ( Figure 7 ) . This lack of effect on ERK and JNK recruitment at the leading edge when the Exocyst-aPKCs is disrupted by various siRNAs shows that a leading edge is preserved; aPKC/Exocyst disruption does not disorganise globally the leading edge , and cells still retain oriented protrusions . Moreover , the distribution of the actin cytoskeleton of migrating cells at the wound edge is retained as described by Guo et al . using siRNA against Exo70 [15] . These MAPKinases along with characteristic cortical actin structures retain their leading edge location independent of aPKC-Kibra-Exocyst action and their activation by phosphorylation . It is implicit that the polarised delivery and/or retention of these MAPKinases at the leading edge is dependent on distinct non-aPKC signal ( s ) . The upstream signals required for the activation of JNK appears to involve MKK4 and not MKK7 since the former shows sensitivity to inhibition of aPKC ( pan-PKC ) in its migration-induced activation , while the latter is insensitive . Although the depletion of MKK4 inhibits cell migration consistent with the proposed role for MKK4 , so does the depletion of MKK7 , preventing distinction to be made between these two JNK kinases . However , it is notable that the morphology of cells depleted of MKK4 ( but not of MKK7 ) is a phenocopy of the cells depleted of aPKC ( bigger , more spread cells ) , consistent with the selective effects of aPKC inhibition on MKK4; cells depleted of MKK7 displayed a stressed appearance after wounding that was quite distinct from the morphological phenotype of MKK4 and aPKC knock-down cells . It is concluded that MKK7 , though not activated during migration , is probably required for migration in an aPKC independent pathway . PKCι can control JNK via Par6 and Rac [36] , however the control exerted during cell migration via MEK4 remains under investigation . HGK , a MAPKKKKinase specific for the JNK pathway , was described to interact with the Exocyst complex [37] . So one possibility is that aPKCs could control the interaction of HGK with Exocyst complex . Although the Exocyst has been associated with secretory events , the upstream trigger for activation is not thought to be dependent upon any factor ( s ) secreted following movement of the Exocyst to the leading edge , since conditioned medium from wounded cells does not rescue wounded cells where the Exocyst subunit Sec5 has been knocked down ( Figure S3E , S3F ) . It would appear that the trigger for activation derives from changes in cell-cell/matrix interactions triggered by removal of cells from the monolayer ( wounding ) . In conclusion , aPKCs via the Exocyst complex and Kibra are shown to exert a pro-migratory role in NRK cells and do so through the regulated delivery of a signal to the leading edge of migrating cells through kinases upstream of JNK1 and ERK1/2 . This regulation of the ERK and JNK pathways via aPKCs allows the phosphorylation of a common substrate , Paxillin ( Figure 7 ) , and consequently probably the turnover of Paxillin at the focal adhesion sites . The combined actions of this complex thus integrate the polarised , leading edge delivery of signals required for efficient migration .
Normal rat kidney ( here denoted NRK , but specifically NRK-49F cells , confirmed by ß-catenin staining [38]; Figure S3A ) cells were cultivated in Dulbecco's modified Eagle medium and 10% fetal calf serum under 5% CO2 on Falcon plastic dishes . Wounds were inflicted by scratching the cell surface with a plastic pipette tip . Images were recorded using a Zeiss microscope and an Orca ER CCD camera ( Hamamatsu ) . All inhibitor treatments were performed without pre-incubation . Quantification of the speed of individual cells was performed using Metamorph , Tracker , and Mathematica software . Cells were fixed in 4% paraformaldehyde ( unless stated otherwise ) , permeabilized in 1% Triton X-100 , and mounted using Prolong ( Molecular Probes ) . Primary antibodies were obtained from Stressgen ( Sec6 ) , BD Bioscience ( Sec8 ) , Cell Signaling ( phospho-extracellular signal-regulated kinase 1/2; P-ERK1/2 , P-JNK1/2 , P-cJun ) , or Sigma ( fluorescein isothiocyanate ( FITC ) -coupled anti-tubulin ) . The antibody anti-Exo70 was generously provided by Dr . S . C . Hsu ( Department of Cell Biology and Neuroscience , Rutgers University , Piscataway , New Jersey , USA ) . Secondary antibodies were from Jackson Laboratories and Molecular Probes and were coupled to Cy3 and FITC , respectively . Images were acquired using a confocal laser scanning microscope ( LSM510 , Carl Zeiss Inc . ) equipped with a 63×/1 . 4 Plan-Apochromat oil immersion objective . Alexa 488 was excited with the 488-nm line of an argon laser , Cy3 was excited with a 543-nm HeNe laser , and Cy5 was excited with a 633-nm HeNe laser . Each image represents a projection or a single section as indicated in the Figure legend . For experiments involving protein recruitment at the leading edge , at least 70 cells at the wound were counted per experiment according the classification shown in Figure S2 . Each figure shows the quantified recruitment of the specified protein at the leading edge from at least three independent experiments . Where Paxillin spots and Actin stress fibers were quantified , an Array Scan II and the Cellomics analysis program were employed . Each cell was identified by nuclear staining ( Dapi ) and actin staining ( phalloidin ) . Each area of interest for the analysis ( spot or fiber ) was delimited and represented as an object . The number of Paxillin spots as well as the total area of stress fibers per cell were measured ( Figure S10B ) . For whole-cell extracts , cells were lysed directly on plates in hot Laemmli sample buffer . For immunoprecipitation , cells were lysed in 20 mM Tris-HCl ( pH 7 . 4 ) , 100 mM NaCl , 1 mM MgCl2 , 0 . 1 mM dithiothreitol , 1% Triton X-100 , and 10% glycerol; antibodies were used at the concentrations recommended by suppliers for immunoprecipitation or for immunodetection on membranes . Proteins were visualized on membranes with a chemiluminescent detection system ( ECL; Amersham ) . Quantitation of the immunoprecipitates were performed using Image J . Primary antibodies were obtained from Santa Cruz ( PKCζ/ι , JNK1/2 , Paxillin ) , BD Biosciences ( PKCι and rSec8 ) , UBI ( ERK1/2 ) , cell signaling ( P-MKK4 , MKK4 , P-MKK7 , MKK7 , P-JNK1/2 , P-ERK1/2 ) , or Calbiochem ( Phospho-Paxillin [ser178] ) . The Phospho-SAPK/JNK ( Thr183/Tyr185 ) Blocking Peptide was obtained from Cell Signaling . The different inhibitors ( Gö6976 , Gö6983 , BIM-I , SP600125 , and UO126 ) were obtained from Calbiochem . For “scratch tests , ” NRK cells were brought to confluence and scratched orthogonally at least 20 times with a p20-200 yellow tip . Cells were either further incubated for 3 h or harvested immediately . Preparations of cell extracts and co-immunoprecipitation were performed according to published procedures [16] . For detection of the activation of ERK1/2 , JNK1/2 , c-Jun , and MKK4 , cells were scratched , allowed to migrate for different times ( as indicated ) , and then harvested in Laemmli sample buffer , boiled , and processed for Western blotting . For direct comparison all cells were maintained in contact with the inhibitors for a 3 or 6 h period as indicated in the text or figure legends . Full-length human Sec3 was cloned into pB27 , derived from the original pBTM116 plasmid [39] , and used as bait to screen a random-primed human placenta cDNA library constructed in pP6 [40] . A total of 120 million clones ( 12-fold library coverage ) were screened using a mating approach with L40ΔGal4 ( mata ) and Y187 ( matα ) yeast strains [41] . His+ colonies were selected on medium lacking tryptophan , leucine , and histidine , supplemented with 2mM 3-aminotriazole to reduce bait autoactivation . Prey fragments of the positive clones were amplified by PCR and sequenced at their 5′ and 3′ junctions on a PE3700 Sequencer . The resulting sequences were used to identify the corresponding interactors in the GenBank database ( NCBI ) using a fully automated procedure . siRNAs were transfected at 10 nM with Hyperfect ( Qiagen ) according to the recommendations of the manufacturer . siRNA were ordered from Dharmacon ( rPKCζ-1 ) or Proligo ( the others ) . Target sequences were: GCAAGCUGCUUGUCCAUAAdTdT ( rPKCζ-1 ) , GCAAACUGCUGGUUCAUAAdTdT ( rPKCι-1 ) , GAAGAAAGAGCUCGUCAAUdTdT ( rPKCι-2 ) , GCAGUGAGGUUCGAGAUAUdTdT ( rPKCι-3 ) , GAACGAUGGUGUAGACC U UdTdT ( rPKCζ-2 ) . Sequences for Sec5 were described previously [16] . The sequence for Exo84 is UGGGCAUGUUCGUGGAUGCdTdT . A smart-pool untargeted plus from Dharmacon was used to deplete rat Kibra and independent siRNAs against rKibra AGGAGAUCUACCAGGUGAAdTdT ( Kibra-330 ) , AGCACGACUACAGUUCAAdTdT ( rKibra-1 ) , and CCACUCACCUUUGCUGACUdTdT ( rKibra-2 ) were also employed . Myc-PKCζ and PKCι constructs were described previously [42] . Myc-Kibra and Flag-Kibra constructs were described previously [17] , [22] . The siRNA for the JNK and ERK pathways were obtained from Qiagen; siJNK1 ( SI03083185 and SI03105802 ) and a smart pool from Dharmacon was also used to deplete rat JNK1 , siJNK2 ( SI01906310 and SI02723588 ) , siERK1 ( SI01300593 and SI01906163 ) , siERK2 ( SI02672117 and SI02692326 ) , siMKK4 ( SI01533791 and SI01533798 ) , and siMKK7 ( SI04404680 and SI04404687 ) .
|
Cell migration is an essential process in multicellular organisms during such events as embryonic development , the immune response , and wound healing . Cell migration is also instrumental in the development of pathologies such as cancer cell invasion of healthy tissues . To make cells move , key molecules must be engaged in a coordinated manner; understanding which molecules , and how and when they work ( for example , under physiological versus pathological conditions ) will impact on new therapies designed to suppress abnormal migration . Migrating cells must coordinate two key processes: extension of the front or ‘leading’ edge of the cell and retraction of the back edge . Both processes require the turnover of protein assemblies known as focal adhesion complexes . In this paper we show that two different groups of regulators of migration – aPKC , a protein kinase , and exocyst , a complex of proteins also known to be required for exocytosis – interact physically via the scaffold protein kibra . All these components are required for efficient cell migration and all are enriched at the leading edge of moving cells , in a mutually dependent manner . At the leading edge , these components control the local activation of two additional protein kinases , ERK and JNK . The activation of ERK and JNK at the front of migrating cells in turn controls the phosphorylation of paxillin , a component of focal adhesions . Phosphorylation of paxillin is associated with the presence of more dynamic focal adhesions . Our data thus indicate that an aPKC-kibra-exocyst complex plays a crucial role in delivering local stimulatory signals to the leading edge of migrating cells .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"Methods"
] |
[
"cell",
"biology/membranes",
"and",
"sorting",
"cell",
"biology/cell",
"signaling"
] |
2009
|
An aPKC-Exocyst Complex Controls Paxillin Phosphorylation and Migration through Localised JNK1 Activation
|
Herpes simplex virus 1 ( HSV-1 ) latency establishment is tightly controlled by promyelocytic leukemia ( PML ) nuclear bodies ( NBs ) ( or ND10 ) , although their exact contribution is still elusive . A hallmark of HSV-1 latency is the interaction between latent viral genomes and PML NBs , leading to the formation of viral DNA-containing PML NBs ( vDCP NBs ) , and the complete silencing of HSV-1 . Using a replication-defective HSV-1-infected human primary fibroblast model reproducing the formation of vDCP NBs , combined with an immuno-FISH approach developed to detect latent/quiescent HSV-1 , we show that vDCP NBs contain both histone H3 . 3 and its chaperone complexes , i . e . , DAXX/ATRX and HIRA complex ( HIRA , UBN1 , CABIN1 , and ASF1a ) . HIRA also co-localizes with vDCP NBs present in trigeminal ganglia ( TG ) neurons from HSV-1-infected wild type mice . ChIP and Re-ChIP show that vDCP NBs-associated latent/quiescent viral genomes are chromatinized almost exclusively with H3 . 3 modified on its lysine ( K ) 9 by trimethylation , consistent with an interaction of the H3 . 3 chaperones with multiple viral loci and with the transcriptional silencing of HSV-1 . Only simultaneous inactivation of both H3 . 3 chaperone complexes has a significant impact on the deposition of H3 . 3 on viral genomes , suggesting a compensation mechanism . In contrast , the sole depletion of PML significantly impacts the chromatinization of the latent/quiescent viral genomes with H3 . 3 without any overall replacement with H3 . 1 . vDCP NBs-associated HSV-1 genomes are not definitively silenced since the destabilization of vDCP NBs by ICP0 , which is essential for HSV-1 reactivation in vivo , allows the recovery of a transcriptional lytic program and the replication of viral genomes . Consequently , the present study demonstrates a specific chromatin regulation of vDCP NBs-associated latent/quiescent HSV-1 through an H3 . 3-dependent HSV-1 chromatinization involving the two H3 . 3 chaperones DAXX/ATRX and HIRA complexes . Additionally , the study reveals that PML NBs are major actors in latent/quiescent HSV-1 H3 . 3 chromatinization through a PML NB/histone H3 . 3/H3 . 3 chaperone axis .
Herpes simplex virus 1 ( HSV-1 ) is a human pathogen with neurotropic tropism and the causal agent of cold sores and more severe CNS pathologies such as encephalitis [1] . After the initial infection , HSV-1 remains latent in neuronal ganglia with the main site of latency being the trigeminal ( or Gasserian ) ganglion ( TG ) . Two transcriptional programs are associated with HSV-1 infection , the lytic cycle and latency , which differ by the number and degree of viral gene transcription . The lytic cycle results from the sequential transcription of all viral genes ( approximately 80 ) and leads to the production of viral progeny . The latency phase , occurring exclusively in neurons , is limited to the abundant expression of the so-called Latency Associated Transcripts ( LATs ) , although physiologically a transitory expression of a limited number of lytic genes is not excluded , making latency a dynamic process[2–4] . Following lytic infection of epithelial cells at the periphery , the viral particle enters the axon termini of the innervating neurons by fusion of its envelope with the plasma membrane . The nucleocapsid is then carried into the neuron body by retrograde transport , most likely through the interaction of viral capsid components [5] with microtubule-associated proteins such as dynein and dynactin [6–10] . Once the nucleocapsid reaches the cell body , the virus phenotype changes from the one at the axon termini because most of the outer tegument proteins , including VP16 , a viral transactivator that is essential for the onset of lytic infection , remain at the axonal tip [11–13] . Hence , when the viral DNA is injected into the neuron nucleus , it does not automatically benefit from the presence of VP16 to initiate transcription of lytic genes . Rather , the balance between lytic and latent transcriptional programs most likely depends on stochastic events and on undescribed neuron-associated factor ( s ) able to initiate the transcription of VP16 through the activation of neuro-specific sequences present in the VP16 promoter [14] . Without VP16 synthesis , transcription of the viral genes encoding ICP4 ( the major transactivator protein ) and ICP0 ( a positive regulator of viral and cellular gene transcription ) is hampered . Hence , ICP4 and ICP0 gene transcription is unlikely to reach the required level to produce these two proteins above a threshold that would favor onset of the lytic cycle . Therefore , in neurons , commitment of the infectious process towards the lytic cycle or latency depends on a race between opposing infection-prone viral components and cellular features with antiviral activities . Promyelocytic leukemia ( PML ) nuclear bodies ( NBs ) ( also called ND10 ) are proteinaceous entities involved in the control of viral infection as part of the cell and nucleus-associated intrinsic antiviral response but also through innate immunity associated with the interferon ( IFN ) response [15] . Our recent studies have shown that PML NBs tightly associate with incoming HSV-1 genomes in the nucleus of infected TG neurons in mouse models and in primary TG neuron cultures [16 , 17] . Hence , PML NBs reorganize in structures called viral DNA-containing PML NBs ( vDCP NBs ) , which are formed at early times during the process of HSV-1 latency establishment and persist during latency per se in a large subset of latently infected neurons in a mouse model of infection [16] . The entrapment of incoming wild type HSV-1 genomes by PML NBs is not a unique feature of latency , because it has recently been shown to occur prior to the onset of lytic infection , as part of the intrinsic antiviral response . HSV-1 genomes trapped in the vDCP NBs are transcriptionally repressed for LATs production [16] . It is known that HSV-1 latency , at least in the mouse model and possibly in humans , is heterogeneous at the single neuron level for the expression of LATs [16 , 18–25] . Therefore , although at the entire TG level HSV-1 latency could be a dynamic process from a transcriptional perspective , at the single neuron level , a strict , transcriptionally silent , quiescence can be observed , and vDCP NB-containing neurons are major contributors of this latent/quiescent HSV-1 state . In humans , vDCP NB-like structures have also been observed in latently infected TG neurons [17] , suggesting that vDCP NBs are probably molecular hallmarks of the HSV-1 latency process , including in the natural host . Another essential feature of HSV-1 latency is the chromatinization of its 150-kb genome , which enters the nucleus of the infected cells as a naked/non-nucleosomal dsDNA [26–28] . Once the viral genome is injected into the nucleus of the infected neuron , it circularizes , associates with nucleosomes to become chromatinized , and remains as an episome that is unintegrated into the host cell genome [29] . Although latent viral genomes sustain chromatin regulation , essentially through post-translational modifications of associated histones [30–34] not much is known about the mechanisms that induce their chromatinization and which specific histone variants are associated with these latent genomes . In mammals , specific H3 histone variants that differ by a few amino acid residues can influence chromatin compaction and transcriptional activity of the genome . The histone variant H3 . 3 , a specific variant of the histone H3 that is expressed throughout the cell cycle , is deposited in a replication-independent manner , in contrast to H3 . 1 ( [35] and for review [36] ) . Interestingly , death domain associated protein 6 ( DAXX ) and α-thalassemia mental retardation X-linked protein ( ATRX ) , initially identified as a transcriptional repressor and a chromatin remodeler , respectively , are constitutively present in PML NBs , and have now been identified as H3 . 3-specific histone chaperones [37–39] . The other histone H3 . 3 specific chaperone complex is called the HIRA complex , which is composed of Histone cell cycle regulator ( HIRA ) , Ubinuclein 1 ( UBN1 ) , Calcineurin-binding protein 1 ( CABIN1 ) , and Anti-silencing function protein 1 homolog A ( ASF1a ) [35] . The HIRA complex does not normally accumulate in PML NBs except upon entry of the cell into senescence [40 , 41] . The histone variant H3 . 3 itself localizes in PML NBs in proliferating and senescent cells , linking PML NBs with the chromatin assembly pathway independently of replication [42–44] . Because vDCP NBs contain DAXX and ATRX [16 , 17 , 45] , their involvement in the chromatinization of incoming HSV-1 genomes and/or long-term maintenance of chromatinized HSV-1 genomes is thus plausible . Human primary fibroblasts or adult mouse primary TG neuron cultures infected through their cell body with a replication-defective HSV-1 virus , in1374 , which is unable to synthesize functional ICP4 and ICP0 under specific temperature conditions , enable the establishment of a latent/quiescent state for HSV-1 [17 , 45–47] . The latent/quiescent state of HSV-1 in human primary fibroblasts has also been reproduced using engineered HSV-1 unable to express major immediate early genes [48 , 49] . We have shown that this latent/quiescent state is linked to the formation of vDCP NBs , mimicking , at least concerning this particular structural aspect , the latency observed in a subset of neurons in mouse models and in humans [16 , 17] . Here , using the in1374-based in cellula model of infection , we showed that vDCP NBs contained not only the DAXX and ATRX proteins but also all the components of the HIRA complex and H3 . 3 itself . HIRA was also found to co-localize with vDCP NBs in neurons of TG harvested from HSV-1 wild type infected mice . Both DAXX/ATRX and HIRA complex components were found to interact with multiple viral loci by chromatin immunoprecipitation ( ChIP ) . Using the same approaches , we showed that latent/quiescent viral genomes were almost exclusively chromatinized with H3 . 3 , itself modified on its lysine ( K ) 9 by trimethylation ( H3 . 3K9me3 ) . Most interestingly , we found that H3 . 3 chromatinization of the viral genomes was dependent on intact PML NBs , demonstrating that PML NBs contribute to an essential part of the chromatinization of the latent/quiescent HSV-1 genomes . Overall , this study shows that the chromatinization of latent HSV-1 involves a PML NB/histone H3 . 3/histone H3 . 3 chaperone axis that confers and probably maintains chromatin marks on viral genomes .
The formation of vDCP NBs is a molecular hallmark of HSV-1 latency , and vDCP NBs are present in infected neurons from the initial stages of latency establishment to latency per se in mouse models [16 , 17] . Using a previously established in vitro latency system [46] consisting of human primary fibroblast cultures infected with a replication-deficient virus ( hereafter called in1374 ) unable to express functional VP16 , ICP4 and ICP0 , we and others were able to reproduce the formation of vDCP NBs [17 , 45] . We first verified that vDCP NBs induced in BJ and other human primary cells infected with in1374 at a non-permissive temperature of 38 . 5°C , contained , in addition to PML , the proteins constitutively found in the PML NBs , i . e . , Sp100 , DAXX , ATRX , SUMO-1 and SUMO-2/3 ( S1Ai to S1vi Fig , and S1 Table ) . The DAXX/ATRX complex is one of the two chaperones of the histone variant H3 . 3 involved in the replication-independent chromatinization of specific , mostly heterochromatic , genome loci [39] . Interestingly , HSV-1 enters the nucleus of the infected cell as a naked/non-nucleosomal dsDNA and remains during latency as a circular chromatinized episome unintegrated in the host genome [29 , 50] . It is thus tempting to speculate that the presence of DAXX/ATRX in the vDCP NBs could be linked to a process of initiation and/or maintenance of chromatinization of the latent/quiescent viral genome . The other H3 . 3 chaperone is known as the HIRA complex and was initially described as specific for the replication-independent chromatinization of euchromatin regions [35 , 51] . Remarkably , proteins of the HIRA complex are able to bind in a sequence-independent manner to a naked/non-nucleosomal DNA [52] , suggesting that the HIRA complex could also participate in the recognition and chromatinization of the incoming naked HSV-1 genome . We thus investigated the localization of all members of the HIRA complex and found that they co-localized with the latent/quiescent HSV-1 genomes at 2 days post-infection ( dpi ) in BJ and other human primary cells ( Fig 1Ai to 1iv , S1 Table ) . To confirm that the co-localization of members of the HIRA complex with the latent/quiescent HSV-1 could be reproduced in neuronal cells , adult mouse TG neuron cultures were infected with in1374 for 2 days before performing immuno-FISH . Mouse Hira , which was the only protein of the HIRA complex detectable in mouse cells , showed a clear co-localization with a subset of viral genomes ( Fig 1B ) . To analyze whether this co-localization was also reproducible in vivo , immuno-FISH was performed on TG samples from HSV-1-infected mice . Hira was found to co-localize with HSV-1 genomes with the “multiple acute”/vDCP NB pattern ( see [17 , 53 , 54] ) in TG neurons from infected mice at 6 dpi ( Fig 1C ) but not with the “single”/vDCP NB pattern ( see [16 , 53 , 54] ) at 28 dpi ( Fig 1D ) , suggesting a dynamic association of this protein with the vDCP NBs . To analyze this dynamic association , co-localization between incoming HSV-1 genomes and proteins of the PML NBs or of the HIRA complex was quantified at early times from 30 min pi to 6 hpi using a synchronized infection procedure ( Fig 1E and S2 Table ) . Except for the proteins of the HIRA complex , the percentages of co-localization increased with time . Interestingly , at 30 min pi , the percentage of co-localization of HSV-1 genomes with HIRA was significantly higher than with PML ( 41±7% vs 23±5% , p value = 0 . 03 , Student’s t-test , S2 Table ) . Although DAXX and ATRX also showed , on average , a greater percentage of co-localization with HSV-1 genomes ( 36±7% and 34±5% at 30 min , respectively ) compared with PML , the data were not significant ( S2 Table ) . Moreover , a recent study showed the interaction of at least PML , SUMO-2 , and Sp100 with incoming HSV-1 genomes as soon as 1 hpi , which supports our data [55] . The absence of co-localization of mouse Hira with viral genomes with the “single”/vDCP NB pattern in mouse TG neurons at 28 dpi suggested that longer infection times could lead to loss of proteins of the HIRA complex from the vDCP NBs . Infection of BJ cells were reiterated as above , but this time quantifications were performed from 24 hpi to 7 dpi . Strikingly , whereas all the proteins permanently present in the PML NBs remained co-localized with a maximum of 100% of the latent/quiescent HSV-1 genome from 2 dpi until 7 dpi , proteins of the HIRA complex peaked at 2 dpi , and then their co-localization decreased at longer times pi , confirming the temporary association of the HIRA complex with the vDCP NBs ( Fig 1F , and S3 Table ) . To definitively show that proteins of the HIRA complex were present in vDCP NBs , immuno-FISH were performed on BJ cells infected for 2 days with in1374 to detect a member of the HIRA complex , HSV-1 genomes , and PML . Strikingly , while proteins of the HIRA complex showed predominant nucleoplasmic staining in non-infected cells ( Fig 2i , 2iii , 2v and 2vii ) , in infected cells all the proteins clearly and systematically accumulated in PML NBs ( Fig 2ii , 2iv , 2vi and 2viii ) . The accumulation of HIRA in PML NBs following infection by HSV-1 has recently been suggested to be part of an interferon-induced antiviral mechanism [56] . Consequently , HIRA , UBN1 , CABIN1 and ASF1a co-localized with the latent/quiescent HSV-1 genomes in vDCP NBs ( arrows in Fig 2ii , 2iv , 2vi and 2viii ) . Altogether , these data show that both DAXX/ATRX and HIRA complexes are present within vDCP NBs in neuronal and non-neuronal cells , suggesting a role for these two complexes in latent/quiescent HSV-1 chromatinization . The co-localization of proteins of the DAXX/ATRX and HIRA complexes with the incoming HSV-1 genomes and their presence in the vDCP NBs suggested an interaction of these proteins with the viral genome , as shown recently for HIRA on a small subset of viral loci [56] . Since DAXX , HIRA , and UBN1 antibodies were not efficient in the ChIP experiments , we constructed cell lines stably expressing myc-DAXX , HIRA-HA , or HA-UBN1 by transduction of BJ cells with lentiviral- vectors ( S2 Fig ) . Cells were infected with in1374 at 38 . 5°C and harvested 24 hpi to perform ChIP-qPCR on multiple loci spread over the entire HSV-1 genome , representing promoter or core regions ( CDS ) of genes of all kinetics ( IE/α , E/β , L/γ ) ( Fig 3A ) . Cellular glyceraldehyde 3-phosphate dehydrogenase ( GAPDH ) locus was used as a positive control for enrichment . Significant enrichments compared to controls were detected for all proteins on several viral loci independently of their promoter or CDS status , and with no obvious discrepancy regarding the gene kinetic , confirming the potential interaction of these proteins all along the latent/quiescent HSV-1 genomes . Our immuno-FISH data anticipated a gradual interaction of the four proteins with the incoming viral genomes at early times post infection ( see Fig 1E ) . To verify if this could be measured , ChIP-qPCR were performed at 30 min pi , 2 hpi and 6 hpi , using the same experimental conditions as for the immuno-FISH at early times pi ( with synchronization of the infection , see Materials and Methods ) . The data showed a tendency for a weak interaction with the viral genomes at 30 min pi then an increase at 2 hpi and 6 hpi , although with a lot of variability , probably highlighting the dynamic of the biological events occurring during the initial stages of the infection process ( S3B Fig ) . ATRX showed the more regular increase in its interaction with viral genomes from 30 min to 24 hpi . Overall , the ChIP data correlate with the immuno-FISH , and suggest a dynamic process for the interaction between HSV-1 genomes and proteins of the DAXX/ATRX and HIRA complexes , initiating early after the viral DNA enters the nucleus , and remaining at later times when vDCP NBs are structured . The co-localization of the two histone H3 . 3 chaperone complexes with viral genomes suggested the chromatinization of HSV-1 latent/quiescent genomes with the histone variant H3 . 3 . Histones H3 . 1 and H3 . 3 differ by only 5 amino acids , and , in our hands , no suitable antibody is available that can distinguish both histones by IF or IF-FISH . We thus constructed lentivirus-transduced BJ cell lines expressing a tagged version of either histone ( e-H3 . 1 and e-H3 . 3 ) ( see Materials and Methods , and [43] , S4A and S4B Fig ) . We confirmed that ectopic expression of e-H3 . 3 led to its accumulation in PML NBs unlike e-H3 . 1 ( S4C Fig ) [42 , 43] . In1374 infection of BJ e-H3 . 1/3-expressing cells led to the co-localization of viral genomes almost exclusively with e-H3 . 3 ( Fig 4Ai , 4ii and 4B ) . Importantly , e-H3 . 3 co-localized with HSV-1 genomes together with PML in vDCP NBs ( Fig 4C ) . The lack of co-localization of viral genomes with e-H3 . 1 was in agreement with the absence of any of the H3 . 1 CAF-1 chaperone subunits ( p150 , p60 , p48 ) in the vDCP NBs ( Fig 4D , S1 Table ) . To confirm that e-H3 . 3 , unlike e-H3 . 1 , interacted with HSV-1 genomes , ChIP-qPCR were conducted on the same loci as those analyzed above . As expected , e-H3 . 3 , but not e-H3 . 1 , was highly enriched on the viral genome independently of the examined locus ( Fig 4E ) . Several cellular loci were analyzed as controls for specific enrichments with H3 . 3 ( Enhancer 1 ( Enh . 1 ) on chromosome 9 , [57] ) , or H3 . 1 ( leucine-zipper-like transcriptional regulator 1 ( LZTR1 ) on chromosome 22 , GEO accession number GSM1135044 ) . Similar data were obtained for all other canonical histones ( S5 Fig ) , confirming that H3 . 3 association with latent/quiescent HSV-1 genomes is in a nucleosomal context . To confirm that the discrepancy between the binding of e-H3 . 3 and e-H3 . 1 to viral genomes was not due to the ectopic expression of histones , we performed similar experiments using antibodies against native proteins . One specific antibody for H3 . 3 , and suitable for ChIP experiments has previously been described [58] . We performed ChIP using antibodies against native H3 . 1/2 or H3 . 3 in normal BJ cells infected for 24 h by in1374 . The results were similar to those obtained in infected BJ e-H3 . 3 using the anti-HA antibody ( S6 Fig ) . These data confirmed that no bias was introduced in the ChIP experiments due to the use of tagged histones , and that latent/quiescent HSV-1 genomes are chromatinized with H3 . 3 . The gradual interaction of the four proteins of the H3 . 3 chaperone complexes with the incoming viral genomes anticipated similar changes in the interaction of H3 . 3 . ChIP-qPCR were performed at 30 min pi , 2 hpi and 6 hpi , using the same experimental conditions as above . The data showed an overall weak or lack of , H3 . 3 association with the viral genomes at 30 min pi , followed by an increased interaction at 2 hpi and 6 hpi . These data show that the H3 . 3 chromatinization of the incoming HSV-1 genomes is progressive and follows a kinetic that matches that observed with the proteins of the H3 . 3 chaperone complexes . The data also fit with recently published data showing the interaction of incoming viral genomes with canonical histones by 2 hpi [55] . Both constitutive ( H3K9me2 , H3K9me3 ) and facultative ( H3K27me3 ) heterochromatin marks have been found on various loci on latent HSV-1 genomes in vivo [31 , 33 , 34] . To analyze the association of these marks with vDCP NBs-associated latent/quiescent HSV-1 genomes , ChIP were performed targeting H3K9me3 , H3K27me3 and one euchromatic mark H3K4me2 as a control ( Fig 5 ) . HSV-1 genomes were exclusively associated with H3K9me3 ( Fig 5A ) , matching previous results obtained using quiescent viruses [59 , 60] . In contrast H3K27me3 ( Fig 5B ) or H3K4me2 ( Fig 5C ) marks were not detected . Cellular genes previously described for their association with either marks were analyzed for the specificity of the antibodies used ( Zinc-finger protein 554 ( ZNF554 ) /H3K9me3 [61] ) ; myelin transcription factor 1 ( MYT1 ) /H3K27me3 ( [62]; Actin/H3K4me2 ) . To confirm that the K9me3 modification is present on H3 . 3 associated with the HSV-1 genomes , Re-ChIP was performed targeting first H3K9me3 then e-H3 . 3 in infected BJ and BJ e-H3 . 3 ( Fig 5D ) . An overall enrichment for H3 . 3 from samples initially ChIPed with the H3K9me3 antibody was detected only in BJ e-H3 . 3 and not BJ cells , with 17 viral loci over 31 ( 55% ) showing significant enrichment . The cellular locus , family with sequence similarity 19 member A2 ( FAM19A2 ) specifically enriched with H3 . 3K9me3 ( GEO accession numbers: GSM1358809 ( H3 . 3 ) , and GSM1289412 ( H3K9me3 ) ) was used as positive control . These data show that ( i ) the Re-ChIP experiment is specific of e-H3 . 3 and ( ii ) H3 . 3K9me3 is indeed associated with the vDCP NB-associated HSV-1 latent/quiescent genomes . To analyze the requirement of the histone H3 . 3 chaperones for the formation of the vDCP NBs and HSV-1 chromatinization , DAXX , ATRX , HIRA or UBN1 were depleted by shRNAs in normal BJ cells or cells constitutively expressing e-H3 . 3 prior to infection with in1374 and completion of the experiments . The two tested shRNAs for each protein significantly diminished mRNA and protein quantities in BJ cells ( S7A and S7B Fig ) . None of the shRNA impacted the detection of PML NBs , suggesting that PML NBs were potentially functional when the proteins were individually inactivated ( S8 Fig ) . We first measured the impact of the depletion of each protein on the co-localization of HSV-1 genomes with PML . Both shRNAs for each protein gave similar results , i . e . , a significant decrease in the co-localization between HSV-1 genomes and PML and thus a decrease in the formation and/or stability of the vDCP NBs ( Fig 6A and 6B , S4 Table ) . These data show that the inactivation of any of the H3 . 3 chaperone complex affects to a certain extent the fate of vDCP NBs suggesting a connection between the activity of each H3 . 3 chaperone complex and the formation and/or maintenance of the vDCP NBs . We then analyzed the potential impact of the loss of vDCP NB stability on the H3 . 3-dependent HSV-1 chromatinization . We performed H3 . 3 ChIP in in1374-infected BJ e-H3 . 3 cells that had been previously depleted for HIRA , UBN1 , DAXX or ATRX using one of the previously validated shRNAs ( S9A and S9B Fig ) . The data showed that overall the inactivation of UBN1 , DAXX or ATRX , had a weak impact on the association of H3 . 3 with the viral loci ( 1 to 3 loci significantly affected over 31 , 3 . 2 to 9 . 6% ) ( Fig 6C ) . The depletion of HIRA had a relatively greater effect ( 6/31 , 19 . 4% ) . To analyze if simultaneous inactivation of both complexes would significantly impact on HSV-1 chromatinization with H3 . 3 , one protein of each complex was inactivated at the same time before performing HSV-1 infection ( Fig 7A ) . Individual inactivation of HIRA and ATRX is known to lead to the functional inactivation of the HIRA and DAXX/ATRX complexes , respectively [35 , 52 , 63 , 64] . We noticed that the inactivation of HIRA by a siRNA was not as efficient as the shRNA on preventing the association of H3 . 3 with viral genomes ( Fig 7B ) . This is likely due to differences in the efficiency of the siRNA compared to the shRNA ( compare WBs of Figs S7B and 7A ) , and to the transitory effect of the siRNA compared to the stable effect of the shRNA at the time of the infection ( see Materials and Methods ) . Nonetheless , a significant decrease of the association of H3 . 3 with a large number of viral loci ( 20/31 , 64 . 5% ) was measured by the simultaneous inactivation of HIRA and ATRX compared to their individual inactivation ( Fig 7B ) . These results indicate that the DAXX/ATRX complex may compensate for the loss of the HIRA complex on the chromatinization of latent/quiescent HSV-1 genomes with H3 . 3 , and conversely . The above experiments were conducted in a context where the cells , although deficient for the activity of one H3 . 3 chaperone complex at a time , still contained intact PML NBs accumulating e-H3 . 3 ( S7 and S10 Fig ) . Therefore , we hypothesized that the accumulation of H3 . 3 within the PML NBs could be one of the key events acting upstream of the H3 . 3 chaperone complex activity for the induction of chromatinization of the latent/quiescent HSV-1 by H3 . 3 . We analyzed the HSV-1 chromatinization in cells lacking PML NBs . In a previous study conducted in HSV-1 latently infected PML KO mice , we showed that the absence of PML significantly impacted the number of latently infected TG neurons showing the “single”/vDCP NB HSV-1 pattern and favored the detection of neurons containing the “multiple-latency” pattern prone to LAT expression [16 , 53] . We analyzed the very few neurons showing a “single”/vDCP NB-like pattern in the latently infected PML KO mice for the co-localization of DAXX and ATRX with the viral genomes . We could not detect any of the two proteins co-localizing with the latent HSV-1 genomes ( Fig 8Ai to 8vi ) . Although informative , these in vivo studies did not allow the analysis of the real impact of the absence of PML on the co-localization of the other PML NB-associated proteins with latent HSV-1 genomes , because the neurons showing the “single”/vDCP NB-like pattern were too few to quantify the effect . We thus depleted PML in normal BJ cells using a PML shRNA-expressing lentiviral transduction approach . We verified the efficiency of the shRNAs against PML in normal BJ cells by IF , RT-qPCR and WB ( S11A–S11C Fig ) . PML-depleted BJ cells were superinfected with in1374 , and immuno-FISH was performed at 2 dpi to analyze the co-localization of HSV-1 genomes with DAXX , ATRX , HIRA , and UBN1 ( Fig 8B ) . Notably , both PML shRNAs gave similar results . The quantification of the data showed that , similarly to the in vivo situation , the depletion of PML significantly decreased the co-localization of DAXX and ATRX with latent/quiescent HSV-1 genomes , leaving HIRA and UBN1 unaffected for their co-localization ( Fig 8C , and S5 Table ) . Thus , we analyzed whether the failure of DAXX/ATRX to co-localize with the latent/quiescent HSV-1 genomes in the absence of PML NBs , could impact the chromatinization of HSV-1 with H3 . 3 . We first generated BJ e-H3 . 3 cells depleted for PML by shRNA-expressing lentiviral transduction similarly to the BJ cells ( S11D and S11E Fig ) . BJ e-H3 . 3 control or PML-depleted cells were superinfected with in1374 to perform immuno-FISH and analyze the co-localization of HSV-1 genomes with H3 . 3 ( Fig 9A ) . Quantification of the data showed a significant decrease in the co-localization of latent/quiescent HSV-1 genomes with H3 . 3 compared with controls ( Fig 9B ) , suggesting an impact of the absence of PML NBs on the latent/quiescent HSV-1 association with H3 . 3 . To complement these results at a more quantitative level , we performed ChIP on e-H3 . 3 . The data showed a major impact of the absence of PML NBs on the H3 . 3 association with viral genomes , with a significant depletion of H3 . 3 on multiple loci ( 21/31 , 68% ) ( Fig 9C ) . This could not be due to an indirect effect of PML depletion on H3 . 3 stability because e-H3 . 3 protein levels were similar in control cells and cells depleted for PML ( Fig 9D ) . Both PML shRNAs gave similar results . To confirm that the absence of PML had an impact on the H3 . 3 association with latent/quiescent viral genomes , we performed ChIP on in1374-infected control MEF pml+/+ or MEF pml-/- cells previously engineered by lentiviral transduction to express e-H3 . 3 ( Fig 9E ) . The data confirmed the impaired association of e-H3 . 3 with latent/quiescent HSV-1 genomes in the absence of PML , with 26/31 ( 84% ) viral loci significantly impacted ( Fig 9F ) . Cellular loci acid Sensing Ion Channel Subunit 2 ( Asic2 ) , and Heme Oxygenase 1 ( Hmox1 ) were used respectively as positive and negative controls for deposition of H3 . 3 in the absence of Pml as described in [44] . To definitively attribute the lack of deposition of H3 . 3 on viral loci to the absence of PML , MEF pml-/-;e-H3 . 3 cells were engineered to allow re-expression , under doxycycline induction , of the isoform I of human PML ( PML . I ) ( Fig9G ) , which was shown to participate to the HSV-1 antiviral restriction mechanism [65] . The formation of PML NBs after induction of PML . I was visualized by IF ( Fig 9H ) . ChIPs were then performed on in1374-infected MEF pml-/-;e-H3 . 3;myc-PML . I cells previously treated or not with doxycycline ( Fig 9I ) . The data showed that the re-expression of PML . I allowed the re-loading of H3 . 3 on all the analyzed loci of the latent/quiescent viral genomes with significant results obtained for 21 loci over 31 ( 68% ) , demonstrating the essential role of PML/PML NBs in the association of H3 . 3 with incoming viral genomes . Finally , we wanted to analyze whether the deficit of the H3 . 3 association with the viral genome in the absence of PML could be compensated by an increase of H3 . 1 on viral loci . The data from BJ e-H3 . 1 cells depleted for PML or MEF pml-/-;e-H3 . 1 cells , and infected with in1374 showed that H3 . 1 did not replace H3 . 3 on the viral loci ( S12A and S12B Fig ) . Altogether , these data demonstrate the essential role of PML NBs , probably through the DAXX/ATRX complex activity , in the exclusive H3 . 3 chromatinization of incoming viral genomes forced to adopt a vDCP NB-associated latent/quiescent pattern due to a deficit in the onset of lytic cycle . vDCP NB-associated latent genomes have been shown to be transcriptionally silent for the LAT expression in vivo [16] , and for the expression of a reporter gene in vitro in mouse TG neuron cultures [17] , and in human primary fibroblasts [45] . Moreover , it is known that the viral protein ICP0 induces the destabilization of PML NBs [66] and is essential for HSV-1 reactivation in vivo [67] , and for the transcriptional de-repression of a silenced viral genome in vitro [45 , 59 , 60] . However , it is not known if the transcriptional recovery is correlated to the destabilization of the vDCP NBs . We analyzed if latent HSV-1 genomes trapped in vDCP NBs were definitively silenced or could resume a transcriptional program leading to replication of viral genomes provided that vDCP NBs were destabilized . ICP0 or its non-functional RING finger mutant ( ICP0ΔRF ) were expressed from BJ-eTetR/cICP0 or BJ-eTetR/cICP0ΔRF cells harboring vDCP NBs for 4 days ( Fig 10 ) . HSV-1 in1374 infected BJ-eTetR cells were used as controls . Expression of ICP0 or ICP0ΔRF was induced for 24 h , 48 h or 72h at the permissive temperature for in1374 replication ( 32°C ) ( S13 Fig ) . Transcription of the reporter ( LacZ ) gene was measured by RT-qPCR to analyze the transcriptional recovery of the vDCP NB-associated latent/quiescent viral genomes ( Fig 10A ) . The addition of doxycycline in infected BJ-eTetR or BJ-eTetR/c ICP0ΔRF cells did not lead to any significant transcription of the LacZ gene . Only infected BJ-eTetR/cICP0 showed the recovery of LacZ mRNA transcription from 24 h post addition of doxycycline . To analyze if the virus could sustain replication , as suggested by the observation of the BJ-eTetR/cICP0 cell monolayer ( S14 Fig ) , following vDCP NB destabilization , immuno-FISH were performed at 24 h and 48 h post-addition of doxycycline . BJ-eTetR/cICP0 cells ( Fig 10B ) but not BJ-eTetR/cICP0ΔRF ( S15 Fig ) showed a clear disappearance of the vDCP NBs . Concomitantly , only BJ-eTetR/cICP0 cells showed the formation of replication compartments ( RCs ) indicating that the virus is in the process of lytic phase following vDCP NBs destruction by ICP0 . To confirm that the lytic transcriptional program was indeed occurring , viral transcripts of all kinetics were analyzed ( Fig 10C ) . Twenty four and 48 h post ICP0 induction , lytic genes were expressed with a clear switch towards the γ genes ( UL44/gC and US6/gD ) at 48 h confirming the onset of the lytic transcriptional program . Expression of ICP0ΔRF did not enable the re-expression of viral genes . These data show that vDCP NBs-associated latent/quiescent HSV-1 genomes can resume transcription and a lytic program provided that the vDCP NBs are destabilized , suggesting that these genomes are not definitively silenced , and could participate to the reactivation process of HSV-1 .
The HSV-1 genome enters the nucleus of infected neurons , which support HSV-1 latency as a naked/non-nucleosomal DNA . Many studies have described the acquisition of chromatin marks on the viral genome concomitantly to the establishment , and during the whole process , of latency . Paradoxically , although it is undisputable that these chromatin marks will predominantly be associated with latency and reactivation , few data are available for the initiation of the chromatinization of the incoming viral genome . Here , we demonstrate the essential contribution of PML NBs in the process of chromatinization of incoming HSV-1 genomes meant to remain in a latent/quiescent state . We showed that PML NBs are essential for the association of the histone variant H3 . 3 with the latent/quiescent HSV-1 . Two members of the HIRA complex , HIRA and ASF1a , were previously shown to be involved in H3 . 3-dependent chromatinization of HSV-1 genomes at early times after infection in non-neuronal and non-primary cells favoring the onset of the lytic cycle [68 , 69] . Moreover a recent study highlighted the interaction of HIRA with quiescent HSV-1 and plasmid DNA in primary human fibroblasts [56] . Our in vivo data in TG neurons and in vitro data in infected human primary fibroblasts or adult mice TG neuron cultures , show that all the proteins of the HIRA complex accumulate within specific nucleoprotein structures called the viral DNA-containing PML NBs or vDCP NBs . vDCP NBs contain transcriptionally silent HSV-1 genome that we previously demonstrated in vivo to be associated with the establishment of latency from the early steps of neuron infection [17] . Additionally , our data show that: ( i ) the mouse Hira protein , in vivo , and all the components of the HIRA complex , in cultured cells , temporarily accumulate in vDCP NBs , and ( ii ) significantly greater amount of incoming HSV-1 genomes co-localize with HIRA compared with PML at very early times pi ( 30 min ) . These data suggest that the HIRA complex could also be involved to some extent in the establishment of HSV-1 latency by the initial recognition of the incoming naked/non-nucleosomal viral DNA and the chromatinization of non-replicative HSV-1 genomes intended to become latent . In this respect , a recent study suggested an anti-viral activity associated with HIRA against HSV-1 and murine cytomegalovirus lytic cycles [56] . To that extent , although they are both functionally essential for the activity of the HIRA complex [35 , 51 , 64 , 70] , our data show that the depletion of HIRA has a greater effect compared to the UBN1 depletion , on the H3 . 3 association with the viral genomes . This could be simply explained by a better efficiency of the HIRA , compared to the UBN1 , shRNAs . Alternatively , HIRA was shown to be recruited to UV-induced DNA damage independently of UBN1 ( see figure S2D in [71] ) , and to participate to the loading of newly synthesized H3 . 3 on chromatin [72] . Therefore , the depletion of HIRA could indirectly and/or directly impact on two initial events occurring concomitantly to the entry of the viral genomes in the nucleus; first a signaling pathway associated to the detection of DNA breaks present in incoming viral DNA as suggested in [55 , 73]; and second the chromatinization process per se . If these two events are linked it could explain the differences observed between the HIRA and UBN1 depletion on the loading of H3 . 3 on the viral genomes . Experiments are in progress to investigate this . Interestingly , proteins of the HIRA complex have been previously shown to be able to directly bind to naked DNA in a sequence-independent manner , in contrast to DAXX and ATRX [52] . Nevertheless , our ChIP data highlight the interaction of viral genomes with DAXX and ATRX , but we cannot assert that the two proteins directly interact with naked DNA . The gamma-interferon-inducible protein 16 ( IFI16 ) , a member of the PYHIN protein family , has been described as a nuclear sensor of incoming herpesviruses genomes , and suggested to promote the addition of specific chromatin marks that contribute to viral genome silencing [74–81] . A proteomic study determining the functional interactome of human PYHIN proteins revealed the possible interaction between ATRX and IFI16 [82] . Thus , it will be interesting to determine in future studies if IFI16 and H3 . 3 chaperone complexes physically and functionally cooperate in the process of chromatinization of the latent/quiescent HSV-1 genome . One of the main finding of our study is the demonstration of the essential contribution of PML NBs in the H3 . 3-dependent chromatinization of the latent/quiescent HSV-1 genomes . A close link between PML NBs and H3 . 3 in chromatin dynamics has been demonstrated during oncogene-induced senescence ( OIS ) . In OIS , expression of the oncogene H-RasV12 induces DAXX-dependent relocalization of neo-synthesized H3 . 3 in the PML NBs before a drastic reorganization of the chromatin to form senescence-associated heterochromatin foci [42 , 43] . Hence , the contribution of the PML NBs in the deposition of H3 . 3 on specific cellular chromatin loci has also been reported [43 , 44] . The present study shows that the absence of Pml in HSV-1wt latently infected Pml KO mice , or the depletion of PML by shRNA in BJ cells infected with in1374 , significantly affects the co-localization of DAXX and ATRX , but not HIRA and UBN1 , with latent/quiescent HSV-1 genomes , confirming previous studies for DAXX and ATRX [45] . Taken together with the impaired association of H3 . 3 with the viral genomes in the absence of PML NBs , these data suggest that a significant part of the latent/quiescent HSV-1 genome chromatinization by H3 . 3 could occur through the activity of the DAXX/ATRX complex in association with the PML NBs . Given the particular structure formed by the latent/quiescent HSV-1 genome with the PML NBs , our study raises the question of the possible acquisition of a chromatin structure within the vDCP NBs . The individual inactivation of DAXX , ATRX , HIRA , or UBN1 significantly impacts the co-localization of the latent/quiescent HSV-1 genomes with PML , and hence the formation of vDCP NBs . However , it only mildly affects the association of H3 . 3 with viral genomes , suggesting an absence of correlation between the formation of vDCP NBs and H3 . 3 chromatinization . However , our data show that the depletion of DAXX , ATRX , HIRA , or UBN1 does not modify the accumulation of e-H3 . 3 at PML NBs , leaving intact the upstream requirement of H3 . 3 accumulation in PML NBs for H3 . 3-dependent viral chromatin assembly . We have recently shown that vDCP NBs are dynamic structures that can fuse during the course of a latent infection [17] . It is thus possible that incoming viral genomes can be dynamically associated with vDCP NBs to be chromatinized , and in the absence of any of the H3 . 3 chaperone complex subunit , this dynamic can be perturbed , resulting in some viral genomes that do not show a co-localization with PML . Given that depletion of none of the four proteins affects the structure of the PML NBs , and considering the essential role of PML NBs in the H3 . 3 chromatinization of the viral genomes , this possibility cannot be ruled out . The depletion of H3 . 3 , which almost exclusively participates in latent/quiescent HSV-1 genome chromatinization compared to H3 . 1/2 , does not prevent the formation of vDCP NBs ( S16 Fig ) , and is rather in favor of a chromatinization of the viral genome in the vDCP NBs . It is unlikely that canonical H3 . 1/2 could replace H3 . 3 for the chromatinization of the incoming HSV-1 genomes prior to the formation of the vDCP NBs . Indeed , our multiple immuno-FISH and ChIP assays failed to detect H3 . 1/2 and/or H3 . 1/2 chaperones that associate or co-localize with viral genomes . Nonetheless , we cannot rule out a possible replacement of H3 . 3 with another H3 variant for the chromatinization of viral genomes before their entrapment by the PML NBs to form vDCP NBs . Our data show that the vDCP NBs-associated HSV-1 genomes are chromatinized with H3K9me3 , and the Re-ChIP assays confirm an association with H3 . 3K9me3 , but not H3K27me3 . In vivo , it has been shown that both H3 modifications could be found on latent HSV-1 genomes [31 , 33 , 34] . One simple explanation could reside in the heterogeneity of latent genomes distribution within the nuclei of the infected neurons in the in vivo mouse and/or rabbit models of latency [16 , 17 , 54] , however this would need to be formally demonstrated . Though , vDCP NBs-associated HSV-1 genomes remain compatible with the transcription of lytic genes provided that the vDCP NBs are destabilized by ICP0 , a viral protein known to be required for full in vivo reactivation [67] , and to erase chromatin marks associated with latent/quiescent viral genomes in vitro [59] . Therefore , vDCP NBs are not a dead end for the virus life cycle , and HSV-1 latently infected neurons containing vDCP NBs are likely to contribute to the process of reactivation . Altogether , our study demonstrates the essential role of a PML NB/H3 . 3/H3 . 3 chaperone axis in the process of chromatinization of viral genomes adopting a vDCP NB pattern , which represents an essential structural and functional aspect of HSV-1 latency establishment . Given the involvement of H3 . 3 in the chromatinization of other latent herpesviruses belonging to different sub-families than HSV-1 , such as EBV [83] and HCMV [58] , as well as adenovirus type 5 [84] , this pathway of chromatinization is likely to play a major role in the biology of the whole Herpesviridae family , and possibly of other DNA viruses such as adenoviruses , papillomaviruses , hepatitis B virus , and retroviruses .
All procedures involving experimental animals conformed to the ethical standards of the Association for Research in Vision and Ophthalmology ( ARVO ) statement for the use of animals in research and were approved by the local Ethics Committee of the Institute for Integrative Biology of the Cell ( I2BC ) and the Ethics Committee for Animal Experimentation ( CEEA ) 59 ( Paris I ) under number 2012–0047 and in accordance with European Community Council Directive 2010/63/EU . For animal experiments performed in the USA: animals were housed in American Association for Laboratory Animal Care-approved housing with unlimited access to food and water . All procedures involving animals were approved by the Children’s Hospital Animal Care and Use Committee and were in compliance with the Guide for the Care and Use of Laboratory Animals ( protocol number: IAUC2013-0162 of 2/28/2107 ) . The HSV-1 SC16 strain was used for mouse infections and has been characterized previously [85] . The HSV-1 mutant in1374 is derived from the 17 syn + strain and expresses a temperature-sensitive variant of the major viral transcriptional activator ICP4 [86] and is derived from in1312 , a virus derived from the VP16 insertion mutant in1814 [87] , which also carries a deletion/frameshift mutation in the ICP0 open reading frame [88] and contains an HCMV-lacZ reporter cassette inserted into the UL43 gene of in1312 [89] . This virus has been used and described previously [17 , 45] . All HSV-1 strains were grown in baby hamster kidney cell ( BHK-21 , ATCC , CCL-10 ) and titrated in human bone osteosarcoma epithelial cells ( U2OS , ATCC , HTB-96 ) . In1374 was grown and titrated at 32°C in the presence of 3 mM hexamethylene bisacetamide [90] . PML wild-type , and knockout mice were obtained from the NCI Mouse Repository ( NIH , http://mouse . ncifcrf . gov; strain , 129/Sv-Pmltm1Ppp ) [91] . Genotypes were confirmed by PCR , according to the NCI Mouse Repository guidelines with primers described in [16] . Mice were inoculated and TG processed as described previously [16] . Briefly , for the lip model: 6-week-old inbred female BALB/c mice ( Janvier Labs , France ) were inoculated with 106 PFU of SC16 virus into the upper-left lip . Mice were sacrificed at 6 or 28 dpi . Frozen sections of mouse TG were prepared as described previously [16 , 92] . For the eye model: inoculation was performed as described previously [93] . Briefly , prior to inoculation , mice were anesthetized by intra-peritoneal injection of sodium pentobarbital ( 50 mg/kg of body weight ) . A 10-μL drop of inoculum containing 105 PFU of 17syn+ was placed onto each scarified corneal surface . This procedure results in ~80% mouse survival and 100% infected TG . Primary mouse TG neuron cultures were established from OF1 male mice ( Janvier lab ) , following a previously described procedure [17] . Briefly , 6–8-week-old mice were sacrificed before TG removal . TG were incubated at 37°C for 20 min in papain ( 25 mg ) ( Worthington ) reconstituted with 5 mL Neurobasal A medium ( Invitrogen ) and for 20 min in Hank’s balanced salt solution ( HBSS ) containing dispase ( 4 . 67 mg/mL ) and collagenase ( 4 mg/mL ) ( Sigma ) on a rotator , and mechanically dissociated . The cell suspension was layered twice on a five-step OptiPrep ( Sigma ) gradient , followed by centrifugation for 20 min at 800 g . The lower ends of the centrifuged gradient were transferred to a new tube and washed twice with Neurobasal A medium supplemented with 2% B27 supplement ( Invitrogen ) and 1% penicillin–streptomycin ( PS ) . Cells were counted and plated on poly-D-lysine ( Sigma ) - and laminin ( Sigma ) -coated , eight-well chamber slides ( Millipore ) at a density of 8 , 000 cells per well . Neuronal cultures were maintained in complete neuronal medium consisting of Neurobasal A medium supplemented with 2% B27 supplement , 1% PS , L-glutamine ( 500 μM ) , nerve growth factor ( NGF; 50 ng/mL , Invitrogen ) , glial cell-derived neurotrophic factor ( GDNF; 50 ng/mL , PeproTech ) , and the mitotic inhibitors fluorodeoxyuridine ( 40 μM , Sigma ) and aphidicolin ( 16 . 6 μg/mL , Sigma ) for the first 3 days . The medium was then replaced with fresh medium without fluorodeoxyuridine and aphidicolin . Primary human foreskin ( BJ , ATCC , CRL-2522 ) , lung ( IMR-90 , Sigma , 85020204 ) , fetal foreskin ( HFFF-2 , European Collection of Authenticated Cell Cultures , ECACC 86031405 , kind gift from Roger Everett , CVR-University of Glasgow ) fibroblast cells , primary human hepatocyte ( HepaRG , HPR101 , kind gift from Olivier Hantz & Isabelle Chemin , CRCL , Lyon , France ) cells , human embryonic kidney ( HEK 293T , ATCC CRL-3216 , kind gift from M . Stucki , University Hospital Zürich ) cells , U2OS , mouse embryonic fibroblast ( MEF ) pml+/+ , MEF pml-/- cells ( kind gift from Valérie Lallemand , Hopital St Louis , Paris ) , and BHK-21 cells were grown in Dulbecco’s Modified Eagle’s Medium ( DMEM ) supplemented with 10% fetal bovine serum ( Sigma , F7524 ) , L-glutamine ( 1% v/v ) , 10 U/mL penicillin , and 100 mg/mL streptomycin . BJ cell division is stopped by contact inhibition . Therefore , to limit their division , cells were seeded at confluence before being infected at a multiplicity of infection ( m . o . i . ) of 3 , and then maintained in 2% serum throughout the experiment . Infections of BJ cells for short times ( from 30 min to 6 h ) were performed by synchronizing the infection process with a pre-step of virus attachment to the cells at 4°C for one hour . The infection medium was then removed , and the temperature was shifted to 37°C to allow a maximum of viruses to simultaneously penetrate into the cells . Frozen sections of mouse TG were generated as previously described [92] . Mice were anesthetized at 6 or 28 d . p . i . , and before tissue dissection , mice were perfused intracardially with a solution of 4% formaldehyde , 20% sucrose in 1X PBS . Individual TG were prepared as previously described [92] , and 10-μm frontal sections were collected in three parallel series and stored at -80°C . HSV-1 DNA FISH probes consisting of cosmids 14 , 28 and 56 [94] comprising a total of ~90 kb of the HSV-1 genome were labeled by nick-translation ( Invitrogen ) with dCTP-Cy3 ( GE Healthcare ) and stored in 100% formamide ( Sigma ) . The DNA-FISH and immuno-DNA FISH procedures have been described previously [16 , 92] . Briefly , infected cells or frozen sections were thawed , rehydrated in 1x PBS and permeabilized in 0 . 5% Triton X-100 . Heat-based unmasking was performed in 100 mM citrate buffer , and sections were post-fixed using a standard methanol/acetic acid procedure and dried for 10 min at RT . DNA denaturation of the section and probe was performed for 5 min at 80°C , and hybridization was carried out overnight at 37°C . Sections were washed 3 x 10 min in 2 x SSC and for 3 x 10 min in 0 . 2 x SSC at 37°C , and nuclei were stained with Hoechst 33258 ( Invitrogen ) . All sections were mounted under coverslips using Vectashield mounting medium ( Vector Laboratories ) and stored at 4°C until observation . For immuno-DNA FISH , cells or frozen sections were treated as described for DNA-FISH up to the antigen-unmasking step . Tissues were then incubated for 24 h with the primary antibody . After three washes , secondary antibody was applied for 1 h . Following immunostaining , the cells were post-fixed in 1% PFA , and DNA FISH was carried out from the methanol/acetic acid step onward . The same procedures were used for infected neuronal cultures except that the cells were fixed in 2% PFA before permeabilization . Cells were collected in lysis buffer ( 10 mM Tris-EDTA , pH 8 . 0 ) containing a protease inhibitor cocktail ( Complete EDTA-free; Roche ) and briefly sonicated . Protein extracts were homogenized using QiaShredders ( Qiagen ) . The protein concentration was estimated by the Bradford method . Extracted proteins were analyzed by Western blotting using appropriate antibodies ( see below ) . Observations and most image collections were performed using an inverted Cell Observer microscope ( Zeiss ) with a Plan-Apochromat ×100 N . A . 1 . 4 objective and a CoolSnap HQ2 camera from Molecular Dynamics ( Ropper Scientific ) or a Zeiss LSM 800 confocal microscope . Raw images were processed using ImageJ software ( NIH ) . BJ or MEF cell lines expressing H3 . 1-SNAP-HAx3 ( e-H3 . 1 ) , H3 . 3-SNAP-HAx3 ( e-H3 . 3 ) , or Myc-hDAXX were established by retroviral transduction [95] . Briefly , pBABE plasmids encoding H3 . 1-SNAP-HAx3 or H3 . 3-SNAP-HAx3 ( gift from Dr L . Jansen ) , pLNCX2 encoding Myc-hDAXX [43] , were co-transfected with pCL-ampho ( for subsequent transduction of BJ cells , kind gift from M . Stucki , University Hospital Zürich ) or pCL-eco ( for subsequent transduction of MEF cells , kind gift from M . Stucki , University Hospital Zürich ) plasmids [96] by the calcium phosphate method into HEK 293T cells to package retroviral particles [97] . BJ cells stably expressing HIRA-HA and HA-UBN1 or transiently expressing the shRNAs were established by lentiviral transduction . Briefly , pLenti encoding HIRA-HA or HA-UBN1 , pLKOneo . CMV . EGFPnlsTetR , pLKO . DCMV . TetO . cICP0 , pLKO . DCMV . TetO . cICP0ΔRF ( gift from Dr . R . D . Everett , [98] ) , pLVX-TetOne-Myc-PML . I ( issued from pLNGY-PML . I , gift from Dr . R . D . Everett [65] ) , pLKO empty , pLKO shPML_01 , 02 , shDAXX_01 , 02 , shATRX_01 , 02 , shHIRA_01 , 02 , shUBN1_01 , 02 , were co-transfected with psPAX . 2 ( Addgene #12260 ) and pMD2 . G ( Addgene #12259 ) plasmids by the calcium phosphate method into HEK 293T cells to package lentiviral particles . After 48 h , supernatant containing replication-incompetent retroviruses or lentiviruses was filtered and applied for 24 h on the target BJ or MEF cells in a medium containing polybrene 8 μg/mL ( Sigma ) [95] . Stable transfectants were selected with Blasticidin S ( 5 μg/mL , Invivogen ) , puromycin ( 1 μg/mL , Invivogen ) , or neomycin ( G418 , 1 mg/mL , Millipore ) for 3 days , and a polyclonal population of cells was used for all experiments . Target sequences of the shRNA-expressing plasmids are provided in Table 1 . Cells were fixed with methanol-free formaldehyde ( #28908 , Thermo Fisher Scientific ) 1% for 5 min at RT , and then glycine 125 mM was added to arrest fixation for 5 min . After two washes with ice-cold PBS , the cells were scraped and resuspended in “Lysis Buffer” ( 10% glycerol , 50mM HEPES pH7 , 5; 140mM NaCl; 0 , 8% NP40;0 , 25% Triton; 1mM EDTA , Protease Inhibitor Cocktail 1X ( PIC ) ( Complete EDTA-free; Roche ) and incubated for 10 min at 4°C under shaking . The cells were subsequently washed in “Wash buffer” ( 200mM NaCl; 20mM Tris pH8; 0 , 5mM EGTA; 1mM EDTA , PIC 1X ) for 10 min at 4°C under shaking then were resuspended and centrifuged twice during 5 min 1700g at 4°C in “Shearing Buffer” ( 10mM Tris pH7 , 6; 1mM EDTA; 0 , 1%SDS; PIC 1X ) . Finally , nuclei were resuspended in 1mL of “Shearing Buffer” and were sonicated with a S220 Focused-ultrasonicator ( Covaris ) ( Power 140W; Duty Off 10%; Burst Cycle 200 ) . Eighty-five μL of the sonication product were kept for the input , 50 μL for analyzes of the sonication efficiency , and 850 μL diluted twice in IP buffer 2X ( 300mM NaCl , 10mM Tris pH8; 1mM EDTA; 0 , 1% SDS; 2% Triton ) for ChIP . Two micrograms of Ab were added and incubated overnight at 4°C . Fifty microliters of agarose beads coupled to protein A ( Millipore 16–157 ) or G ( Millipore 16–201 ) were added for 2 h at 4°C under constant shaking . Beads were then successively washed for 5 min at 4°C under constant shaking once in “low salt” ( 0 . 1% SDS , 1% Triton X-100 , 2 mM EDTA , 20 mM Tris HCl pH 8 . 0 , 150 mM NaCl ) buffer , once in “high salt” ( 0 . 1% SDS , 1% Triton X-100 , 2 mM EDTA , 20 mM Tris HCl pH 8 . 0 , 500 mM NaCl ) buffer , once in “LiCl” ( 0 . 25 mM LiCL , 1% NP40 , 1% NaDOC , 1 mM EDTA , 10 mM Tris HCl pH 8 . 0 ) buffer , and twice in TE ( 10 mM Tris pH 8 . 0 , 1 mM EDTA ) buffer . Chromatin-antibody complexes are then eluted at 65°C for 30 min under constant shaking with 200 μL of elution buffer ( 1% SDS , 0 . 1 M NaHCO3 ) . Input and IP products were de-crosslinked overnight at 65°C with 20 mg/mL of proteinase K ( Sigma ) and 10 mg/mL of RNAse A ( Sigma ) . DNA was then purified by phenol-chloroform/ethanol precipitation , resuspended in water , and kept at -20°C until use for qPCR . Quantitative PCR was performed using Quantifast SYBR Green mix ( Qiagen ) and the MX3005P apparatus ( Agilent/Stratagene ) . Primers were used at a final concentration of 1 μM . Their sequences and target genes are provided in Tables 2 and 3 . Cells were processed similarly to ChIP until addition of the antibody . Two μg of the first antibody were pre-incubating with agarose beads coupled to protein A ( Millipore 16–157 ) in PBS-0 . 5% BSA overnight at 4°C under shaking . The beads were washed twice with IP Buffer 1X ( 150mM NaCl , 5mM Tris pH8; 0 . 5mM EDTA; 0 . 1% SDS; 1% Triton ) , then the chromatin was incubated with the antibody/beads overnight at 4°C . Beads were then successively washed for 5 min at 4°C under constant shaking once in “low salt” buffer , once in “high salt” buffer , once in “LiCl” buffer and twice in TE buffer . Chromatin-antibody complexes were eluted at 37°C for 30 min with 100 μL of Re-ChIP elution buffer ( 1% SDS , 0 . 1M NaHCO3 , 10mM DTT ) . Fifty μL were kept for analysis of the first capture efficiency , then the other 50 μL were diluted 20 times with IP buffer 1X and incubated overnight at 4°C with the second antibody pre-incubated with agarose beads coupled to protein A . The beads were washed twice with IP Buffer 1X , then successively washed for 5 min at 4°C under constant shaking , once in “low salt” buffer , once in “high salt” buffer , once in “LiCl” buffer and twice in TE buffer . Chromatin-antibody complexes were then eluted at 65°C for 30 min under constant shaking with 200 μL of elution buffer ( 1% SDS , 0 . 1M NaHCO3 ) . Input and IP products were de-crosslinked overnight at 65°C a with 20 mg/mL of proteinase K ( Sigma ) and 10 mg/mL of RNAse A ( Sigma ) . DNA was then purified by phenol-chloroform/ethanol precipitation , resuspended in water , and kept at -20°C until use for qPCR . Transfections of BJ cells with siRNAs was performed using Lipofectamine RNAiMAX and following the supplier’s procedure ( Thermo Fisher Scientific ) . The following siRNAs were used at a final concentration of 40 nM for 48 h: siRNA_negative control ( EUROGENTEC , FR-CL000-005 ) , siHIRA 5’–GGAUAACACUGUCGUCAUC ( Dharmacon: J-013610-07 ) [52]; siH3F3A: 5′-CUACAAAAGCCGCUCGCAA [100]; siH3F3B: 5′-GCUAAGAGAGUCACCAUCA [100] . The antibodies used for immunofluorescence , ChIP and WB are provided in Tables 4–6 . BJ cells were transduced first with pLKOneo . CMV . EGFPnlsTetR to produce BJ-eTetR cell lines stably and constitutively expressing the EGFPnlsTetR protein ( selection G418 1 mg/mL ) . BJ-eTetR cells were then transduced with pLKO . DCMV . TetO . cICP0 or pLKO . DCMV . TetO . cICP0ΔRF to produce BJ-eTetR/cICP0 or BJ-eTetR/cICP0ΔRF expressing ICP0 or its RING finger mutant FXE , respectively ( selection puromycin 1 μg/mL ) . The expression of ICP0 or ICP0ΔRF was induced by the addition of doxycycline ( 100ng/μL ) in the medium . BJ-eTetR , BJ-eTetR/cICP0 or BJ-eTetR/cICP0ΔRF were infected with HSV-1 in1374 for 4 days at 38 . 5°C to stabilize the formation of vDCP NBs . Then doxycycline was added or not in the medium to induce the expression of ICP0 or ICP0ΔRF . Cells were incubated at 32°C the permissive temperature for in1374 ( see section virus ) . Twenty four hours , 48h or 72h after addition of doxycycline , the cells were fixed to proceed to IF or IF-FISH analyses or treated with FastLane cell SYBR Green RT-PCR ( Qiagen 204243 ) to analyze the LacZ and viral transcripts by RT-qPCR .
|
An understanding of the molecular mechanisms contributing to the persistence of a virus in its host is essential to be able to control viral reactivation and its associated diseases . Herpes simplex virus 1 ( HSV-1 ) is a human pathogen that remains latent in the PNS and CNS of the infected host . The latency is unstable , and frequent reactivations of the virus are responsible for PNS and CNS pathologies . It is thus crucial to understand the physiological , immunological and molecular levels of interplay between latent HSV-1 and the host . Promyelocytic leukemia ( PML ) nuclear bodies ( NBs ) control viral infections by preventing the onset of lytic infection . In previous studies , we showed a major role of PML NBs in favoring the establishment of a latent state for HSV-1 . A hallmark of HSV-1 latency establishment is the formation of PML NBs containing the viral genome , which we called “viral DNA-containing PML NBs” ( vDCP NBs ) . The genome entrapped in the vDCP NBs is transcriptionally silenced . This naturally occurring latent/quiescent state could , however , be transcriptionally reactivated . Therefore , understanding the role of PML NBs in controlling the establishment of HSV-1 latency and its reactivation is essential to design new therapeutic approaches based on the prevention of viral reactivation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"microbiology",
"dna-binding",
"proteins",
"neuroscience",
"viral",
"genome",
"mammalian",
"genomics",
"cell",
"nucleus",
"nuclear",
"bodies",
"cellular",
"structures",
"and",
"organelles",
"microbial",
"genomics",
"viral",
"genomics",
"genome",
"complexity",
"animal",
"cells",
"proteins",
"histones",
"animal",
"genomics",
"biochemistry",
"cellular",
"neuroscience",
"cell",
"biology",
"viral",
"persistence",
"and",
"latency",
"virology",
"neurons",
"genetics",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"genomics",
"computational",
"biology"
] |
2018
|
Promyelocytic leukemia (PML) nuclear bodies (NBs) induce latent/quiescent HSV-1 genomes chromatinization through a PML NB/Histone H3.3/H3.3 Chaperone Axis
|
Concerns have been raised regarding handling of Ebola virus contaminated wastewater , as well as the adequacy of proposed disinfection approaches . In the current study , we investigate the inactivation of Ebola virus in sterilized domestic wastewater utilizing sodium hypochlorite addition and pH adjustment . No viral inactivation was observed in the one-hour tests without sodium hypochlorite addition or pH adjustment . No virus was recovered after 20 seconds ( i . e . 4 . 2 log10 unit inactivation to detection limit ) following the addition of 5 and 10 mg L-1 sodium hypochlorite , which resulted in immediate free chlorine residuals of 0 . 52 and 1 . 11 mg L-1 , respectively . The addition of 1 mg L-1 sodium hypochlorite resulted in an immediate free chlorine residual of 0 . 16 mg L-1 , which inactivated 3 . 5 log10 units of Ebola virus in 20 seconds . Further inactivation was not evident due to the rapid consumption of the chlorine residual . Elevating the pH to 11 . 2 was found to significantly increase viral decay over ambient conditions . These results indicate the high susceptibility of the enveloped Ebola virus to disinfection in the presence of free chlorine in municipal wastewater; however , we caution that extension to more complex matrices ( e . g . bodily fluids ) will require additional verification .
Ebola virus infected individuals shed the virus in bodily fluids [1–3] and may produce up to nine liters of bodily waste per day , in addition to wash waters [4] . Subsequently , concerns were raised during the 2014/15 Ebola virus epidemic regarding the appropriate handling of Ebola virus contaminated wastewater to minimize potential secondary exposure to the virus [5] . Ebola virus is an enveloped filovirus that is primarily spread via direct contact with infected individuals [6] . Secondary transmission via environmental routes ( i . e . fomites ) has previously been recognized [7] , but the available evidence on environmental transmission is controversial [8] . Previously reported concentrations of Ebola virus in bodily fluids ( sweat , urine , and stool ) has been in the range of 2 . 8–7 . 2 log10 viral RNA copies mL-1 [9–11] , and 5 log10TCID50 mL-1 in the blood of infected macaques [12] . The conversion of RNA copies to viable virus is unknown . The median infectious dose for Ebola virus is low , in the range of nine plaque forming units , depending on the route of infection [13] . The World Health Organization initially recommended that liquid waste from Ebola patients be directly disposed into the sanitary sewers or latrines without disinfection [5] . The recommendation for direct disposal of Ebola virus contaminated liquid waste was made due to the expected rapid inactivation and dilution of Ebola virus in wastewater , as well as a lack of evidence for Ebola virus transmission via water . Subsequently , questions were raised regarding Ebola virus persistence in wastewater and appropriate approaches for disinfection . Research has since identified the T90 ( time for 90% inactivation ) of Ebola virus in sterilized wastewater to be 2 . 1 days [14] , which is consistent with estimated persistence using viral surrogates [15] . Additionally , waste , including wastewater , has since been highlighted as a possible transmission risk—especially waste contaminated with infected blood [16] . Previous evaluations have demonstrated that Ebola virus is highly stable in blood [17] . In response to the uncertainty regarding appropriate wastewater disinfection approaches and the resulting risk of secondary exposure or transmission , Ebola Treatment Units in the United States chose ad hoc liquid waste disinfection approaches prior to disposal [4] . The World Health Organization ultimately revised recommendations to suggest holding liquid waste in latrines for a week to allow viral decay and inactivation [18] . Currently , the disinfection kinetics of Ebola virus in liquid is unknown . In a previous evaluation of Ebola virus disinfection on surfaces , sodium hypochlorite at 0 . 01% and 0 . 1% was found to be ineffective but 0 . 5% and 1% sodium hypochlorite removed viable virus in five minutes [19] . Additionally , filoviruses have been previously recognized to be highly susceptible to inactivation by UV exposure [20 , 21] . The pH stability of Ebola virus in wastewater is unknown . The overarching study goal was to determine the disinfection of Ebola virus in municipal wastewater , of direct relevance to wastewater management in an outbreak scenario . Our scope was limited to municipal wastewater and did not consider the disinfection of Ebola virus in concentrated human waste ( e . g . feces , vomit , or blood ) . It should be noted that disinfection under high organic load ( e . g . feces , vomit , or blood ) , which is not the focus of the current manuscript , would require hyper-chlorination , which has been suggested to inconsistently achieve adequate disinfection and would require additional experimental verification [22] . In the current study we evaluated the disinfection of Ebola virus in sterilized domestic wastewater by chlorine addition and pH adjustment . Study limitations as well as implications for wastewater handling in outbreak response are discussed .
Wastewater samples were collected from a municipal wastewater treatment plant as described previously [14] and shipped overnight on ice to Rocky Mountain Laboratories . Upon receipt , samples were sterilized with five mega-rads of gamma irradiation and a subset of gamma-irradiated sample was sent back to the University of Pittsburgh for characterization and chlorine demand analysis . Wastewater characteristics are summarized in Table 1 . Sterilization was performed to block microbial growth during cell culture , which would make virological analyses impossible . Stock virus ( Ebola virus Guinea Makona-WPGC07 , 107 . 3 TCID50 mL-1 ) [23] was diluted in wastewater to achieve an approximate starting viral titer of 105 TCID50 mL-1 for both Ebola virus disinfection experiments and pH inactivation experiments . All experiments were completed in triplicate at 20°C . Ebola virus titration and cultivation were performed as previously described [14] . The limit of detection for all replicates was 0 . 75 log TCID50 mL-1 . For disinfection experiments , sodium hypochlorite ( Acros Organics ) was added to two milliliter vials of the wastewater/virus suspension at initial doses of 0 , 1 , 5 , and 10 mgL-1 . Samples were then taken at the indicated time points and chlorine demand immediately quenched by the addition of sodium thiosulfate . The ‘time zero’ sampling point was taken approximately 20 seconds following the addition of chlorine to enable sample mixing . Three pH values were evaluated for pH inactivation experiments: 6 . 9 ( intrinsic ) , 4 . 3 , and 11 . 2 . pH values were found to be stable for the time period evaluated . The tested pH values were chosen to be below the previously recognized Ebola virus glycoprotein stability down to pH = 4 . 8 [24] and to be within the tested values for sterilization of wastewater in an outbreak setting via elevated pH [22] . The virus was then directly added to the pH-adjusted wastewater , mixed via pipetting , and sampled . The ‘time zero’ sampling point was taken approximately 20 seconds following the addition of virus to enable sample mixing . Chlorine residuals in both the untreated and the gamma-irradiated wastewater were experimentally determined outside of the Biosafety Level 4 facility using a Hach Free Chlorine test kit ( method 10069 ) in triplicate . Chlorine residual was experimentally found to be dose dependent ( S1 Fig ) . To determine the immediate chlorine demand ( and residual ) , chlorine residual was plotted versus time for each initial chlorine dose . A linear fit was then applied to each the residual versus time plot for each dose , and the y-intercept ( i . e . modeled initial chlorine residual ) of the linear fit was determined ( S2–S4 Figs ) . Chlorine residuals of zero were excluded from this fit . Chlorine decay was then modeled as previously described eq ( 1 ) [25]; C=C0e−kt ( 1 ) C0 was the modeled initial chlorine residual . The concentration-time exposure was then calculated for each sampling time point by integrating the area under the modeled chlorine residual curve at each time point . Statistical analyses and graphing were completed with Prism 7 . 0a and Microsoft Excel 2011 .
The current study has multiple limitations . The wastewater was required to be disinfected by gamma irradiation to avoid bacterial contamination and toxicity of the cell culture line . This irradiation resulted in an increased chlorine demand by the wastewater and may have altered other wastewater chemistry . Additionally , chlorine residual modeling was performed based upon laboratory tests using solely the gamma-irradiated wastewater . The virus to be disinfected was suspended in cell culture media—while the virus suspension comprised less than 1% of the test matrix , this has the potential to alter the chlorine demand of the test . We note that in this case , the observed viral persistence would be conservative , i . e . the actual Ct would in fact be less than the modeled Ct for the evaluated conditions and viral inactivation would be more rapid than reported . Due to the dilute nature of the wastewater evaluated , the role of higher organic loading and particle association in protecting virus from disinfection remains unresolved , although recent studies using Ebola virus surrogates have suggested that the majority ( ~90% ) of viral particles remain not particle associated [29 , 30] . Despite the end of the most recent Ebola virus epidemic , concerns remain regarding the potential transmission of emerging enveloped viruses via water [31] , highlighting the value of continued investigation into enveloped virus persistence and disinfection . These results demonstrate the high susceptibility of Ebola virus to disinfection in the presence of free chlorine . The most conservative estimate for Ebola virus disinfection was less than current recommendations for waterborne virus inactivation , suggesting that existing disinfection approaches are adequate to achieve Ebola virus reductions in wastewater . In addition , elevated pH would provide significantly improved viral inactivation over ambient decay . These results highlight the value of considering wastewater disinfection in response to infectious disease outbreaks to minimize the risk of secondary transmission , as well as to address public concern .
|
Ebola virus infected individuals may generate up to nine liters of potentially infectious liquid waste per day . Previous recommendations were to directly dispose of this waste into a sanitary sewer or latrine; however , release of infectious virus raised the concern of environmental transmission through unintentional contact with contaminated wastewater . One possibility to reduce or eliminate the release of infectious virus is disinfection of Ebola virus contaminated liquid waste . A hurdle to making recommendations for liquid waste disinfection is the lack of data on disinfection efficacy . Here we demonstrate that Ebola virus in municipal wastewater is highly sensitive to disinfection in the presence of free chlorine . In addition , elevating the pH to 11 . 2 significantly increased the rate of decay over neutral pH conditions . These results provide a basis to develop recommendations for the disinfection of Ebola virus contaminated wastewater .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"and",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"pathology",
"and",
"laboratory",
"medicine",
"chemical",
"compounds",
"pathogens",
"biological",
"cultures",
"salts",
"microbiology",
"hypochlorites",
"disinfection",
"health",
"care",
"viruses",
"preventive",
"medicine",
"filoviruses",
"rna",
"viruses",
"cell",
"cultures",
"infectious",
"disease",
"control",
"research",
"and",
"analysis",
"methods",
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"medical",
"microbiology",
"microbial",
"pathogens",
"chemistry",
"chlorine",
"hematology",
"chemical",
"elements",
"blood",
"ebola",
"virus",
"viral",
"persistence",
"and",
"latency",
"anatomy",
"sanitization",
"virology",
"viral",
"pathogens",
"physiology",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"hemorrhagic",
"fever",
"viruses",
"organisms"
] |
2017
|
Disinfection of Ebola Virus in Sterilized Municipal Wastewater
|
The mean age of acute dengue has undergone a shift towards older ages . This fact points towards the relevance of assessing the influence of age-related comorbidities , such as diabetes , on the clinical presentation of dengue episodes . Identification of factors associated with a severe presentation is of high relevance , because timely treatment is the most important intervention to avert complications and death . This review summarizes and evaluates the published evidence on the association between diabetes and the risk of a severe clinical presentation of dengue . A systematic literature review was conducted using the MEDLINE database to access any relevant association between dengue and diabetes . Five case-control studies ( 4 hospital-based , 1 population-based ) compared the prevalence of diabetes ( self-reported or abstracted from medical records ) of persons with dengue ( acute or past; controls ) and patients with severe clinical manifestations . All except one study were conducted before 2009 and all studies collected information towards WHO 1997 classification system . The reported odds ratios were formally summarized by random-effects meta-analyses . A diagnosis of diabetes was associated with an increased risk for a severe clinical presentation of dengue ( OR 1 . 75; 95% CI: 1 . 08–2 . 84 , p = 0 . 022 ) . Large prospective studies that systematically and objectively obtain relevant signs and symptoms of dengue fever episodes as well as of hyperglycemia in the past , and at the time of dengue diagnosis , are needed to properly address the effect of diabetes on the clinical presentation of an acute dengue fever episode . The currently available epidemiological evidence is very limited and only suggestive . The increasing global prevalence of both dengue and diabetes justifies further studies . At this point , confirmation of dengue infection as early as possible in diabetes patients with fever if living in dengue endemic regions seems justified . The presence of this co-morbidity may warrant closer observation for glycemic control and adapted fluid management to diminish the risk for a severe clinical presentation of dengue .
With low- and middle-income countries ( LMIC ) experiencing a growing chronic non-communicable disease ( NCD ) burden and a continuously high communicable disease ( CD ) incidence rate , understanding the co-morbidity between the two disease groups is necessary to properly assess , monitor , evaluate , and control their prevalence [1 , 2] . Cardiovascular diseases and their risk factors including diabetes mellitus ( DM ) are major contributors to the growing NCD burden [3] . WHO projects that DM will be the 7th leading cause of death in 2030 . Today there are 347 million people worldwide who have DM [4] , around 90% of them type 2 DM [5] . More than 80% of DM deaths occur in LMIC[5] . In high income countries DM has long been known for its association with increased susceptibility to infections such as tuberculosis [6] . Although these associations have been attributed in part to DM associated alterations in innate immunity , related evidence is inconsistent and underlying mechanisms remain poorly understood . They are likely to vary by type of infection [7] . Yet , only few studies investigated the complex associations of diabetes with neglected tropical diseases ( NTDs ) . Dengue , one of 17 diseases assigned NTD status by WHO , is next to malaria the most important arthropo-borne ( ARBO ) tropical infection caused by the dengue virus . It is transmitted by several mosquito species within the genus Aedes , principally A . aegypti [8] . The number of Dengue virus infections has increased 30fold over the last decades . Today it is a major public health problem in tropical and subtropical regions [9] . The absence of adequate public health awareness , surveillance and control , population growth , globalization and urbanization contributed to this increase . An estimated 2 . 5 billion people are at risk of infection in over 100 endemic countries[10] . WHO estimates that currently between 50 and 100 million dengue infections occur annually . An estimated 500’000 dengue patients with potentially life threatening symptoms require hospitalization each year and about 2 . 5% of those affected die [11] . Dengue ranges from asymptomatic or self-limiting non-severe dengue ( with or without warning symptoms ) to severe dengue , characterized variously by severe plasma leakage , severe bleeding or severe organ involvement [12] . The studies reviewed in this paper were all except one conducted before 2009 and therefore routine clinical data was collected according to the WHO 1997 guidelines . These guidelines group symptomatic dengue virus infections into three clinical categories: undifferentiated fever , dengue fever ( DF ) , and dengue hemorrhagic fever ( DHF ) . DHF is further subclassified into four severity grades , with grades III and IV being defined as dengue shock syndrome ( DSS ) [13] . The clinical presentation of a dengue infection is difficult to predict , although the presence of warning signs occurring within 3 to 7 days after the first symptoms warrant strict observation and medical intervention . Intervention , including intravenous rehydration as the therapy of choice , can reduce case fatality in severe dengue to less than 1% [14] . Dengue has long been viewed as a pediatric disease , but the average age of dengue cases has been rising and there is a suggestion for adults to be at increased risk for dying from dengue . The increase of tourism in tropical regions also contributed to the increase in adult dengue cases [15] . There are five dengue virus ( DENV ) serotypes ( DENV-1 , DENV-2 , DENV-3 , DENV-4 and DENV-5 ) , however , serotype 5 seems not to have a sustained transmission cycle in humans [16] . Infection confers immunity to the infecting serotype which is life-long , but not to the remaining three . Subsequent infections with a different dengue virus serotype increase the risk of severe complications [17] . Other than that , factors increasing the risk of severe clinical manifestations remain poorly characterized [18–20] . Present evidence suggests that beyond viral factors age , gender , social status , genetic background , sickle cell anemia , uremia , bronchial asthma , allergies , hypertension , chronic renal failure and also DM might adversely influence the clinical presentation of an infection [18 , 19 , 21–24] . In acknowledging the shift of dengue to older ages and the steep increase in the prevalence of DM , the objective of this review is to access the current clinical and epidemiological evidence for this NCD to contribute to a higher risk of a severe clinical presentation in dengue fever patients .
We conducted a systematic literature review using MEDLINE database to access any relevant publication describing an association between dengue and diabetes up to February 28 2014 . The search terms used were “ ( "dengue"[MeSH Terms] OR "dengue"[All Fields] ) AND ( "diabetes mellitus"[MeSH Terms] OR ( "diabetes"[All Fields] AND "mellitus"[All Fields] ) OR "diabetes mellitus"[All Fields] OR "diabetes"[All Fields]” . We included articles in all languages; articles which reported on epidemiology , clinical signs , and laboratory parameters for dengue-infected patients , or on severity assessment . There was no restriction in publication dates , place of study , study design or age of research participants . As a validity assessment , the PRISMA criteria were used [25 , 26] . For meta-analyses , we included studies that compared the prevalence of DM between persons affected by different dengue stages ( case-control studies ) , reporting estimates of association and their 95% confidence intervals , or enough information to derive this . We extracted the year during which dengue cases were diagnosed ( year during which the study was conducted ) , year of the publication , country of the study , study design , study definitions of dengue infection and diabetes , and confounder adjustments . We extracted data on the sample size of enrolled persons and number of cases and controls and on the estimates ( unadjusted and adjusted models ) of the association ( and their 95% confidence intervals ) between diabetes and severe dengue . The transition of the patients data from clinical or hospital records to this review was based on published non-individual and non-identifying data . Data were extracted from the published papers independently by two reviewers and disagreements were resolved by discussion . We used random-effects models for meta-analyses of the association between DM and a severe clinical presentation of dengue [29] . They take into consideration the variation between the true effects estimated by included studies unlike fixed effect models which assume a common true effect across studies . We used odds ratios as measure of association across all studies . We used the estimates reported by the authors as “primary model” and the I2 metric and Tau2 to describe the between study heterogeneity and variance respectively . We conducted sensitivity analyses by using a fixed effect model and excluding a study , which was based on non-matched random controls and that only reported unadjusted estimates [30] . We performed analyses with Stata version 12 ( Stata Corporation , Texas ) and considered p<0 . 05 as statistically significant .
Epidemiological studies included in the meta-analysis compared the prevalence of DM and other co-morbidities between patients suffering from acute dengue with a severe presentation and controls with acute or past dengue without severe clinical manifestations ( Table 1 ) . In the absence of controls without evidence for a dengue infection history , studies thereby compared the prevalence of DM in dengue patients with different degrees for severity in clinical presentation , rather than the risk of being infected with dengue virus . The only population-based case-control study [27] identified was conducted in two Brazilian cities and included both , children and adults . DHF cases were ascertained through the national surveillance system . The surveillance records were reviewed by two physicians . DHF was defined according to criteria used by Brazilian Healthy System , which were very close to WHO 1997 criteria . Controls were selected from the same neighborhood as cases . In addition , they were tested positive for anti-dengue IgG and matched to cases by age , sex and a report of a past dengue-like fever in the same year as the DHF diagnosis of cases . Additional information from both , cases and controls was obtained through in-person interviews . DM was based on a self-reported physician-diagnosis . Interviewers also asked for medication intake and verified it by seeing the prescription of packaging . DM was statistically significantly associated with DHF independent of age , sex skin color , income and educational level ( aOR 2 . 75 , 95% CI 1 . 12–6 . 73 ) . The association of self-reported diabetes with DHF was stronger in diabetic patients being treated , especially if treated with insulin or more than 1 drug ( aOR 3 . 36; 95% CI 0 . 72–15 . 61 ) . In addition , white ethnicity ( aOR 4 . 70 , 95% CI 2 . 17–10 . 2 ) , high income ( aOR 6 . 84 , 95% CI 4 . 09–11 . 43 ) , high educational level ( aOR 4 . 67 , 95% CI 4 . 09–11 . 43 ) and a self-report of allergy treated with steroids ( aOR 2 . 94 , 95% CI 1 . 01–8 . 54 ) were also associated with a more severe clinical presentation of dengue . A hospital-based study including persons aged 15 to 65 in Pakistan [31] compared hospitalized acute DHF patients ( cases ) to dengue IgG positive patients , but hospitalized for unrelated conditions ( controls ) . The study was conducted in two major tertiary care hospitals . A DHF case was defined as diagnosis by an experienced clinician applying WHO criteria , although the version applied was not specified . The categorization into persons with and without DHF points to the use of WHO 1997 criteria . Information was obtained through structured record review and in-person interview . Age- and sex-matched DHF cases were slightly more likely to report a DM diagnosis than control patients . In both groups , the reported prevalence of DM was exceptionally high , 41 . 8% in controls and 43 . 2% in cases . The association of DM with DHF adjusted for age , sex and duration of illness was statistically non-significant ( aOR 1 . 26 , 95% CI 0 . 78–2 . 03 , p = 0 . 34 ) . In Singapore , a hospital-based study was conducted in the nation’s largest clinic . It included all admitted patients with acute dengue without age restriction [32] . Information on case and control status as well as comorbidities was exclusively derived from abstracting medical charts . Cases were defined as DHF patients , and controls were DF patients . Probable dengue patients had a positive acute dengue serology . Confirmed dengue patients had positive dengue polymerase chain reaction assays . Clinical diagnosis for DHF was based on WHO 1997 criteria . The data was analyzed separately for the two epidemic periods of 2006 ( predominantly DENV-1 ) and 2007/2008 ( predominantly DENV-2; larger sample size ) . No association between DM status and DHF was found in the 2006 dengue outbreak . DM was independently associated with DHF in the 2007/8 epidemic ( aOR 1 . 78 , 95% CI 1 . 06–2 . 97 ) . The association was stronger if the diabetic patients additionally had hypertension ( aOR 2 . 16 , 95% CI 1 . 18–3 . 96 ) or asthma ( aOR 4 . 38 , 95% CI 0 . 80–23 . 85 ) . Mean hospitalization days were longer for DM ( 4 . 99±3 . 34days ) as compared to non-DM patients ( 4 . 04±1 . 62 days , p = 0 . 001 ) . Additional factors associated with DHF were Chinese ethnicity ( compared to Malay or Indian ethnicity ) ( aOR 1 . 90 , 95% CI 1 . 01–3 . 56 in 2006 epidemic periods and aOR 1 . 67 , 95% CI 1 . 24–2 . 24 in 2007/2008 epidemic periods ) as well as middle age in 2007/2008 ( aOR 1 . 41 , 95% CI 1 . 09–1 . 81 in 30–39 years and aOR 1 . 34 , 95%CI 1 . 09–1 . 81 in 40–49 years of age group ) . In 2002 in Taiwan , all patients with confirmed acute DF treated at the Kaoshiung Medical University Hospital during a large outbreak occurring in the southern Taiwan were categorized into groups of DF and DHF/DSS by strictly adhering to clinical WHO 1997 criteria and laboratory confirmation under the auspices of the Taiwanese Centre of Disease Control [33] . Clinical information such as signs and symptoms and the results of blood investigation were abstracted from medical records . Cases were mostly adults , only 4 . 5% were below age 15 years . The prevalence of DM was 16 . 8% in DHF/DSS cases compared to 7 . 6% in DF patients ( controls ) . The adjusted OR reported was 1 . 86 ( 95%CI 1 . 04–3 . 37 ) , albeit covariates were not reported . In addition to the independent association of DHF/DSS with DM , statistically significant associations with hypertension and renal insufficiency , uremia , past history of dengue infection as well as male gender and older age were also found . A hospital-based study in Southern India [30] obtained information on patients admitted to the largest multi-specialty hospital in South Kerala for acute dengue between 2005 and 2008 . The case group consisted of 10 in-hospital deaths of patients admitted with a clinical diagnosis of probable dengue , which was confirmed by either RT-PCR or IgM antibody tests , and review of clinical symptoms through medical record review . Forty non-matched controls were randomly selected among patients with a confirmed acute dengue , but recovering from the illness . The classification of dengue among the controls was not specified . Information on co-morbidities and other factors was abstracted from medical records . The prevalence of DM in controls was 2 . 5% compared to 40% in cases . DM was a strong predictor of mortality in the bivariate analysis ( OR 26 . 0 , 95% CI 2 . 47–273 . 67 , p = 0 . 004 ) . In the same study , hypertension was also a strong predictor of mortality ( OR 44 . 3 , 95% CI 6 . 2–315 . 5 , p = 0 . 000 ) . Mortality was much higher in patients over 40 years ( OR 9 . 3 , 95% CI 1 . 9–44 . 4 , p = 0 . 002 ) . No adjusted odds ratios , which would facilitate the interpretation of independency in the reported associations , were reported . We included the results of five above mentioned studies in a meta-analysis . One of these studies reported two separate estimates from two independent cross-sectional assessments; hence , we considered them as separate studies . The meta-analysis showed that the presence of a severe clinical presentation of dengue was positively associated with the presence of DM . A diagnosis of DM was associated with an increased risk for severe clinical manifestations of dengue by 75% ( 95% CI: 1 . 08–2 . 84 , p = 0 . 022 ) compared to non-DM patients ( Fig 2 ) . This OR remained robust across sensitivity analyses involving fixed effect analysis . We observed some heterogeneity across the studies , consistent with the broadly differing study settings . The small number of studies included in the meta-analysis did not provide statistical power for formal statistical assessment of heterogeneity ( Fig 2 ) . We additionally identified five case series reporting dengue-related hospitalizations and the prevalence of DM in these cases . They are listed in Table 1 as reference of the prevalence of DM in dengue patients with a severe clinical presentation . The studies have been conducted mostly in Asian dengue endemic regions such as Malaysia [36]; Bangladesh [28]; Singapore [34 , 35] . In the absence of a control group , these studies are of limited value for better understanding of the role of DM on the clinical presentation of dengue . In several instances the case definition was restricted to just being dengue seropositive . This does not allow differentiating between DM influencing the clinical manifestations of dengue infection versus dengue infection influencing the clinical presentation of DM . Of interest , in that respect is the study by Hasanat et al [28] . Hospitalized DF patients underwent oral glucose tolerance testing ( OGTT ) between 3 and 10 days after the start of illness . A subset of these patients agreed to a second OGTT before discharge . The authors demonstrated a high rate of glucose intolerance in the early phase of disease , which returned though to normal in 55% of the patients .
Understanding factors increasing the likelihood of dengue patients with severe clinical symptoms would help the physician to decide in a timely fashion on the need for close observation , adequate treatment , or hospitalization . The available evidence points to DM as a potentially important co-factor . Additional prospective studies among DM and non-DM patients are needed to assess the impact of pre-existing DM as well as of hyperglycemia at the time of dengue diagnosis on the risk of a severe clinical presentation of dengue and of related deaths . Even in the absence of causal inference , it seems justified that fever episodes in patients with DM and living in a dengue endemic region are confirmed for dengue as soon as possible and that they remain under close surveillance if an acute dengue infection is confirmed . Future studies need to also address whether better control of glycemia level in dengue patients with DM can improve the outcome of the patient and decrease the risk of a severe clinical presentation . As the fluid management in diabetic patients with a severe clinical presentation of dengue poses a particular challenge , this issue should be taken into consideration by these studies . Diabetic patients in dengue endemic regions should consistently receive recommendations to protect against dengue infection by taking preventive measures against indoor mosquito breeding and against mosquito bites .
|
Both dengue and diabetes have reached epidemic dimensions and pose a joint threat to a large proportion of populations in low- and middle-income countries . Dengue is no longer a disease primarily affecting children . Therefore the influence of non-communicable diseases such as diabetes , which are increasingly prevalent in adults , on the clinical presentation of a dengue episode becomes a public health priority . We conducted a systematic literature review to assess the available evidence on the effect of diabetes mellitus ( DM ) on the clinical presentation of dengue . The meta-analysis of published evidence combined with supporting biological evidence point to an increased risk for potentially life threatening symptoms of dengue among patients with diabetes . The current evidence is limited by statistical power and other study limitations and does not allow conclusions about a causal effect of diabetes . Yet , based on the currently available evidence , diabetes patients with fever and living in a dengue endemic region should seek confirmation of dengue infection as early as possible . Diabetes should be considered in the triage of patients for close observation and early intervention , which are challenges , particularly during dengue outbreaks . Timeliness of intervention is the most important factor averting serious complications and death in patients with acute dengue .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Is Diabetes a Risk Factor for a Severe Clinical Presentation of Dengue? - Review and Meta-analysis
|
The increasing application of gene panels for familial cancer susceptibility disorders will probably lead to an increased proposal of susceptibility gene candidates . Using ERCC2 DNA repair gene as an example , we show that proof of a possible role in cancer susceptibility requires a detailed dissection and characterization of the underlying mutations for genes with diverse cellular functions ( in this case mainly DNA repair and basic cellular transcription ) . In case of ERCC2 , panel sequencing of 1345 index cases from 587 German , 405 Lithuanian and 353 Czech families with breast and ovarian cancer ( BC/OC ) predisposition revealed 25 mutations ( 3 frameshift , 2 splice-affecting , 20 missense ) , all absent or very rare in the ExAC database . While 16 mutations were unique , 9 mutations showed up repeatedly with population-specific appearance . Ten out of eleven mutations that were tested exemplarily in cell-based functional assays exert diminished excision repair efficiency and/or decreased transcriptional activation capability . In order to provide evidence for BC/OC predisposition , we performed familial segregation analyses and screened ethnically matching controls . However , unlike the recently published RECQL example , none of our recurrent ERCC2 mutations showed convincing co-segregation with BC/OC or significant overrepresentation in the BC/OC cohort . Interestingly , we detected that some deleterious founder mutations had an unexpectedly high frequency of > 1% in the corresponding populations , suggesting that either homozygous carriers are not clinically recognized or homozygosity for these mutations is embryonically lethal . In conclusion , we provide a useful resource on the mutational landscape of ERCC2 mutations in hereditary BC/OC patients and , as our key finding , we demonstrate the complexity of correct interpretation for the discovery of “bonafide” breast cancer susceptibility genes .
Since it became evident that only 15%-20% of the familial risk for BC/OC can be explained by mutations in the major breast cancer-susceptibility genes BRCA1 and BRCA2 [1] , the search for additional BC/OC susceptibility loci has been pursued . In times of limited sequencing power this pursuit was based on carefully selected candidate genes which typically came from ( i ) cancer-associated syndromes ( ii ) linkage screens in large BRCA1/2-negative families and ( iii ) case–control association studies using single-nucleotide polymorphisms [2 , 3] . Since sequencing power is no longer an issue , the candidate approach is on its decline and about to be replaced by next generation sequencing ( NGS ) of large gene panels which , taken together , cover a total of more than 100 genes , only 21 of which have been associated with breast cancer so far [4] . This offers amazing opportunities for detection of novel susceptibility loci but also bears the danger of substantial misuse [4] , because variants picked up by these panels are not clinically validated . Therefore , post-marketing data validation is absolutely essential [5] . Rare variants , however , need huge case-control datasets in order to reach the requested statistical significance of P<0 . 0001 [4] . Until such large datasets become available , variant validation needs to focus on mutations that are clearly deleterious on functional level but still frequent enough to be validated by a few thousand controls . Such recurrent yet harmful variants are best identified by screening various populations for founder mutations . In NBN , for example , a protein-truncating variant ( c . 657del5 ) has been identified in Eastern Europe , which is sufficiently common to allow its evaluation in a BC/OC case–control study [6] . Also the successful validation of deleterious Polish and Canadian founder mutations in RECQL [7] underlines the huge potential of multi-national BC/OC cohorts . In this study we sequenced 1345 BC/OC cases from 3 different Central- and East European countries with multi-gene panels and identified recurrent founder mutations in ERCC2 , which were functionally validated in cell-culture based assays . As essential component of transcription factor IIH , the ERCC2 protein is involved in basal cellular transcription [8] and nucleotide excision repair ( NER ) of DNA lesions [9] . The most known inherited disease associated with bi-allelic mutations in ERCC2 is Xeroderma pigmentosum type D ( XPD , OMIM 278730 ) , a hereditary cancer-prone syndrome characterized by extreme skin photosensitivity and early development of multiple skin tumors [10] . Therefore , ERCC2 is a plausible candidate gene for cancer susceptibility . On the other hand , bi-allelic mutations in ERCC2 can also lead to syndromes without increased propensity to tumor development , namely Trichothiodystrophy 1 ( TTD; OMIM 601675 ) and cerebrooculofacioskeletal syndrome ( COFS2; OMIM 610756 ) . This indicates that not all functionally relevant ERCC2 mutations increase cancer susceptibility in their carriers .
Within the entire set of 1345 BC/OC index cases , we have detected three different frame-shift ( fs ) mutations [p . ( Val77fs ) , p . ( Phe568fs ) and p . ( Ser746fs ) ] , one splice-acceptor site mutation ( c . 1903-2A>G ) , one nucleotide exchange that activates a cryptic splice site ( c . 2150C>G ) and 20 rare missense mutations ( Table 1 , Fig 1 ) . Whereas 14 mutations were unique ( 2 fs , 1 splice-site , 11 missense ) , 11 mutations ( 1 fs , 1 splice-affecting , 9 missense ) have been found in 43 independent families . The most frequent mutation was p . ( Asp423Asn ) identified in 8 carriers from Lithuania and one from the Czech Republic . The common polymorphisms p . ( Lys751Gln ) and p . ( Asp312Asn ) have each been encountered in approximately 64% of our cases; since these variants have been considered to be functionally irrelevant [11] , we did not include them in our functional study . Among the 20 rare missense variants reported in Table 1 , thirteen are predicted by various computer algorithms to be pathogenic ( Table 1 and S4 Table ) . Further computational analysis of the conservation ( PhyloP ) and depletion ( CADD ) scores [12] for the mutated nucleotides strongly supported pathogenicity for these variants ( S2 Fig ) . Mapping the mutated AA positions onto the ERCC2 protein structure revealed a widespread distribution pattern ( Fig 1 ) . Residues 13 , 450 , 461 , 513 , 536 , 576 , 592 , 601 , 611 , 631 , 678 cluster at the helicase motifs of the HD1 and HD2 catalytic domains and residues 166 , 167 , 188 , 215 , 280 , 316 , 423 , 487 , 722 locate at the TFIIH transcription factor complex binding domains ( Arch , FES , and C-terminal ) . XPD-causing mutations located at the HD2 domain have been shown to inactivate helicase repair capability without disrupting protein structure . Mutations causing trichothiodystrophy ( TTD , OMIM 601675 ) , on the other hand , are located well away from the catalytic site of the enzyme and destabilize ERCC2 structure and TFIIH protein interactions [13–15] . We suggest that BC/OC relevant mutations might affect both—catalytic activity as well as protein stability . So far , 11 variants ( 9 recurrent founder mutations and 2 unique variants; Fig 2C ) were tested in functional assays for nucleotide excision repair ( NER ) capability ( Fig 2A ) as well as transcription ( Fig 2B ) . Whereas six out of the 11 BC/OC-associated ERCC2 variants tested in this study , have not yet been linked to any disease [AA positions 423 , 450 , 513 , 536 , 631 , 746] , five AA positions have already been found to be mutated in either TTD [AA 461 [16] , 487 [17] , 568 [18 , 19] , 592 [20]] or XPD [AA 601 [21]] ( Figs 1B and 2C ) . According to our functional assays , four ERCC2 protein variants [p . ( Asp423Asn ) , p . ( Arg487Trp ) , p . ( Phe568Tyrfs ) and p . ( Arg631Cys ) ] failed to enhance functional NER of an UV-treated reporter gene plasmid indicating the impairment of ERCC2 repair capacity . The remaining seven tested variants retained some NER capability ( Fig 2A ) . Concerning transcription , we detected a dominant negative influence of seven ERCC2 protein variants [p . ( Asp423Asn ) , p . ( Leu461Val ) , p . ( Arg487Trp ) , p . ( Asp513Tyr ) , p . ( Val536Met ) , p . ( Arg601Gln ) , p . ( Ser746fs ) ] on reporter gene expression ( Fig 2B ) indicating transcription blocking . In summary , 10 of 11 mutations display diminished excision repair efficiency and/or decreased transcriptional activation capability , with p . ( Asp423Asn ) and p . ( Arg487Trp ) being the variants with the highest impact on protein function . The hallmarks of a founder mutation are recurrent appearance , population specificity and haplotype sharing . As to recurrent appearance , 11 out of 25 ERCC2 mutations were seen at least twice in our BC/OC cohort ( last column in Table 1 ) . Among the 11 recurrent variants , 5 were identified exclusively in one of the three populations tested in this study ( e . g . p . ( Arg487Trp ) : 4x LT only ) and another 5 were significantly overrepresented in one of the 3 populations ( e . g . p . ( Asp423Asn ) : 8x LT , 1x CZ , 0x GE ) . For two of the population-enriched recurrent founder mutations , we could also demonstrate haplotype sharing: ( i ) the mutation c . 1381C>G ( rs121913016 ) always co-occurred and co-segregated with mutation c . 2150C>G ( rs144564120 ) , a haplotype which has been observed repeatedly in TTD/XPD patients [9 , 16 , 22] . ( ii ) In almost all cases ( 10/11 ) the frame-shift mutation c . 1703_1704delTT co-occurred with the c . 1758+32C>G polymorphism ( rs238417 ) . Furthermore , these two variants are only 84 nt apart from each other and all NGS-reads covering both variants showed these variants simultaneously , i . e . these variants are definitely localized in cis on the same DNA molecule . In the variant discovery phase of this project , the frequencies of ERCC2 mutations found in the BC/OC cohort were compared to the corresponding frequencies in public databases provided by the NHLBI Exome Sequencing Project ( ESP ) and the Exome Aggregation Consortium ( ExAC ) . As shown in Table 2 , some intriguing mutations , like p . ( Phe568fs ) and p . ( Asp423Asn ) , have very low frequencies according to ExAC , suggesting significant odds ratios ( OR ) . As a first proof of principle measure , we performed segregation analysis . However , none of our recurrent ERCC2 mutations showed convincing co-segregation with BC/OC ( Fig 3 ) . Moreover , as soon as a small number of population-specific control probands has been sequenced , it became clear that almost all founder mutations in the BC/OC cohort showed similar frequencies in the ethnically matching control cohorts . The only exception so far is the Lithuanian mutation p . ( Arg487Trp ) , which was found 4 times in the Lithuanian BC/OC cohort and not ( yet ) in the corresponding control cohort ( Table 2 ) . With just above 100 individuals this cohort is way too small to be of any statistical relevance . Therefore , the acquisition of additional samples is mandatory . But even in this very early phase of variant ( de- ) validation it becomes evident that regionally matching control cohorts–as small as they may be–are superior to any huge global cohort . Since genotypic data allow to locate the geographic origin of a given individual within a few hundred kilometers [23] , the term “regionally matching” should be defined as “less than ca . 300 km distance from the recruitment center” . As a consequence , regionally matching controls are even superior to population-specific controls , because populations do mix , especially in regions close to national borders . The p . Phe568fs mutation , for example , has been seen only once in a German BC/OC index case and never in the 1844 German controls . Based on population-specific data we would have been very excited about this finding . But the German case was recruited in Dresden , close to the Czech border , and in Prague , 118 km away , the same mutation has been found twice in a small control cohort of only 105 non-cancer females . This underlines the importance of regional controls and multi-national studies for reliable variant validation . Due to its involvement in DNA repair and due to encoding a helicase like RECQL [7] , ERCC2 is a plausible gene candidate for familial cancer susceptibility . Bi-allelic mutations in ERCC2 , however , can cause the cancer-prone disease XPD as well as the “non-cancer”-disease TTD [27] and there is no evident genotype-phenotype correlation [19] . The pathogenic p . ( Arg112His ) mutation , for example , has been identified in TTD patients as well as in a patient with major features of XPD [19] . Furthermore , impairment of DNA repair capacity is not correlated with tumor burden: the mutation p . ( Phe568Tyrfs ) , for example , has been identified in non-cancer TTD patients twice , but not once in cancer-prone XPD patients , although this study ( Fig 2 ) as well as a previous study [19] clearly show diminished repair capability of this frameshift variant . From these observations we have to conclude that a limited subset of mutations in ERCC2 might predispose to cancer but these mutations are not likely to cluster in a defined area of the gene nor do they necessarily affect a specific sub-function of the ERCC2 protein . Therefore , cancer predisposing ERCC2 mutations are very likely to be discovered only on the basis of familial co-segregation with cancer and overrepresentation in cancer cohorts vs . region-specific controls . Although the founder mutations tested in this study may not predispose to BC/OC they still confer carrier status for the recessive disorders XPD ( OMIM 278730 ) , TTD ( OMIM 601675 ) and COFS2 ( OMIM 610756 ) . Even the TTD-causing mutation p . ( Phe568fs ) alone has been detected in 7 of 806 samples from the Czech Republic ( CZ ) , i . e . the frequency of heterozygous carriers of this mutation is approx . 0 . 86% . According to Hardy-Weinberg equilibrium model , this would result in a TTD incidence of 1/30 . 000 . Based on combined data from the DNA repair diagnostic centers in France , West-Germany , Italy , the Netherlands and the United Kingdom the actual incidence for TTD is 1 . 2 per million [28] . Since it is reasonable to assume that ( i ) a TTD incidence of 1/30 . 000 would not be missed by the clinical geneticists in CZ and ( ii ) the publications reporting p . ( Phe568fs ) as TTD-causing [9 , 19] are not wrong , there is one logical explanation for the discrepancy between allele frequency and disease incidence: homozygosity for p . Phe568fs is embryonic lethal . This is in-line with the observation that complete loss of ERCC2 activity is not compatible with life in homozygous knock-out mice [29] and it is also consistent with the observation that all XPD and TTD patients tested so far have residual ERCC2 activity [30] . Since an elevated TTD/XPD incidence has not been reported in Lithuania either , we can assume that homozygosity of the frequent Lithuanian founder mutation p . ( Asp423Asn ) ( Table 2 ) , which clearly displayed functional deficiency in our experiments ( Fig 2 ) , is embryonic lethal as well . In conclusion , this multi-national study of ERCC2 mutations in patients with familial BC/OC and regionally matching controls identified and functionally verified a broad spectrum of unique and recurrent ERCC2 mutations . Although the frequent founder mutations are not very likely to predispose to BC/OC , some mutations , like p . ( Val77Alafs ) , that are unique to the BC/OC cohort are worth to be considered in future large-scale association studies .
Informed written consent was obtained from all patients and the study was approved by the Local Research Ethics Committee ( EK 162072007 ) . We enrolled affected individuals from 587 German BC and BC/OC pedigrees with hereditary gynecological malignancies through a genetic counseling program at two centers ( Dresden , Munich ) from the “German Consortium for hereditary breast- and ovarian cancer” ( GC-HBOC ) and at the Medical Genetics Center ( MGZ ) in Munich . Additional 131 BC- and 136 BC/OC families were collected at the Vilnius University Hospital Santariskiu Klinikos in Vilnius , Lithuania and 28 BC/OC families were gathered in the Czech Republic at Brno . The Czech Prague subgroup involved 325 BC patients negatively tested for presence of pathogenic BRCA1 and BRCA2 variants [24] and 105 non-cancer controls analyzed as described recently [25 , 26] , and additional 240 controls [26] sequenced in pools . The BC pedigrees fulfilled the criterion that at least three affected females with breast cancer but no ovarian cancers were present ( breast cancer pedigrees ) . In the BC/OC pedigrees , at least one case of breast and one ovarian cancer had occurred . All individuals with variant ERCC2 alleles were checked for mutations in 10 BC/OC core genes defined by GC-HBOC ( ATM , BRCA1 , BRCA2 , CDH1 , CHEK2 , NBN , PALB2 , RAD51C , RAD51D and TP53 ) . Informed consent was obtained from all people participating in the study , and the experiments were approved by the ethics committees of the institutions contributing to this project . DNA was obtained from peripheral blood of all patients . For panel enrichment approximately 85 ng genomic DNA was required . We used the TruSight Cancer Illumina kit ( Illumina ) , which targets the coding sequences of 94 genes associated with a predisposition towards cancer ( S1 Table ) , following the manufacturer's instructions . Sequencing was carried out on an Illumina MiSeq instrument as 150 bp paired-end runs with V2 chemistry . Reads were aligned to the human reference genome ( GRCh37/hg19 ) using BWA ( v 0 . 7 . 8-r455 ) with standard parameters . Duplicate reads and reads that did not map unambiguously were removed . The percentage of reads overlapping targeted regions and coverage statistics of targeted regions were calculated using Shell scripts . Single-nucleotide variants and small insertions and deletions ( INDELs ) were called using SAMtools ( v1 . 1 ) . We used the following parameters: a maximum read depth of 10000 ( parameter -d ) , a maximum per sample depth of 10000 for INDEL calling ( parameter -L ) , adjustment of mapping quality ( parameter -C ) and recalculation of per-Base Alignment Quality ( parameter -E ) . Additionally , we required putative SNVs to fulfill the following criteria: a minimum of 20% of reads showing the variant base and the variant base is indicated by reads coming from different strands . For INDELs we required that at least 15% of reads covering this position indicate the INDEL . Variant annotation was performed with snpEff ( v 4 . 0e ) and Alamut-Batch ( v 1 . 3 . 1 ) based on the RefSeq database . Only variants ( SNVs/small INDELs ) in the coding region and the flanking intronic regions ( ±15 bp ) were evaluated . The data related to the ERCC2 gene in this study were retrieved from the custom-made gene panel sequencing analysis described recently [25] . Briefly , genomic DNA was obtained from a peripheral blood of 325 BC Czech patients from the Prague area that were negatively tested for a presence of pathogenic variants in the BRCA1 or BRCA2 gene previously [24] . The frequency of population-specific variants was assessed by a concurrent analysis of 105 control DNAs obtained from non-cancer individuals [26] . One μg of genomic DNA was used for library construction . The DNA was fragmented by ultrasonication and edited for SOLiD sequencing . Target DNA enrichment was performed by a custom solution-based sequence capture ( SeqCap EZ Choice Library , Roche ) according to the NimbleGenSeqCap EZ Library SR User's Guide ( Version 4 . 2 , Roche ) . Five hundred and ninety targeted genes include 141 genes that code for known proteins involved in DNA repair and DNA damage response pathways , and an additional set of genes retrieved from Phenopedia at HuGE Navigator16 web site associated with “breast neoplasms” ( assessed February 2012 ) . Captured libraries were sequenced on SOLiD4 system . Finally , exonic regions of 581 genes were captured successfully with sufficient coverage . Reads were aligned to the human reference genome ( GRCh37/hg19 ) using Novoalign ( CS 1 . 01 . 08 ) with standard parameters . Conversion of SAM to BAM format was performed with SAMtools ( 0 . 1 . 8 ) . Single-nucleotide variants and small insertions and deletions ( INDELs ) were called using SAMtools ( 0 . 1 . 8 ) . Variant annotation was performed with ANNOVAR [31] . For final evaluations , small INDELs , intronic variants flanking ± 2 bp to exon borders , and rare SNPs ( presented in 1000 genome or exome sequencing ( ESP ) projects with frequency <1% ) were considered . Validation of ERCC2 variants in probands and family members was performed by classical Sanger sequencing . Additional DNAs from 8 HBOC patients affected by malignant melanoma ( 5 cases ) or presence of melanoma in other family members ( 3 cases ) were analyzed for the complete ERCC2 coding region . ERCC2 exons were amplified with intronic primers ( S2 Table ) and sequenced using the ABI Prism Terminator Cycle Sequencing Ready Reaction Kit ( Applied Biosystems ) . Genomic DNA ( 50 ng ) containing 1x PCR Master Mix ( Qiagen ) and 0 . 25 μM of each forward and reverse primers in 15 μl reaction volume was subjected to PCR amplification for 25 cycles ( 30 sec at 95°C , 30 sec at 64°C and 30 sec at 72°C ) . A multiple alignment of ERCC2 AA sequences was done according to HomoloGene ( NCBI ) in order to assess the AA conservation of the detected variants in 20 species with homologous proteins ( S3 Fig ) .
|
Approximately 5–10% of breast/ovarian cancer ( BC/OC ) cases have inherited an increased risk of developing this malignancy . However , mutations in the two major breast cancer susceptibility genes BRCA1 and BRCA2 explain only 15–20% of all familial BC/OC cases . With the emergence of the high throughput NGS-technology , the number of proposed novel candidate genes for breast cancer predisposition continuously increases . However , a “bonafide” proof of cancer susceptibility requires a detailed characterization of candidate mutations , which we addressed in the current study . Using the DNA repair gene ERCC2 as an example , we performed a comprehensive multi-center approach , analyzing ERCC2 mutations in 1000+ patients with hereditary BC/OC . We identified 25 potential candidate mutations for cancer breast cancer susceptibility , some of them affecting ERCC2 functional activity in appropriate cell-culture based assays . However , a more dissected analysis showed no convincing co-segregation with BC/OC and there was no longer a significant overrepresentation in BC/OC when compared to regionally matched controls instead of the global ExAc variant data base , pointing to the relevance of founder-mutations . In conclusion , we provide a useful resource on the mutational landscape of ERCC2 mutations in hereditary BC/OC patients and , as our key finding , we highlight the complexity of correct interpretation for the discovery of “bonafide” breast cancer susceptibility genes .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"breast",
"tumors",
"luciferase",
"enzymes",
"cancers",
"and",
"neoplasms",
"enzymology",
"dna",
"transcription",
"oncology",
"mutation",
"dna",
"mutation",
"databases",
"frameshift",
"mutation",
"research",
"and",
"analysis",
"methods",
"ovarian",
"cancer",
"proteins",
"gene",
"expression",
"oxidoreductases",
"biological",
"databases",
"breast",
"cancer",
"gynecological",
"tumors",
"biochemistry",
"nucleic",
"acids",
"database",
"and",
"informatics",
"methods",
"genetics",
"biology",
"and",
"life",
"sciences",
"dna",
"repair"
] |
2016
|
Identification and Functional Testing of ERCC2 Mutations in a Multi-national Cohort of Patients with Familial Breast- and Ovarian Cancer
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.